- Research article
- Open access
- Published: 04 June 2021
Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews
- Israel Júnior Borges do Nascimento 1 , 2 ,
- Dónal P. O’Mathúna 3 , 4 ,
- Thilo Caspar von Groote 5 ,
- Hebatullah Mohamed Abdulazeem 6 ,
- Ishanka Weerasekara 7 , 8 ,
- Ana Marusic 9 ,
- Livia Puljak ORCID: orcid.org/0000-0002-8467-6061 10 ,
- Vinicius Tassoni Civile 11 ,
- Irena Zakarija-Grkovic 9 ,
- Tina Poklepovic Pericic 9 ,
- Alvaro Nagib Atallah 11 ,
- Santino Filoso 12 ,
- Nicola Luigi Bragazzi 13 &
- Milena Soriano Marcolino 1
On behalf of the International Network of Coronavirus Disease 2019 (InterNetCOVID-19)
BMC Infectious Diseases volume 21 , Article number: 525 ( 2021 ) Cite this article
Navigating the rapidly growing body of scientific literature on the SARS-CoV-2 pandemic is challenging, and ongoing critical appraisal of this output is essential. We aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.
Nine databases (Medline, EMBASE, Cochrane Library, CINAHL, Web of Sciences, PDQ-Evidence, WHO’s Global Research, LILACS, and Epistemonikos) were searched from December 1, 2019, to March 24, 2020. Systematic reviews analyzing primary studies of COVID-19 were included. Two authors independently undertook screening, selection, extraction (data on clinical symptoms, prevalence, pharmacological and non-pharmacological interventions, diagnostic test assessment, laboratory, and radiological findings), and quality assessment (AMSTAR 2). A meta-analysis was performed of the prevalence of clinical outcomes.
Eighteen systematic reviews were included; one was empty (did not identify any relevant study). Using AMSTAR 2, confidence in the results of all 18 reviews was rated as “critically low”. Identified symptoms of COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%) and gastrointestinal complaints (5–9%). Severe symptoms were more common in men. Elevated C-reactive protein and lactate dehydrogenase, and slightly elevated aspartate and alanine aminotransferase, were commonly described. Thrombocytopenia and elevated levels of procalcitonin and cardiac troponin I were associated with severe disease. A frequent finding on chest imaging was uni- or bilateral multilobar ground-glass opacity. A single review investigated the impact of medication (chloroquine) but found no verifiable clinical data. All-cause mortality ranged from 0.3 to 13.9%.
In this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic were of questionable usefulness. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards.
Peer Review reports
The spread of the “Severe Acute Respiratory Coronavirus 2” (SARS-CoV-2), the causal agent of COVID-19, was characterized as a pandemic by the World Health Organization (WHO) in March 2020 and has triggered an international public health emergency [ 1 ]. The numbers of confirmed cases and deaths due to COVID-19 are rapidly escalating, counting in millions [ 2 ], causing massive economic strain, and escalating healthcare and public health expenses [ 3 , 4 ].
The research community has responded by publishing an impressive number of scientific reports related to COVID-19. The world was alerted to the new disease at the beginning of 2020 [ 1 ], and by mid-March 2020, more than 2000 articles had been published on COVID-19 in scholarly journals, with 25% of them containing original data [ 5 ]. The living map of COVID-19 evidence, curated by the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre), contained more than 40,000 records by February 2021 [ 6 ]. More than 100,000 records on PubMed were labeled as “SARS-CoV-2 literature, sequence, and clinical content” by February 2021 [ 7 ].
Due to publication speed, the research community has voiced concerns regarding the quality and reproducibility of evidence produced during the COVID-19 pandemic, warning of the potential damaging approach of “publish first, retract later” [ 8 ]. It appears that these concerns are not unfounded, as it has been reported that COVID-19 articles were overrepresented in the pool of retracted articles in 2020 [ 9 ]. These concerns about inadequate evidence are of major importance because they can lead to poor clinical practice and inappropriate policies [ 10 ].
Systematic reviews are a cornerstone of today’s evidence-informed decision-making. By synthesizing all relevant evidence regarding a particular topic, systematic reviews reflect the current scientific knowledge. Systematic reviews are considered to be at the highest level in the hierarchy of evidence and should be used to make informed decisions. However, with high numbers of systematic reviews of different scope and methodological quality being published, overviews of multiple systematic reviews that assess their methodological quality are essential [ 11 , 12 , 13 ]. An overview of systematic reviews helps identify and organize the literature and highlights areas of priority in decision-making.
In this overview of systematic reviews, we aimed to summarize and critically appraise systematic reviews of coronavirus disease (COVID-19) in humans that were available at the beginning of the pandemic.
This overview’s primary objective was to summarize and critically appraise systematic reviews that assessed any type of primary clinical data from patients infected with SARS-CoV-2. Our research question was purposefully broad because we wanted to analyze as many systematic reviews as possible that were available early following the COVID-19 outbreak.
We conducted an overview of systematic reviews. The idea for this overview originated in a protocol for a systematic review submitted to PROSPERO (CRD42020170623), which indicated a plan to conduct an overview.
Overviews of systematic reviews use explicit and systematic methods for searching and identifying multiple systematic reviews addressing related research questions in the same field to extract and analyze evidence across important outcomes. Overviews of systematic reviews are in principle similar to systematic reviews of interventions, but the unit of analysis is a systematic review [ 14 , 15 , 16 ].
We used the overview methodology instead of other evidence synthesis methods to allow us to collate and appraise multiple systematic reviews on this topic, and to extract and analyze their results across relevant topics [ 17 ]. The overview and meta-analysis of systematic reviews allowed us to investigate the methodological quality of included studies, summarize results, and identify specific areas of available or limited evidence, thereby strengthening the current understanding of this novel disease and guiding future research [ 13 ].
A reporting guideline for overviews of reviews is currently under development, i.e., Preferred Reporting Items for Overviews of Reviews (PRIOR) [ 18 ]. As the PRIOR checklist is still not published, this study was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 statement [ 19 ]. The methodology used in this review was adapted from the Cochrane Handbook for Systematic Reviews of Interventions and also followed established methodological considerations for analyzing existing systematic reviews [ 14 ].
Approval of a research ethics committee was not necessary as the study analyzed only publicly available articles.
Systematic reviews were included if they analyzed primary data from patients infected with SARS-CoV-2 as confirmed by RT-PCR or another pre-specified diagnostic technique. Eligible reviews covered all topics related to COVID-19 including, but not limited to, those that reported clinical symptoms, diagnostic methods, therapeutic interventions, laboratory findings, or radiological results. Both full manuscripts and abbreviated versions, such as letters, were eligible.
No restrictions were imposed on the design of the primary studies included within the systematic reviews, the last search date, whether the review included meta-analyses or language. Reviews related to SARS-CoV-2 and other coronaviruses were eligible, but from those reviews, we analyzed only data related to SARS-CoV-2.
No consensus definition exists for a systematic review [ 20 ], and debates continue about the defining characteristics of a systematic review [ 21 ]. Cochrane’s guidance for overviews of reviews recommends setting pre-established criteria for making decisions around inclusion [ 14 ]. That is supported by a recent scoping review about guidance for overviews of systematic reviews [ 22 ].
Thus, for this study, we defined a systematic review as a research report which searched for primary research studies on a specific topic using an explicit search strategy, had a detailed description of the methods with explicit inclusion criteria provided, and provided a summary of the included studies either in narrative or quantitative format (such as a meta-analysis). Cochrane and non-Cochrane systematic reviews were considered eligible for inclusion, with or without meta-analysis, and regardless of the study design, language restriction and methodology of the included primary studies. To be eligible for inclusion, reviews had to be clearly analyzing data related to SARS-CoV-2 (associated or not with other viruses). We excluded narrative reviews without those characteristics as these are less likely to be replicable and are more prone to bias.
Scoping reviews and rapid reviews were eligible for inclusion in this overview if they met our pre-defined inclusion criteria noted above. We included reviews that addressed SARS-CoV-2 and other coronaviruses if they reported separate data regarding SARS-CoV-2.
Nine databases were searched for eligible records published between December 1, 2019, and March 24, 2020: Cochrane Database of Systematic Reviews via Cochrane Library, PubMed, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Sciences, LILACS (Latin American and Caribbean Health Sciences Literature), PDQ-Evidence, WHO’s Global Research on Coronavirus Disease (COVID-19), and Epistemonikos.
The comprehensive search strategy for each database is provided in Additional file 1 and was designed and conducted in collaboration with an information specialist. All retrieved records were primarily processed in EndNote, where duplicates were removed, and records were then imported into the Covidence platform [ 23 ]. In addition to database searches, we screened reference lists of reviews included after screening records retrieved via databases.
All searches, screening of titles and abstracts, and record selection, were performed independently by two investigators using the Covidence platform [ 23 ]. Articles deemed potentially eligible were retrieved for full-text screening carried out independently by two investigators. Discrepancies at all stages were resolved by consensus. During the screening, records published in languages other than English were translated by a native/fluent speaker.
Data collection process
We custom designed a data extraction table for this study, which was piloted by two authors independently. Data extraction was performed independently by two authors. Conflicts were resolved by consensus or by consulting a third researcher.
We extracted the following data: article identification data (authors’ name and journal of publication), search period, number of databases searched, population or settings considered, main results and outcomes observed, and number of participants. From Web of Science (Clarivate Analytics, Philadelphia, PA, USA), we extracted journal rank (quartile) and Journal Impact Factor (JIF).
We categorized the following as primary outcomes: all-cause mortality, need for and length of mechanical ventilation, length of hospitalization (in days), admission to intensive care unit (yes/no), and length of stay in the intensive care unit.
The following outcomes were categorized as exploratory: diagnostic methods used for detection of the virus, male to female ratio, clinical symptoms, pharmacological and non-pharmacological interventions, laboratory findings (full blood count, liver enzymes, C-reactive protein, d-dimer, albumin, lipid profile, serum electrolytes, blood vitamin levels, glucose levels, and any other important biomarkers), and radiological findings (using radiography, computed tomography, magnetic resonance imaging or ultrasound).
We also collected data on reporting guidelines and requirements for the publication of systematic reviews and meta-analyses from journal websites where included reviews were published.
Quality assessment in individual reviews
Two researchers independently assessed the reviews’ quality using the “A MeaSurement Tool to Assess Systematic Reviews 2 (AMSTAR 2)”. We acknowledge that the AMSTAR 2 was created as “a critical appraisal tool for systematic reviews that include randomized or non-randomized studies of healthcare interventions, or both” [ 24 ]. However, since AMSTAR 2 was designed for systematic reviews of intervention trials, and we included additional types of systematic reviews, we adjusted some AMSTAR 2 ratings and reported these in Additional file 2 .
Adherence to each item was rated as follows: yes, partial yes, no, or not applicable (such as when a meta-analysis was not conducted). The overall confidence in the results of the review is rated as “critically low”, “low”, “moderate” or “high”, according to the AMSTAR 2 guidance based on seven critical domains, which are items 2, 4, 7, 9, 11, 13, 15 as defined by AMSTAR 2 authors [ 24 ]. We reported our adherence ratings for transparency of our decision with accompanying explanations, for each item, in each included review.
One of the included systematic reviews was conducted by some members of this author team [ 25 ]. This review was initially assessed independently by two authors who were not co-authors of that review to prevent the risk of bias in assessing this study.
Synthesis of results
For data synthesis, we prepared a table summarizing each systematic review. Graphs illustrating the mortality rate and clinical symptoms were created. We then prepared a narrative summary of the methods, findings, study strengths, and limitations.
For analysis of the prevalence of clinical outcomes, we extracted data on the number of events and the total number of patients to perform proportional meta-analysis using RStudio© software, with the “meta” package (version 4.9–6), using the “metaprop” function for reviews that did not perform a meta-analysis, excluding case studies because of the absence of variance. For reviews that did not perform a meta-analysis, we presented pooled results of proportions with their respective confidence intervals (95%) by the inverse variance method with a random-effects model, using the DerSimonian-Laird estimator for τ 2 . We adjusted data using Freeman-Tukey double arcosen transformation. Confidence intervals were calculated using the Clopper-Pearson method for individual studies. We created forest plots using the RStudio© software, with the “metafor” package (version 2.1–0) and “forest” function.
Managing overlapping systematic reviews
Some of the included systematic reviews that address the same or similar research questions may include the same primary studies in overviews. Including such overlapping reviews may introduce bias when outcome data from the same primary study are included in the analyses of an overview multiple times. Thus, in summaries of evidence, multiple-counting of the same outcome data will give data from some primary studies too much influence [ 14 ]. In this overview, we did not exclude overlapping systematic reviews because, according to Cochrane’s guidance, it may be appropriate to include all relevant reviews’ results if the purpose of the overview is to present and describe the current body of evidence on a topic [ 14 ]. To avoid any bias in summary estimates associated with overlapping reviews, we generated forest plots showing data from individual systematic reviews, but the results were not pooled because some primary studies were included in multiple reviews.
Our search retrieved 1063 publications, of which 175 were duplicates. Most publications were excluded after the title and abstract analysis ( n = 860). Among the 28 studies selected for full-text screening, 10 were excluded for the reasons described in Additional file 3 , and 18 were included in the final analysis (Fig. 1 ) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Reference list screening did not retrieve any additional systematic reviews.
PRISMA flow diagram
Characteristics of included reviews
Summary features of 18 systematic reviews are presented in Table 1 . They were published in 14 different journals. Only four of these journals had specific requirements for systematic reviews (with or without meta-analysis): European Journal of Internal Medicine, Journal of Clinical Medicine, Ultrasound in Obstetrics and Gynecology, and Clinical Research in Cardiology . Two journals reported that they published only invited reviews ( Journal of Medical Virology and Clinica Chimica Acta ). Three systematic reviews in our study were published as letters; one was labeled as a scoping review and another as a rapid review (Table 2 ).
All reviews were published in English, in first quartile (Q1) journals, with JIF ranging from 1.692 to 6.062. One review was empty, meaning that its search did not identify any relevant studies; i.e., no primary studies were included [ 36 ]. The remaining 17 reviews included 269 unique studies; the majority ( N = 211; 78%) were included in only a single review included in our study (range: 1 to 12). Primary studies included in the reviews were published between December 2019 and March 18, 2020, and comprised case reports, case series, cohorts, and other observational studies. We found only one review that included randomized clinical trials [ 38 ]. In the included reviews, systematic literature searches were performed from 2019 (entire year) up to March 9, 2020. Ten systematic reviews included meta-analyses. The list of primary studies found in the included systematic reviews is shown in Additional file 4 , as well as the number of reviews in which each primary study was included.
Population and study designs
Most of the reviews analyzed data from patients with COVID-19 who developed pneumonia, acute respiratory distress syndrome (ARDS), or any other correlated complication. One review aimed to evaluate the effectiveness of using surgical masks on preventing transmission of the virus [ 36 ], one review was focused on pediatric patients [ 34 ], and one review investigated COVID-19 in pregnant women [ 37 ]. Most reviews assessed clinical symptoms, laboratory findings, or radiological results.
Systematic review findings
The summary of findings from individual reviews is shown in Table 2 . Overall, all-cause mortality ranged from 0.3 to 13.9% (Fig. 2 ).
A meta-analysis of the prevalence of mortality
Seven reviews described the main clinical manifestations of COVID-19 [ 26 , 28 , 29 , 34 , 35 , 39 , 41 ]. Three of them provided only a narrative discussion of symptoms [ 26 , 34 , 35 ]. In the reviews that performed a statistical analysis of the incidence of different clinical symptoms, symptoms in patients with COVID-19 were (range values of point estimates): fever (82–95%), cough with or without sputum (58–72%), dyspnea (26–59%), myalgia or muscle fatigue (29–51%), sore throat (10–13%), headache (8–12%), gastrointestinal disorders, such as diarrhea, nausea or vomiting (5.0–9.0%), and others (including, in one study only: dizziness 12.1%) (Figs. 3 , 4 , 5 , 6 , 7 , 8 and 9 ). Three reviews assessed cough with and without sputum together; only one review assessed sputum production itself (28.5%).
A meta-analysis of the prevalence of fever
A meta-analysis of the prevalence of cough
A meta-analysis of the prevalence of dyspnea
A meta-analysis of the prevalence of fatigue or myalgia
A meta-analysis of the prevalence of headache
A meta-analysis of the prevalence of gastrointestinal disorders
A meta-analysis of the prevalence of sore throat
Three reviews described methodologies, protocols, and tools used for establishing the diagnosis of COVID-19 [ 26 , 34 , 38 ]. The use of respiratory swabs (nasal or pharyngeal) or blood specimens to assess the presence of SARS-CoV-2 nucleic acid using RT-PCR assays was the most commonly used diagnostic method mentioned in the included studies. These diagnostic tests have been widely used, but their precise sensitivity and specificity remain unknown. One review included a Chinese study with clinical diagnosis with no confirmation of SARS-CoV-2 infection (patients were diagnosed with COVID-19 if they presented with at least two symptoms suggestive of COVID-19, together with laboratory and chest radiography abnormalities) [ 34 ].
Pharmacological and non-pharmacological interventions (supportive therapies) used in treating patients with COVID-19 were reported in five reviews [ 25 , 27 , 34 , 35 , 38 ]. Antivirals used empirically for COVID-19 treatment were reported in seven reviews [ 25 , 27 , 34 , 35 , 37 , 38 , 41 ]; most commonly used were protease inhibitors (lopinavir, ritonavir, darunavir), nucleoside reverse transcriptase inhibitor (tenofovir), nucleotide analogs (remdesivir, galidesivir, ganciclovir), and neuraminidase inhibitors (oseltamivir). Umifenovir, a membrane fusion inhibitor, was investigated in two studies [ 25 , 35 ]. Possible supportive interventions analyzed were different types of oxygen supplementation and breathing support (invasive or non-invasive ventilation) [ 25 ]. The use of antibiotics, both empirically and to treat secondary pneumonia, was reported in six studies [ 25 , 26 , 27 , 34 , 35 , 38 ]. One review specifically assessed evidence on the efficacy and safety of the anti-malaria drug chloroquine [ 27 ]. It identified 23 ongoing trials investigating the potential of chloroquine as a therapeutic option for COVID-19, but no verifiable clinical outcomes data. The use of mesenchymal stem cells, antifungals, and glucocorticoids were described in four reviews [ 25 , 34 , 35 , 38 ].
Laboratory and radiological findings
Of the 18 reviews included in this overview, eight analyzed laboratory parameters in patients with COVID-19 [ 25 , 29 , 30 , 32 , 33 , 34 , 35 , 39 ]; elevated C-reactive protein levels, associated with lymphocytopenia, elevated lactate dehydrogenase, as well as slightly elevated aspartate and alanine aminotransferase (AST, ALT) were commonly described in those eight reviews. Lippi et al. assessed cardiac troponin I (cTnI) [ 25 ], procalcitonin [ 32 ], and platelet count [ 33 ] in COVID-19 patients. Elevated levels of procalcitonin [ 32 ] and cTnI [ 30 ] were more likely to be associated with a severe disease course (requiring intensive care unit admission and intubation). Furthermore, thrombocytopenia was frequently observed in patients with complicated COVID-19 infections [ 33 ].
Chest imaging (chest radiography and/or computed tomography) features were assessed in six reviews, all of which described a frequent pattern of local or bilateral multilobar ground-glass opacity [ 25 , 34 , 35 , 39 , 40 , 41 ]. Those six reviews showed that septal thickening, bronchiectasis, pleural and cardiac effusions, halo signs, and pneumothorax were observed in patients suffering from COVID-19.
Quality of evidence in individual systematic reviews
Table 3 shows the detailed results of the quality assessment of 18 systematic reviews, including the assessment of individual items and summary assessment. A detailed explanation for each decision in each review is available in Additional file 5 .
Using AMSTAR 2 criteria, confidence in the results of all 18 reviews was rated as “critically low” (Table 3 ). Common methodological drawbacks were: omission of prospective protocol submission or publication; use of inappropriate search strategy: lack of independent and dual literature screening and data-extraction (or methodology unclear); absence of an explanation for heterogeneity among the studies included; lack of reasons for study exclusion (or rationale unclear).
Risk of bias assessment, based on a reported methodological tool, and quality of evidence appraisal, in line with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method, were reported only in one review [ 25 ]. Five reviews presented a table summarizing bias, using various risk of bias tools [ 25 , 29 , 39 , 40 , 41 ]. One review analyzed “study quality” [ 37 ]. One review mentioned the risk of bias assessment in the methodology but did not provide any related analysis [ 28 ].
This overview of systematic reviews analyzed the first 18 systematic reviews published after the onset of the COVID-19 pandemic, up to March 24, 2020, with primary studies involving more than 60,000 patients. Using AMSTAR-2, we judged that our confidence in all those reviews was “critically low”. Ten reviews included meta-analyses. The reviews presented data on clinical manifestations, laboratory and radiological findings, and interventions. We found no systematic reviews on the utility of diagnostic tests.
Symptoms were reported in seven reviews; most of the patients had a fever, cough, dyspnea, myalgia or muscle fatigue, and gastrointestinal disorders such as diarrhea, nausea, or vomiting. Olfactory dysfunction (anosmia or dysosmia) has been described in patients infected with COVID-19 [ 43 ]; however, this was not reported in any of the reviews included in this overview. During the SARS outbreak in 2002, there were reports of impairment of the sense of smell associated with the disease [ 44 , 45 ].
The reported mortality rates ranged from 0.3 to 14% in the included reviews. Mortality estimates are influenced by the transmissibility rate (basic reproduction number), availability of diagnostic tools, notification policies, asymptomatic presentations of the disease, resources for disease prevention and control, and treatment facilities; variability in the mortality rate fits the pattern of emerging infectious diseases [ 46 ]. Furthermore, the reported cases did not consider asymptomatic cases, mild cases where individuals have not sought medical treatment, and the fact that many countries had limited access to diagnostic tests or have implemented testing policies later than the others. Considering the lack of reviews assessing diagnostic testing (sensitivity, specificity, and predictive values of RT-PCT or immunoglobulin tests), and the preponderance of studies that assessed only symptomatic individuals, considerable imprecision around the calculated mortality rates existed in the early stage of the COVID-19 pandemic.
Few reviews included treatment data. Those reviews described studies considered to be at a very low level of evidence: usually small, retrospective studies with very heterogeneous populations. Seven reviews analyzed laboratory parameters; those reviews could have been useful for clinicians who attend patients suspected of COVID-19 in emergency services worldwide, such as assessing which patients need to be reassessed more frequently.
All systematic reviews scored poorly on the AMSTAR 2 critical appraisal tool for systematic reviews. Most of the original studies included in the reviews were case series and case reports, impacting the quality of evidence. Such evidence has major implications for clinical practice and the use of these reviews in evidence-based practice and policy. Clinicians, patients, and policymakers can only have the highest confidence in systematic review findings if high-quality systematic review methodologies are employed. The urgent need for information during a pandemic does not justify poor quality reporting.
We acknowledge that there are numerous challenges associated with analyzing COVID-19 data during a pandemic [ 47 ]. High-quality evidence syntheses are needed for decision-making, but each type of evidence syntheses is associated with its inherent challenges.
The creation of classic systematic reviews requires considerable time and effort; with massive research output, they quickly become outdated, and preparing updated versions also requires considerable time. A recent study showed that updates of non-Cochrane systematic reviews are published a median of 5 years after the publication of the previous version [ 48 ].
Authors may register a review and then abandon it [ 49 ], but the existence of a public record that is not updated may lead other authors to believe that the review is still ongoing. A quarter of Cochrane review protocols remains unpublished as completed systematic reviews 8 years after protocol publication [ 50 ].
Rapid reviews can be used to summarize the evidence, but they involve methodological sacrifices and simplifications to produce information promptly, with inconsistent methodological approaches [ 51 ]. However, rapid reviews are justified in times of public health emergencies, and even Cochrane has resorted to publishing rapid reviews in response to the COVID-19 crisis [ 52 ]. Rapid reviews were eligible for inclusion in this overview, but only one of the 18 reviews included in this study was labeled as a rapid review.
Ideally, COVID-19 evidence would be continually summarized in a series of high-quality living systematic reviews, types of evidence synthesis defined as “ a systematic review which is continually updated, incorporating relevant new evidence as it becomes available ” [ 53 ]. However, conducting living systematic reviews requires considerable resources, calling into question the sustainability of such evidence synthesis over long periods [ 54 ].
Research reports about COVID-19 will contribute to research waste if they are poorly designed, poorly reported, or simply not necessary. In principle, systematic reviews should help reduce research waste as they usually provide recommendations for further research that is needed or may advise that sufficient evidence exists on a particular topic [ 55 ]. However, systematic reviews can also contribute to growing research waste when they are not needed, or poorly conducted and reported. Our present study clearly shows that most of the systematic reviews that were published early on in the COVID-19 pandemic could be categorized as research waste, as our confidence in their results is critically low.
Our study has some limitations. One is that for AMSTAR 2 assessment we relied on information available in publications; we did not attempt to contact study authors for clarifications or additional data. In three reviews, the methodological quality appraisal was challenging because they were published as letters, or labeled as rapid communications. As a result, various details about their review process were not included, leading to AMSTAR 2 questions being answered as “not reported”, resulting in low confidence scores. Full manuscripts might have provided additional information that could have led to higher confidence in the results. In other words, low scores could reflect incomplete reporting, not necessarily low-quality review methods. To make their review available more rapidly and more concisely, the authors may have omitted methodological details. A general issue during a crisis is that speed and completeness must be balanced. However, maintaining high standards requires proper resourcing and commitment to ensure that the users of systematic reviews can have high confidence in the results.
Furthermore, we used adjusted AMSTAR 2 scoring, as the tool was designed for critical appraisal of reviews of interventions. Some reviews may have received lower scores than actually warranted in spite of these adjustments.
Another limitation of our study may be the inclusion of multiple overlapping reviews, as some included reviews included the same primary studies. According to the Cochrane Handbook, including overlapping reviews may be appropriate when the review’s aim is “ to present and describe the current body of systematic review evidence on a topic ” [ 12 ], which was our aim. To avoid bias with summarizing evidence from overlapping reviews, we presented the forest plots without summary estimates. The forest plots serve to inform readers about the effect sizes for outcomes that were reported in each review.
Several authors from this study have contributed to one of the reviews identified [ 25 ]. To reduce the risk of any bias, two authors who did not co-author the review in question initially assessed its quality and limitations.
Finally, we note that the systematic reviews included in our overview may have had issues that our analysis did not identify because we did not analyze their primary studies to verify the accuracy of the data and information they presented. We give two examples to substantiate this possibility. Lovato et al. wrote a commentary on the review of Sun et al. [ 41 ], in which they criticized the authors’ conclusion that sore throat is rare in COVID-19 patients [ 56 ]. Lovato et al. highlighted that multiple studies included in Sun et al. did not accurately describe participants’ clinical presentations, warning that only three studies clearly reported data on sore throat [ 56 ].
In another example, Leung [ 57 ] warned about the review of Li, L.Q. et al. [ 29 ]: “ it is possible that this statistic was computed using overlapped samples, therefore some patients were double counted ”. Li et al. responded to Leung that it is uncertain whether the data overlapped, as they used data from published articles and did not have access to the original data; they also reported that they requested original data and that they plan to re-do their analyses once they receive them; they also urged readers to treat the data with caution [ 58 ]. This points to the evolving nature of evidence during a crisis.
Our study’s strength is that this overview adds to the current knowledge by providing a comprehensive summary of all the evidence synthesis about COVID-19 available early after the onset of the pandemic. This overview followed strict methodological criteria, including a comprehensive and sensitive search strategy and a standard tool for methodological appraisal of systematic reviews.
In conclusion, in this overview of systematic reviews, we analyzed evidence from the first 18 systematic reviews that were published after the emergence of COVID-19. However, confidence in the results of all the reviews was “critically low”. Thus, systematic reviews that were published early on in the pandemic could be categorized as research waste. Even during public health emergencies, studies and systematic reviews should adhere to established methodological standards to provide patients, clinicians, and decision-makers trustworthy evidence.
Availability of data and materials
All data collected and analyzed within this study are available from the corresponding author on reasonable request.
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We thank Catherine Henderson DPhil from Swanscoe Communications for pro bono medical writing and editing support. We acknowledge support from the Covidence Team, specifically Anneliese Arno. We thank the whole International Network of Coronavirus Disease 2019 (InterNetCOVID-19) for their commitment and involvement. Members of the InterNetCOVID-19 are listed in Additional file 6 . We thank Pavel Cerny and Roger Crosthwaite for guiding the team supervisor (IJBN) on human resources management.
This research received no external funding.
Authors and affiliations.
University Hospital and School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
Israel Júnior Borges do Nascimento & Milena Soriano Marcolino
Medical College of Wisconsin, Milwaukee, WI, USA
Israel Júnior Borges do Nascimento
Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, College of Nursing, The Ohio State University, Columbus, OH, USA
Dónal P. O’Mathúna
School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland
Department of Anesthesiology, Intensive Care and Pain Medicine, University of Münster, Münster, Germany
Thilo Caspar von Groote
Department of Sport and Health Science, Technische Universität München, Munich, Germany
Hebatullah Mohamed Abdulazeem
School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, Australia
Department of Physiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri Lanka
Cochrane Croatia, University of Split, School of Medicine, Split, Croatia
Ana Marusic, Irena Zakarija-Grkovic & Tina Poklepovic Pericic
Center for Evidence-Based Medicine and Health Care, Catholic University of Croatia, Ilica 242, 10000, Zagreb, Croatia
Cochrane Brazil, Evidence-Based Health Program, Universidade Federal de São Paulo, São Paulo, Brazil
Vinicius Tassoni Civile & Alvaro Nagib Atallah
Yorkville University, Fredericton, New Brunswick, Canada
Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada
Nicola Luigi Bragazzi
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IJBN conceived the research idea and worked as a project coordinator. DPOM, TCVG, HMA, IW, AM, LP, VTC, IZG, TPP, ANA, SF, NLB and MSM were involved in data curation, formal analysis, investigation, methodology, and initial draft writing. All authors revised the manuscript critically for the content. The author(s) read and approved the final manuscript.
Correspondence to Livia Puljak .
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Additional file 1: appendix 1..
Search strategies used in the study.
Additional file 2: Appendix 2.
Adjusted scoring of AMSTAR 2 used in this study for systematic reviews of studies that did not analyze interventions.
Additional file 3: Appendix 3.
List of excluded studies, with reasons.
Additional file 4: Appendix 4.
Table of overlapping studies, containing the list of primary studies included, their visual overlap in individual systematic reviews, and the number in how many reviews each primary study was included.
Additional file 5: Appendix 5.
A detailed explanation of AMSTAR scoring for each item in each review.
Additional file 6: Appendix 6.
List of members and affiliates of International Network of Coronavirus Disease 2019 (InterNetCOVID-19).
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Borges do Nascimento, I.J., O’Mathúna, D.P., von Groote, T.C. et al. Coronavirus disease (COVID-19) pandemic: an overview of systematic reviews. BMC Infect Dis 21 , 525 (2021). https://doi.org/10.1186/s12879-021-06214-4
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DOI : https://doi.org/10.1186/s12879-021-06214-4
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- Published: 16 June 2020
COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research
- Debra L. Weiner 1 , 2 ,
- Vivek Balasubramaniam 3 ,
- Shetal I. Shah 4 &
- Joyce R. Javier 5 , 6
on behalf of the Pediatric Policy Council
Pediatric Research volume 88 , pages 148–150 ( 2020 ) Cite this article
The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.
The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1
Impact of COVID-19 on ongoing research
The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.
In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.
This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.
Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2
The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )
Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.
Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.
Differences between adult and pediatric COVID-19, the need for pediatric research
As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.
We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.
Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.
Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.
The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.
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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA
Debra L. Weiner
Harvard Medical School, Boston, MA, USA
Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA
Shetal I. Shah
Division of General Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA
Joyce R. Javier
Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.
Pediatric Policy Council
Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.
Correspondence to Debra L. Weiner .
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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3
Received : 07 May 2020
Accepted : 21 May 2020
Published : 16 June 2020
Issue Date : August 2020
DOI : https://doi.org/10.1038/s41390-020-1006-3
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- 1 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India
- 2 Department of Health Sciences, School of Education and Health, Cape Breton University, Sydney, NS, Canada
The Coronavirus (CoV) is a large family of viruses known to cause illnesses ranging from the common cold to acute respiratory tract infection. The severity of the infection may be visible as pneumonia, acute respiratory syndrome, and even death. Until the outbreak of SARS, this group of viruses was greatly overlooked. However, since the SARS and MERS outbreaks, these viruses have been studied in greater detail, propelling the vaccine research. On December 31, 2019, mysterious cases of pneumonia were detected in the city of Wuhan in China's Hubei Province. On January 7, 2020, the causative agent was identified as a new coronavirus (2019-nCoV), and the disease was later named as COVID-19 by the WHO. The virus spread extensively in the Wuhan region of China and has gained entry to over 210 countries and territories. Though experts suspected that the virus is transmitted from animals to humans, there are mixed reports on the origin of the virus. There are no treatment options available for the virus as such, limited to the use of anti-HIV drugs and/or other antivirals such as Remdesivir and Galidesivir. For the containment of the virus, it is recommended to quarantine the infected and to follow good hygiene practices. The virus has had a significant socio-economic impact globally. Economically, China is likely to experience a greater setback than other countries from the pandemic due to added trade war pressure, which have been discussed in this paper.
Coronaviridae is a family of viruses with a positive-sense RNA that possess an outer viral coat. When looked at with the help of an electron microscope, there appears to be a unique corona around it. This family of viruses mainly cause respiratory diseases in humans, in the forms of common cold or pneumonia as well as respiratory infections. These viruses can infect animals as well ( 1 , 2 ). Up until the year 2003, coronavirus (CoV) had attracted limited interest from researchers. However, after the SARS (severe acute respiratory syndrome) outbreak caused by the SARS-CoV, the coronavirus was looked at with renewed interest ( 3 , 4 ). This also happened to be the first epidemic of the 21st century originating in the Guangdong province of China. Almost 10 years later, there was a MERS (Middle East respiratory syndrome) outbreak in 2012, which was caused by the MERS-CoV ( 5 , 6 ). Both SARS and MERS have a zoonotic origin and originated from bats. A unique feature of these viruses is the ability to mutate rapidly and adapt to a new host. The zoonotic origin of these viruses allows them to jump from host to host. Coronaviruses are known to use the angiotensin-converting enzyme-2 (ACE-2) receptor or the dipeptidyl peptidase IV (DPP-4) protein to gain entry into cells for replication ( 7 – 10 ).
In December 2019, almost seven years after the MERS 2012 outbreak, a novel Coronavirus (2019-nCoV) surfaced in Wuhan in the Hubei region of China. The outbreak rapidly grew and spread to neighboring countries. However, rapid communication of information and the increasing scale of events led to quick quarantine and screening of travelers, thus containing the spread of the infection. The major part of the infection was restricted to China, and a second cluster was found on a cruise ship called the Diamond Princess docked in Japan ( 11 , 12 ).
The new virus was identified to be a novel Coronavirus and was thus initially named 2019-nCoV; later, it was renamed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ( 13 ), and the disease it causes is now referred to as Coronavirus Disease-2019 (COVID-19) by the WHO. The virus was suspected to have begun its spread in the Huanan seafood wholesale market in the Wuhan region. It is possible that an animal that was carrying the virus was brought into or sold in the market, causing the spread of the virus in the crowded marketplace. One of the first claims made was in an article published in the Journal of Medical Virology ( 14 ), which identified snakes as the possible host. A second possibility was that pangolins could be the wild host of SARS-CoV-2 ( 15 ), though the most likely possibility is that the virus originated from bats ( 13 , 16 – 19 ). Increasing evidence and experts are now collectively concluding the virus had a natural origin in bats, as with previous such respiratory viruses ( 2 , 20 – 24 ).
Similarly, SARS and MERS were also suspected to originate from bats. In the case of MERS, the dromedary camel is an intermediate host ( 5 , 10 ). Bats have been known to harbor coronaviruses for quite some time now. Just as in the case of avian flu, SARS, MERS, and possibly even HIV, with increasing selection and ecological pressure due to human activities, the virus made the jump from animal to man. Humans have been encroaching increasingly into forests, and this is true over much of China, as in Africa. Combined with additional ecological pressure due to climate change, such zoonotic spillovers are now more common than ever. It is likely that the next disease X will also have such an origin ( 25 ). We have learned the importance of identification of the source organism due to the Ebola virus pandemic. Viruses are unstable organisms genetically, constantly mutating by genetic shift or drift. It is not possible to predict when a cross-species jump may occur and when a seemingly harmless variant form of the virus may turn into a deadly strain. Such an incident occurred in Reston, USA, with the Reston virus ( 26 ), an alarming reminder of this possibility. The identification of the original host helps us to contain future spreads as well as to learn about the mechanism of transmission of viruses. Until the virus is isolated from a wild animal host, in this case, mostly bats, the zoonotic origin will remain hypothetical, though likely. It should further be noted that the virus has acquired several mutations, as noted by a group in China, indicating that there are more than two strains of the virus, which may have had an impact on its pathogenicity. However, this claim remains unproven, and many experts have argued otherwise; data proving this are not yet available ( 27 ). A similar finding was reported from Italy and India independently, where they found two strains ( 28 , 29 ). These findings need to be further cross-verified by similar analyses globally. If true, this finding could effectively explain why some nations are more affected than others.
When the spread of COVID-19 began ( Figure 1 ), the virus appeared to be contained within China and the cruise ship “Diamond Princess,” which formed the major clusters of the virus. However, as of April 2020, over 210 countries and territories are affected by the virus, with Europe, the USA, and Iran forming the new cluster of the virus. The USA ( Figure 2 ) has the highest number of confirmed COVID-19 cases, whereas India and China, despite being among the most population-dense countries in the world, have managed to constrain the infection rate by the implementation of a complete lockdown with arrangements in place to manage the confirmed cases. Similarly, the UK has also managed to maintain a low curve of the graph by implementing similar measures, though it was not strictly enforced. Reports have indicated that the presence of different strains or strands of the virus may have had an effect on the management of the infection rate of the virus ( 27 – 29 ). The disease is spread by droplet transmission. As of April 2020, the total number of infected individuals stands at around 3 million, with ~200,000 deaths and more than 1 million recoveries globally ( 30 , 34 ). The virus thus has a fatality rate of around 2% and an R 0 of 3 based on current data. However, a more recent report from the CDC, Atlanta, USA, claims that the R 0 could be as high as 5.7 ( 35 ). It has also been observed from data available from China and India that individuals likely to be infected by the virus from both these countries belong to the age groups of 20–50 years ( 36 , 37 ). In both of these countries, the working class mostly belongs to this age group, making exposure more likely. Germany and Singapore are great examples of countries with a high number of cases but low fatalities as compared to their immediate neighbors. Singapore is one of the few countries that had developed a detailed plan of action after the previous SARS outbreak to deal with a similar situation in the future, and this worked in their favor during this outbreak. Both countries took swift action after the outbreak began, with Singapore banning Chinese travelers and implementing screening and quarantine measures at a time when the WHO recommended none. They ordered the elderly and the vulnerable to strictly stay at home, and they ensured that lifesaving equipment and large-scale testing facilities were available immediately ( 38 , 39 ). Germany took similar measures by ramping up testing capacity quite early and by ensuring that all individuals had equal opportunity to get tested. This meant that young, old, and at-risk people all got tested, thus ensuring positive results early during disease progression and that most cases were mild like in Singapore, thus maintaining a lower death percentage ( 40 ). It allowed infected individuals to be identified and quarantined before they even had symptoms. Testing was carried out at multiple labs, reducing the load and providing massive scale, something which countries such as the USA did quite late and India restricted to select government and private labs. The German government also banned large gatherings and advocated social distancing to further reduce the spread, though unlike India and the USA, this was done quite late. South Korea is another example of how a nation has managed to contain the spread and transmission of the infection. South Korea and the USA both reported their first COVID-19 cases on the same day; however, the US administration downplayed the risks of the disease, unlike South Korean officials, who constantly informed their citizens about the developments of the disease using the media and a centralized messaging system. They also employed the Trace, Test, and Treat protocol to identify and isolate patients fast, whereas the USA restricted this to patients with severe infection and only later broadened this criterion, like many European countries as well as India. Unlike the USA, South Korea also has universal healthcare, ensuring free diagnostic testing.
Figure 1 . Timeline of COVID-19 progression ( 30 – 32 ).
Figure 2 . Total confirmed COVID 19 cases as of May 2020 ( 33 ).
The main mode of transmission of 2019-nCoV is human to human. As of now, animal-to-human transfer has not yet been confirmed. Asymptomatic carriers of the virus are at major risk of being superinfectors with this disease, as all those infected may not develop the disease ( 41 ). This is a concern that has been raised by nations globally, with the Indian government raising concerns on how to identify and contain asymptomatic carriers, who could account for 80% of those infected ( 42 ). Since current resources are directed towards understanding the hospitalized individuals showing symptoms, there is still a vast amount of information about asymptomatic individuals that has yet to be studied. For example, some questions that need to be answered include: Do asymptomatic individuals develop the disease at any point in time at all? Do they eventually develop antibodies? How long do they shed the virus for? Can any tissue of these individuals store the virus in a dormant state? Asymptomatic transmission is a gray area that encompasses major unknowns in COVID-19.
The main route of human-to-human transmission is by droplets, which are generated during coughing, talking, or sneezing and are then inhaled by a healthy individual. They can also be indirectly transmitted to a person when they land on surfaces that are touched by a healthy individual who may then touch their nose, mouth, or eyes, allowing the virus entry into the body. Fomites are also a common issue in such diseases ( 43 ).
Aerosol-based transmission of the virus has not yet been confirmed ( 43 ). Stool-based transmission via the fecal-oral route may also be possible since the SARS-CoV-2 has been found in patient feces ( 44 , 45 ). Some patients with COVID-19 tend to develop diarrhea, which can become a major route of transmission if proper sanitation and personal hygiene needs are not met. There is no evidence currently available to suggest intrauterine vertical transmission of the disease in pregnant women ( 46 ).
More investigation is necessary of whether climate has played any role in the containment of the infection in countries such as India, Singapore, China, and Israel, as these are significantly warmer countries as compared with the UK, the USA, and Canada ( Figure 2 ). Ideally, a warm climate should prevent the virus from surviving for longer periods of time on surfaces, reducing transmissibility.
On gaining entry via any of the mucus membranes, the single-stranded RNA-based virus enters the host cell using type 2 transmembrane serine protease (TMPRSS2) and ACE2 receptor protein, leading to fusion and endocytosis with the host cell ( 47 – 49 ). The uncoated RNA is then translated, and viral proteins are synthesized. With the help of RNA-dependant RNA polymerase, new RNA is produced for the new virions. The cell then undergoes lysis, releasing a load of new virions into the patients' body. The resultant infection causes a massive release of pro-inflammatory cytokines that causes a cytokine storm.
The clinical presentation of the disease resembles beta coronavirus infections. The virus has an incubation time of 2–14 days, which is the reason why most patients suspected to have the illness or contact with an individual having the illness remain in quarantine for the said amount of time. Infection with SARS-CoV-2 causes severe pneumonia, intermittent fever, and cough ( 50 , 51 ). Symptoms of rhinorrhoea, pharyngitis, and sneezing have been less commonly seen. Patients often develop acute respiratory distress syndrome within 2 days of hospital admission, requiring ventilatory support. It has been observed that during this phase, the mortality tends to be high. Chest CT will show indicators of pneumonia and ground-glass opacity, a feature that has helped to improve the preliminary diagnosis ( 51 ). The primary method of diagnosis for SARS-CoV-2 is with the help of PCR. For the PCR testing, the US CDC recommends testing for the N gene, whereas the Chinese CDC recommends the use of ORF lab and N gene of the viral genome for testing. Some also rely on the radiological findings for preliminary screening ( 52 ). Additionally, immunodiagnostic tests based on the presence of antibodies can also play a role in testing. While the WHO recommends the use of these tests for research use, many countries have pre-emptively deployed the use of these tests in the hope of ramping up the rate and speed of testing ( 52 – 54 ). Later, they noticed variations among the results, causing them to stop the use of such kits; there was also debate among the experts about the sensitivity and specificity of the tests. For immunological tests, it is beneficial to test for antibodies against the virus produced by the body rather than to test for the presence of the viral proteins, since the antibodies can be present in larger titers for a longer span of time. However, the cross-reactivity of these tests with other coronavirus antibodies is something that needs verification. Biochemical parameters such as D-dimer, C-reactive protein, and variations in neutrophil and lymphocyte counts are some other parameters that can be used to make a preliminary diagnosis; however, these parameters vary in a number of diseases and thus cannot be relied upon conclusively ( 51 ). Patients with pre-existing diseases such as asthma or similar lung disorder are at higher risk, requiring life support, as are those with other diseases such as diabetes, hypertension, or obesity. Those above the age of 60 have displayed the highest mortality rate in China, a finding that is mirrored in other nations as well ( Figure 3 ) ( 55 ). If we cross-verify these findings with the population share that is above the age of 70, we find that Italy, the United Kingdom, Canada, and the USA have one of the highest elderly populations as compared to countries such as India and China ( Figure 4 ), and this also reflects the case fatality rates accordingly ( Figure 5 ) ( 33 ). This is a clear indicator that aside from comorbidities, age is also an independent risk factor for death in those infected by COVID-19. Also, in the US, it was seen that the rates of African American deaths were higher. This is probably due to the fact that the prevalence of hypertension and obesity in this community is higher than in Caucasians ( 56 , 57 ). In late April 2020, there are also claims in the US media that young patients in the US with COVID-19 may be at increased risk of stroke; however, this is yet to be proven. We know that coagulopathy is a feature of COVID-19, and thus stroke is likely in this condition ( 58 , 59 ). The main cause of death in COVID-19 patients was acute respiratory distress due to the inflammation in the linings of the lungs caused by the cytokine storm, which is seen in all non-survival cases and in respiratory failure. The resultant inflammation in the lungs, served as an entry point of further infection, associated with coagulopathy end-organ failure, septic shock, and secondary infections leading to death ( 60 – 63 ).
Figure 3 . Case fatality rate by age in selected countries as of April 2020 ( 33 ).
Figure 4 . Case fatality rate in selected countries ( 33 ).
Figure 5 . Population share above 70 years of age ( 33 ).
For COVID-19, there is no specific treatment available. The WHO announced the organization of a trial dubbed the “Solidarity” clinical trial for COVID-19 treatments ( 64 ). This is an international collaborative study that investigates the use of a few prime candidate drugs for use against COVID-19, which are discussed below. The study is designed to reduce the time taken for an RCT by over 80%. There are over 1087 studies ( Supplementary Data 1 ) for COVID-19 registered at clinicaltrials.gov , of which 657 are interventional studies ( Supplementary Data 2 ) ( 65 ). The primary focus of the interventional studies for COVID-19 has been on antimalarial drugs and antiviral agents ( Table 1 ), while over 200 studies deal with the use of different forms of oxygen therapy. Most trials focus on improvement of clinical status, reduction of viral load, time to improvement, and reduction of mortality rates. These studies cover both severe and mild cases.
Table 1 . List of therapeutic drugs under study for COVID-19 as per clinical trials registered under clinicaltrials.gov .
Use of Antimalarial Drugs Against SARS-CoV-2
The use of chloroquine for the treatment of corona virus-based infection has shown some benefit in the prevention of viral replication in the cases of SARS and MERS. However, it was not validated on a large scale in the form of a randomized control trial ( 50 , 66 – 68 ). The drugs of choice among antimalarials are Chloroquine (CQ) and Hydroxychloroquine (HCQ). The use of CQ for COVID-19 was brought to light by the Chinese, especially by the publication of a letter to the editor of Bioscience Trends by Gao et al. ( 69 ). The letter claimed that several studies found CQ to be effective against COVID-19; however, the letter did not provide many details. Immediately, over a short span of time, interest in these two agents grew globally. Early in vitro data have revealed that chloroquine can inhibit the viral replication ( 70 , 71 ).
HCQ and CQ work by raising the pH of the lysosome, the cellular organelle that is responsible for phagocytic degradation. Its function is to combine with cell contents that have been phagocytosed and break them down eventually, in some immune cells, as a downstream process to display some of the broken proteins as antigens, thus further enhancing the immune recruitment against an antigen/pathogen. The drug was to be administered alone or with azithromycin. The use of azithromycin may be advocated by the fact that it has been seen previously to have some immunomodulatory role in airway-related disease. It appears to reduce the release of pro-inflammatory cytokines in respiratory illnesses ( 72 ). However, HCQ and azithromycin are known to have a major drug interaction when co-administered, which increases the risk of QT interval prolongation ( 73 ). Quinine-based drugs are known to have adverse effects such as QT prolongation, retinal damage, hypoglycemia, and hemolysis of blood in patients with G-6-PD deficiency ( 66 ). Several preprints, including, a metanalysis now indicate that HCQ may have no benefit for severe or critically ill patients who have COVID-19 where the outcome is need for ventilation or death ( 74 , 75 ). As of April 21, 2020, after having pre-emptively recommended their use for SARS-CoV-2 infection, the US now advocates against the use of these two drugs based on the new data that has become available.
Use of Antiviral Drugs Against SARS-CoV-2
The antiviral agents are mainly those used in the case of HIV/AIDS, these being Lopinavir and Ritonavir. Other agents such as nucleoside analogs like Favipiravir, Ribavirin, Remdesivir, and Galidesivir have been tested for possible activity in the prevention of viral RNA synthesis ( 76 ). Among these drugs, Lopinavir, Ritonavir, and Remdesivir are listed in the Solidarity trial by the WHO.
Remdesivir is a nucleotide analog for adenosine that gets incorporated into the viral RNA, hindering its replication and causing chain termination. This agent was originally developed for Ebola Virus Disease ( 77 ). A study was conducted with rhesus macaques infected with SARS-CoV-2 ( 78 ). In that study, after 12 h of infection, the monkeys were treated with either Remdesivir or vehicle. The drug showed good distribution in the lungs, and the animals treated with the drug showed a better clinical score than the vehicle group. The radiological findings of the study also indicated that the animals treated with Remdesivir have less lung damage. There was a reduction in viral replication but not in virus shedding. Furthermore, there were no mutations found in the RNA polymerase sequences. A randomized clinical control study that became available in late April 2020 ( 79 ), having 158 on the Remdesivir arm and 79 on the placebo arm, found that Remdesivir reduced the time to recovery in the Remdesivir-treated arm to 11 days, while the placebo-arm recovery time was 15 days. Though this was not found to be statistically significant, the agent provided a basis for further studies. The 28-days mortality was found to be similar for both groups. This has now provided us with a basis on which to develop future molecules. The study has been supported by the National Institute of Health, USA. The authors of the study advocated for more clinical trials with Remdesivir with a larger population. Such larger studies are already in progress, and their results are awaited. Remdesivir is currently one of the drugs that hold most promise against COVID-19.
An early trial in China with Lopinavir and Ritonavir showed no benefit compared with standard clinical care ( 80 ). More studies with this drug are currently underway, including one in India ( 81 , 82 ).
Use of Convalescent Patient Plasma
Another possible option would be the use of serum from convalescent individuals, as this is known to contain antibodies that can neutralize the virus and aid in its elimination. This has been tried previously for other coronavirus infections ( 83 ). Early emerging case reports in this aspect look promising compared to other therapies that have been tried ( 84 – 87 ). A report from China indicates that five patients treated with plasma recovered and were eventually weaned off ventilators ( 84 ). They exhibited reductions in fever and viral load and improved oxygenation. The virus was not detected in the patients after 12 days of plasma transfusion. The US FDA has provided detailed recommendations for investigational COVID-19 Convalescent Plasma use ( 88 ). One of the benefits of this approach is that it can also be used for post-exposure prophylaxis. This approach is now beginning to be increasingly adopted in other countries, with over 95 trials registered on clinicaltrials.gov alone, of which at least 75 are interventional ( 89 ). The use of convalescent patient plasma, though mostly for research purposes, appears to be the best and, so far, the only successful option for treatment available.
From a future perspective, the use of monoclonal antibodies for the inhibition of the attachment of the virus to the ACE-2 receptor may be the best bet. Aside from this, ACE-2-like molecules could also be utilized to attach and inactivate the viral proteins, since inhibition of the ACE-2 receptor would not be advisable due to its negative repercussions physiologically. In the absence of drug regimens and a vaccine, the treatment is symptomatic and involves the use of non-invasive ventilation or intubation where necessary for respiratory failure patients. Patients that may go into septic shock should be managed as per existing guidelines with hemodynamic support as well as antibiotics where necessary.
The WHO has recommended that simple personal hygiene practices can be sufficient for the prevention of spread and containment of the disease ( 90 ). Practices such as frequent washing of soiled hands or the use of sanitizer for unsoiled hands help reduce transmission. Covering of mouth while sneezing and coughing, and disinfection of surfaces that are frequently touched, such as tabletops, doorknobs, and switches with 70% isopropyl alcohol or other disinfectants are broadly recommended. It is recommended that all individuals afflicted by the disease, as well as those caring for the infected, wear a mask to avoid transmission. Healthcare works are advised to wear a complete set of personal protective equipment as per WHO-provided guidelines. Fumigation of dormitories, quarantine rooms, and washing of clothes and other fomites with detergent and warm water can help get rid of the virus. Parcels and goods are not known to transmit the virus, as per information provided by the WHO, since the virus is not able to survive sufficiently in an open, exposed environment. Quarantine of infected individuals and those who have come into contact with an infected individual is necessary to further prevent transmission of the virus ( 91 ). Quarantine is an age-old archaic practice that continues to hold relevance even today for disease containment. With the quarantine being implemented on such a large scale in some countries, taking the form of a national lockdown, the question arises of its impact on the mental health of all individuals. This topic needs to be addressed, especially in countries such as India and China, where it is still a matter of partial taboo to talk about it openly within the society.
In India, the Ministry of Ayurveda, Yoga, and Naturopathy, Unani, Siddha and Homeopathy (AYUSH), which deals with the alternative forms of medicine, issued a press release that the homeopathic, drug Arsenicum album 30, can be taken on an empty stomach for 3 days to provide protection against the infection ( 92 ). It also provided a list of herbal drugs in the same press release as per Ayurvedic and Unani systems of medicine that can boost the immune system to deal with the virus. However, there is currently no evidence to support the use of these systems of medicine against COVID-19, and they need to be tested.
The prevention of the disease with the use of a vaccine would provide a more viable solution. There are no vaccines available for any of the coronaviruses, which includes SARS and MERS. The development of a vaccine, however, is in progress at a rapid pace, though it could take about a year or two. As of April 2020, no vaccine has completed the development and testing process. A popular approach has been with the use of mRNA-based vaccine ( 93 – 96 ). mRNA vaccines have the advantage over conventional vaccines in terms of production, since they can be manufactured easily and do not have to be cultured, as a virus would need to be. Alternative conventional approaches to making a vaccine against SARS-CoV-2 would include the use of live attenuated virus as well as using the isolated spike proteins of the virus. Both of these approaches are in progress for vaccine development ( 97 ). Governments across the world have poured in resources and made changes in their legislation to ensure rapid development, testing, and deployment of a vaccine.
Barriers to Treatment
Lack of transparency and poor media relations.
The lack of government transparency and poor reporting by the media have hampered the measures that could have been taken by healthcare systems globally to deal with the COVID-19 threat. The CDC, as well as the US administration, downplayed the threat and thus failed to stock up on essential supplies, ventilators, and test kits. An early warning system, if implemented, would have caused borders to be shut and early lockdowns. The WHO also delayed its response in sounding the alarm regarding the severity of the outbreak to allow nations globally to prepare for a pandemic. Singapore is a prime example where, despite the WHO not raising concerns and banning travel to and from China, a country banned travelers and took early measures, thus managing the outbreak quite well. South Korea is another example of how things may have played out had those measures by agencies been taken with transparency. Increased transparency would have allowed the healthcare sector to better prepare and reduced the load of patients they had to deal with, helping flatten the curve. The increased patient load and confusion among citizens arising from not following these practices has proved to be a barrier to providing effective treatments to patients with the disease elsewhere in the world.
Lack of Preparedness and Protocols
Despite the previous SARS outbreak teaching us important lessons and providing us with data on a potential outbreak, many nations did not take the important measures needed for a future outbreak. There was no allocation of sufficient funds for such an event. Many countries experienced severe lack of PPE, and the lockdown precautions hampered the logistics of supply and manufacturing of such essential equipment. Singapore and South Korea had protocols in place and were able to implement them at a moment's notice. The spurt of cases that Korea experienced was managed well, providing evidence to this effect. The lack of preparedness and lack of protocol in other nations has resulted in confusion as to how the treatment may be administered safely to the large volume of patients while dealing with diagnostics. Both of these factors have limited the accessibility to healthcare services due to sheer volume.
During the SARS epidemic, China faced an economic setback, and experts were unsure if any recovery would be made. However, the global and domestic situation was then in China's favor, as it had a lower debt, allowing it to make a speedy recovery. This is not the case now. Global experts have a pessimistic outlook on the outcome of this outbreak ( 98 ). The fear of COVID-19 disease, lack of proper understanding of the dangers of the virus, and the misinformation spread on the social media ( 99 ) have caused a breakdown of the economic flow globally ( 100 ). An example of this is Indonesia, where a great amount of fear was expressed in responses to a survey when the nation was still free of COVID-19 ( 101 ). The pandemic has resulted in over 2.6 billion people being put under lockdown. This lockdown and the cancellation of the lunar year celebration has affected business at the local level. Hundreds of flights have been canceled, and tourism globally has been affected. Japan and Indonesia are estimated to lose over 2.44 billion dollars due to this ( 102 , 103 ). Workers are not able to work in factories, transportation in all forms is restricted, and goods are not produced or moved. The transport of finished products and raw materials out of China is low. The Economist has published US stock market details indicating that companies in the US that have Chinese roots fell, on average, 5 points on the stock market as compared to the S&P 500 index ( 104 ). Companies such as Starbucks have had to close over 4,000 outlets due to the outbreak as a precaution. Tech and pharma companies are at higher risk since they rely on China for the supply of raw materials and active pharmaceutical ingredients. Paracetamol, for one, has reported a price increase of over 40% in India ( 104 – 106 ). Mass hysteria in the market has caused selling of shares of these companies, causing a tumble in the Indian stock market. Though long-term investors will not be significantly affected, short-term traders will find themselves in soup. Politically, however, this has further bolstered support for world leaders in countries such as India, Germany, and the UK, who are achieving good approval ratings, with citizens being satisfied with the government's approach. In contrast, the ratings of US President Donald Trump have dropped due to the manner in which the COVID-19 pandemic was handled. These minor impacts may be of temporary significance, and the worst and direct impact will be on China itself ( 107 – 109 ), as the looming trade war with the USA had a negative impact on the Chinese and Asian markets. The longer production of goods continues to remain suspended, the more adversely it will affect the Chinese economy and the global markets dependent on it ( 110 ). If this disease is not contained, more and more lockdowns by multiple nations will severely affect the economy and lead to many social complications.
The appearance of the 2019 Novel Coronavirus has added and will continue to add to our understanding of viruses. The pandemic has once again tested the world's preparedness for dealing with such outbreaks. It has provided an outlook on how a massive-scale biological event can cause a socio-economic disturbance through misinformation and social media. In the coming months and years, we can expect to gain further insights into SARS-CoV-2 and COVID-19.
KN: conceptualization. RK, AA, JM, and KN: investigation. RK and AA: writing—original draft preparation. KN, PN, and JM: writing—review and editing. KN: supervision.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors would like to acknowledge the contributions made by Dr. Piya Paul Mudgal, Assistant Professor, Manipal Institute of Virology, Manipal Academy of Higher Education towards inputs provided by her during the drafting of the manuscript.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2020.00216/full#supplementary-material
Supplementary Data 1, 2. List of all studies registered for COVID-19 on clinicaltrials.gov .
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Keywords: 2019-nCoV, COVID-19, SARS-CoV-2, coronavirus, pandemic, SARS
Citation: Keni R, Alexander A, Nayak PG, Mudgal J and Nandakumar K (2020) COVID-19: Emergence, Spread, Possible Treatments, and Global Burden. Front. Public Health 8:216. doi: 10.3389/fpubh.2020.00216
Received: 21 February 2020; Accepted: 11 May 2020; Published: 28 May 2020.
Copyright © 2020 Keni, Alexander, Nayak, Mudgal and Nandakumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Krishnadas Nandakumar, firstname.lastname@example.org
This article is part of the Research Topic
Coronavirus Disease (COVID-19): Pathophysiology, Epidemiology, Clinical Management and Public Health Response
Coronavirus disease 2019 (COVID-19): A literature review
- 1 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
- 2 Division of Infectious Diseases, AichiCancer Center Hospital, Chikusa-ku Nagoya, Japan. Electronic address: [email protected].
- 3 Department of Family Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
- 4 Department of Pulmonology and Respiratory Medicine, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
- 5 School of Medicine, The University of Western Australia, Perth, Australia. Electronic address: [email protected].
- 6 Siem Reap Provincial Health Department, Ministry of Health, Siem Reap, Cambodia. Electronic address: [email protected].
- 7 Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia; Department of Medical Microbiology and Immunology, University of California, Davis, CA, USA. Electronic address: [email protected].
- 8 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Clinical Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
- 9 Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, MI 48109, USA. Electronic address: [email protected].
- 10 Medical Research Unit, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia; Department of Microbiology, School of Medicine, Universitas Syiah Kuala, Banda Aceh, Indonesia. Electronic address: [email protected].
- PMID: 32340833
- PMCID: PMC7142680
- DOI: 10.1016/j.jiph.2020.03.019
In early December 2019, an outbreak of coronavirus disease 2019 (COVID-19), caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), occurred in Wuhan City, Hubei Province, China. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of February 14, 2020, 49,053 laboratory-confirmed and 1,381 deaths have been reported globally. Perceived risk of acquiring disease has led many governments to institute a variety of control measures. We conducted a literature review of publicly available information to summarize knowledge about the pathogen and the current epidemic. In this literature review, the causative agent, pathogenesis and immune responses, epidemiology, diagnosis, treatment and management of the disease, control and preventions strategies are all reviewed.
Keywords: 2019-nCoV; COVID-19; Novel coronavirus; Outbreak; SARS-CoV-2.
Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.
- Clinical Trials as Topic
- Coronavirus Infections* / epidemiology
- Coronavirus Infections* / immunology
- Coronavirus Infections* / therapy
- Coronavirus Infections* / virology
- Disease Outbreaks* / prevention & control
- Pneumonia, Viral* / epidemiology
- Pneumonia, Viral* / immunology
- Pneumonia, Viral* / therapy
- Pneumonia, Viral* / virology
COVID-19 (2019 Novel Coronavirus) Research Guide
COVID-19 Research Guide Home
- Research Articles Downloadable Database
- COVID-19 Science Updates
- Databases and Journals
- Secondary Data and Statistics
From the CDC’s COVID-19 (2019 Novel Coronavirus) website :
“COVID-19 (coronavirus disease 2019) is a disease caused by a virus named SARS-CoV-2. It can be very contagious and spreads quickly. Over one million people have died from COVID-19 in the United States.
COVID-19 most often causes respiratory symptoms that can feel much like a cold, the flu, or pneumonia. COVID-19 may attack more than your lungs and respiratory system. Other parts of your body may also be affected by the disease. Most people with COVID-19 have mild symptoms, but some people become severely ill.
Some people including those with minor or no symptoms will develop Post-COVID Conditions – also called “Long COVID.”
May 11, 2023, marks the end of the federal COVID-19 PHE declaration . After this date, CDC’s authorizations to collect certain types of public health data will expire.
The latest situation summary updates are available on CDC’s web page for COVID-19 . “
This guide provides resources for researching COVID-19. In this guide you can find the following:
- The CDC Database of COVID-19 Research Articles became a collaboration with the WHO to create the WHO COVID-19 database during the pandemic to make it easier for results to be searched, downloaded, and used by researchers worldwide.
- The last version of the CDC COVID-19 database was archived and remain available on this website. Please note that it has stopped updating as of October 9, 2020 and all new articles were integrated into the WHO COVID-19 database . The WHO Covid-19 Research Database was a resource created in response to the Public Health Emergency of International Concern (PHEIC). Its content remains searchable and spans the time period March 2020 to June 2023. Since June 2023, manual updates to the database have been discontinued.
- COVID-19 Science Updates : To help inform CDC’s COVID-19 Response, as well as to help CDC staff stay up to date on the latest COVID-19 research, the Response’s Office of the Chief Medical Officer has collaborated with the CDC Office of Library Science to create a series called COVID-19 Science Update . This series, the first of its kind for a CDC emergency response, provides brief summaries of new COVID-19-related studies on many topics, including epidemiology, clinical treatment and management, laboratory science, and modeling. As of December 18, 2021, CDC has stopped production of the weekly COVID-19 Science Update.
- Selected scholarly literature databases and journals available to help you find research about COVID-19.
- Search alerts notify you when new research is published on COVID-19.
- Search alerts available for Ovid , PubMed , Scopus , and News sources .
- Selected sources for secondary data and statistics on COVID-19.
- Selected websites and organizations where you can find more information on COVID-19.
Some resources within this guide are accessible only to those with a CDC user ID and password. Find a library near you that may be able to help you access similar resources by clicking the following links: https://www.worldcat.org/libraries OR https://www.usa.gov/libraries .
Materials listed in these guides are selected to provide awareness of quality public health literature and resources. A material’s inclusion does not necessarily represent the views of the U.S. Department of Health and Human Services (HHS), the Public Health Service (PHS), or the Centers for Disease Control and Prevention (CDC), nor does it imply endorsement of the material’s methods or findings. HHS, PHS, and CDC assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by HHS, PHS, and CDC. Opinion, findings, and conclusions expressed by the original authors of items included in these materials, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of HHS, PHS, or CDC. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by HHS, PHS, or CDC.
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Introduction. On pandemics: the impact of COVID-19 on the practice of neurosurgery
1 Associate Editor, Journal of Neurosurgery Publishing Group;
William T. Couldwell
2 Editor-in-Chief, Neurosurgical Focus; and
James T. Rutka
3 Editor-in-Chief, Journal of Neurosurgery Publishing Group, Charlottesville, Virginia
O n January 8, 2020, a prescient scientific article was submitted for publication to the Journal of Travel Medicine on a pneumonia of unknown etiology that was identified in patients in Wuhan, China, and the potential for its international spread through commercial air travel. 1 This article was in direct response to a communication from the World Health Organization, which reported on 44 patients with pneumonia from Wuhan City, Hubei province, China. 2 On January 8, 2020, the pathogen causing this form of pneumonia was identified as the newest member of the coronavirus family confirmed to cause disease in humans. 3 Clinical epidemiological studies and characterization of affected patients soon followed. 4 Prior to the identification of this new virus, there were 6 known human coronaviruses, of which 4 cause only minor cold-like symptoms, but 2 cause more serious illnesses: Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and Middle East Respiratory Syndrome coronavirus (MERS-CoV).
In the relatively short time span of 3 months since the new coronavirus was identified, the literature on COVID-19 has grown exponentially, from not a single scientific report to over 1600 publications at the time of this writing, across numerous disciplines in the field of medicine, including infectious disease, virology, epidemiology, immunology, critical care medicine, pediatrics, medical education, psychiatry, and surgery. Just now, neurosurgical programs from Wuhan, China, are submitting articles to the Journal describing their experiences combating COVID-19 in their hospitals. According to these reports, endoscopic endonasal skull base procedures should be avoided as much as possible given the high likelihood of aerosolizing virus particles within the nasal sinuses and infecting operating room staff.
In 2003, SARS-CoV gripped the world. More than 8000 total cases of SARS-CoV were reported following the initial outbreak of cases in the Guangdong province of China in 2002. In 2003, the city of Toronto was the site of a large cluster of infected individuals, with over 400 cases documented. Large international meetings planned in Toronto, such as the American Association of Cancer Research annual meeting, the largest cancer research meeting of its kind in the world, were cancelled due to the SARS epidemic. Now, with the COVID-19 pandemic, virtually all the conferences of organized neurosurgical associations and societies around the world have been either cancelled or postponed. International airplane travel is forbidden in most countries. National travel is being discouraged. Practices of self-isolation and social distancing have become the norm.
As the numbers of new COVID-19 cases appear to be flattening in China at the time of the writing of this introduction, a wave of new COVID-19 infections is striking Europe, South America, and North America at an alarming rate. While COVID-19 has limited neurovirulence, its reproductive number (R 0 ) is high, indicating that its transmissibility gives it the potential to infect large proportions of the world population, with enough patients with severe symptoms to overwhelm our healthcare systems, as has been demonstrated in jurisdictions such as northern Italy. The many COVID-19 patients requiring admission to critical care and intensive care units have already led to the prioritization of neurosurgical cases throughout North America.
For these reasons, we thought it prudent to share up-to-date information regarding the best neurosurgical practices during the COVID-19 pandemic. We hope these timely communications will lead to better preparedness for taking care of our patients and maintaining the safety and well-being of all healthcare workers on the front lines, while enabling us to continue to promote innovations through educational and teaching opportunities that may take place outside the operating room. In addition, perhaps there will be an increased focus on basic and translational viral science that will lead to more rapid therapeutic responses.
Accordingly, a series of rapid-communication editorials has been commissioned by the Journal of Neurosurgery Publishing Group (JNSPG) from neurosurgeons in regions severely affected by COVID-19, such as China, South Korea, Singapore, and Italy. We provide communications on the impact of COVID-19 on the practice of particular aspects of neurosurgery ( Fig. 1 ), such as general and specialized adult neurosurgery, spine surgery, and pediatric neurosurgery. The effects of the COVID-19 pandemic on annual neurosurgical meetings, such as the American Association of Neurological Surgeons (AANS), are discussed by Kathleen Craig, Chief Executive Officer, and Chris Shaffrey, President of the AANS. The downstream effects of a reduction in operative experience and formal in-person conferencing on resident education are described in an editorial prepared by neurosurgery residency program directors. In this regard, the importance of web-based conferencing systems has emerged and reached primacy. The effects of COVID-19 on large academic neurosurgical units versus small private neurosurgical practices are compared and contrasted. Members of the Editorial Board of the JNSPG for all three print journals were asked to collate and prepare their experiences in the form of an editorial from across the United States and beyond. And finally, as issues arise when allocating scarce medical resources during pandemics, we present an editorial on the ethics of prioritizing and rationing neurosurgical care during the COVID-19 pandemic.
Impact of COVID-19 on the practice of neurosurgery. Clinical care, neurosurgical procedures, resident/fellow education, neurosurgical research, neurosurgical organizations, national and international travel, and the world economy have all been dramatically altered by the pandemic. New systems are being designed and implemented to offset many of the downstream, deleterious effects of COVID-19. Figure is available in color online only.
In a Ted Talk delivered in 2014, Bill Gates warned that we were ill prepared for the next global catastrophic event, which he predicted would be from another uncontrolled viral epidemic. 5
Despite what we learned from prior epidemics, such as SARS, Ebola, and MERS, the words of Gates and others now appear to have been prophetic. While it is important to recognize the impact of COVID-19 on the practice of neurosurgery through this series of rapid communications in the Journal, it will be equally important to “debrief” on where we will be 6 to 12 months from now. It is our strong hope that we will be able to codify systems of containment so that we can immediately prevent or minimize the spread of diseases such as COVID-19; that we develop standardized systems of care so that a necessary and required stock of personal protective equipment (PPE) is always available; and that we work together to ensure that the prioritization of the care of acutely ill neurosurgical patients is forever seamless, even at times of limited inpatient hospital resources. Through arduous training processes and previous experiences, neurosurgeons are by nature a resilient group of surgical specialists. Accordingly, we hope to demonstrate that our responses to the COVID-19 pandemic will make our specialty stronger, and better prepared for the future.
On the day this piece was submitted, March 30, 2020, we learned of the passing of fellow neurosurgeon Dr. James T. Goodrich, Chief of Pediatric Neurosurgery, Montefiore Medical Center, New York City, from complications related to COVID-19.
The authors report no conflict of interest.
The Centre for Evidence-Based Medicine
Evidence Service to support the COVID-19 response
Coronaviruses – a general introduction
March 25, 2020
Who first described them; why they are called coronaviruses; what they are; how they invade cells; how we detect them
Jeffrey K Aronson Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences University of Oxford
Until recently, most people will never have heard of coronaviruses. But they, and the diseases they cause in humans and animals, have been recognized for over 50 years.
Who first discovered coronaviruses?
Avian infectious bronchitis was first described in newborn chicks in 1931 by Schalk & Hawn (J Am Vet Med Ass 1931; 78: 413–23) and by Bushnell & Brandly in 1933 (Poultry Science 1933; 12: 55-60). These papers were both cited by Beach & Schalm , 1936, who confirmed that the infection was due to a filterable virus and identified two strains, with cross-immunity. The virus was cultivated in 1937 by Fred Beaudette and Charles Hudson, from the New Jersey Agricultural Experiment Station (J Am Vet Med Ass 1937; 90: 51–8 cited by Marks ) and later by Cunningham & Stuart in 1947 .
In 1951 Gledhill & Andrewes isolated a hepatitis virus from mice, now also known to be a coronavirus.
In 1965, the virologist David Tyrrell, Director of the Medical Research Council’s Common Cold Research Unit at Harnham Down near Salisbury in Wiltshire, and his colleague Mark Bynoe published a paper in the British Medical Journal , in which they described a virus, which they called B814, and identified it as a cause of the common cold. They tried to characterize other viruses, but without much success, and thought that viruses of which they found evidence were rhinoviruses.
On 1 April 1967 Tyrell, this time with his colleague June Almeida, from the Department of Medical Microbiology in London’s St Thomas’s Hospital Medical School, identified three uncharacterized respiratory viruses, of which two had not previously been associated with human diseases. They reported that two of the viruses, 229E and B814, of which they published electron micrographs, were indistinguishable from the particles of avian infectious bronchitis.
Then Almeida and Tyrell, with six other colleagues, reported in Nature in 1968 that there was a group of viruses that caused not only avian bronchitis but also murine hepatitis and upper respiratory tract diseases in humans, as shown in Figure 1, taken from their brief annotation, which was published under the general heading “News and Views” (Almeida JD, Berry DM, Cunningham CH, Hamre D, Hofstad MS, Mallucci L, McIntosh K, Tyrrell DAJ. Virology: Coronaviruses. Nature 1968; 220(5168): 650). This is the first recorded instance of the term “coronaviruses”.
The virus of avian infectious bronchitis is classified as a gammacoronavirus, while most of the coronaviruses that infect humans are betacoronoviruses. The human coronavirus HCoV-229E described by Almeida and Tyrrell is an alphacoronavirus.
Figure 1 . Details of the properties of coronaviruses, first published in Nature (1968; 220(5168): 650); David Tyrrell, at the Common Cold Research Unit, Salisbury, Wiltshire, offered to provide a short bibliography to anyone interested in the data on which the table was based
Why are they called coronaviruses?
As the journal Nature reported in 1968, “these viruses are members of a previously unrecognized group which [the virologists] suggest should be called the coronaviruses, to recall the characteristic appearance by which these viruses are identified in the electron microscope.”
The word “corona” has many different meanings (see Appendix 2). But it was the sun that the virologists had in mind when they chose the name coronaviruses. As they wrote, they compared “the characteristic ‘fringe’ of projections” on the outside of the virus with the solar corona (not, as some have suggested, the points on a crown). Figure 2 illustrates this.
Figure 2 . Left: The virions of coronaviruses; Right: The corona of the sun seen during an eclipse
What are coronaviruses and how do they invade cells?
Coronaviruses are single-stranded RNA viruses, about 120 nanometers in diameter. They are susceptible to mutation and recombination and are therefore highly diverse. There are about 40 different varieties (see Appendix 1) and they mainly infect human and non-human mammals and birds. They reside in bats and wild birds, and can spread to other animals and hence to humans. The virus that causes COVID-19 is thought to have originated in bats and then spread to snakes and pangolins and hence to humans, perhaps by contamination of meat from wild animals, as sold in China’s meat markets.
The corona-like appearance of coronaviruses is caused by so-called spike glycoproteins, or peplomers, which are necessary for the viruses to enter host cells. The spike has two subunits; one subunit, S1, binds to a receptor on the surface of the host’s cell; the other subunit, S2, fuses with the cell membrane. The cell membrane receptor for both SARS-CoV-1 and SARS-CoV-2 is a form of angiotensin converting enzyme, ACE-2, different from the enzyme that is inhibited by conventional ACE-1 inhibitors, such as enalapril and ramipril.
Briefly, the S1 subunit of the spike binds to the ACE-2 enzyme on the cell membrane surface. A host transmembrane serine protease, TMPRSS2 , then activates the spike and cleaves ACE-2. TMPRSS2 also acts on the S2 subunit, facilitating fusion of the virus to the cell membrane. The virus then enters the cell. Inside the cell the virus is released from endosomes by acidification or the action of an intracellular cysteine protease, cathepsin.
A model and a more detailed description of these events is shown in Figure 3.
Figure 3 . A proposed model of the mechanisms whereby coronavirus SRA-CoV-2 enters cells
- The coronavirus approaches the cell membrane
- An S1 subunit (red) at the distal end of a glycoprotein spike of the virus binds to a membrane-bound molecule of ACE-2 (blue)
- As more S1 subunits of the glycoprotein spikes bind to membrane-bound molecules of ACE-2, the membrane starts to form an envelope around the virus (an endosome)
- The process continues …
- … until the endosome is complete
- The virus can enter the cell in two ways:
(a) A cell membrane-bound serine protease (brown), TMPRSS2, cleaves the virus’s S1 subunits (red) from its S2 subunits (black) and also cleaves the ACE-2 enzymes; the endosome enters the cell ( endocytosis ), where the virus is released by acidification or the action of another protease, cathepsin
(b) The same serine protease, TMPRSS2, causes irreversible conformational changes in the virus’s S2 subunits, activating them, after which the virus fuses to the cell membrane and can be internalized by the cell
A serine protease inhibitor, camostat mesylate, used in Japan to treat chronic pancreatitis , inhibits the TMPRSS2 and partially blocks the entry of SARS-CoV-2 into bronchial epithelial cells in vitro.
Research interest in coronaviruses
The first coronaviruses found to infect humans were called 229E and OC43, but they caused very mild infections, similar to the common cold. It was not until the outbreaks of SARS (severe acute respiratory syndrome) and then MERS (the Middle Eastern respiratory syndrome or camel flu) that it was appreciated that they could cause serious human infections. Those two infections are thought to have come from bats via civet cats and camels.
This awakening of interest in coronaviruses at different times is reflected in the pattern of publications about them. After the initial description of coronaviruses in 1968 there was a slow increase in the numbers of publications dealing with them, followed by two peaks, after two epidemics: the SARS coronavirus epidemic in 2003–4 and an outbreak of porcine epidemic diarrhoea in North America in 2013 (Figure 4). Identification of the first cases of MERS in Saudi Arabia in 2012, and then elsewhere (e.g. in South Korea in 2015), also caused by a coronavirus, may also have contributed.
I have previously highlighted the fact that the major peaks of interest in the coronaviruses have followed major infections in humans and animals. In my BMJ opinion column on 31 January this year, where some of this article has previously appeared. I wrote that I expected to see another peak in the numbers of publications following the current epidemic. My original graph ended with the 2019 figures. I have now added the latest numbers, from 2020, to the graph, which shows that my prophecy has already been fulfilled. More publications on coronaviruses have been logged in Pubmed in the first 12 weeks of 2020 than in any previous complete year. The difficulty in preventing and treating the infection is matched by the difficulty in keeping up with the published literature.
Figure 4 . Numbers of publications with “coronavirus/es” as text words (blue) or in titles (orange) (source PubMed Legacy); each point represents one year, but the rightmost points cover only the first 12 weeks of 2020
Testing for coronavirus, SARS-CoV-2
Viral RNA can be detected by polymerase chain reaction (PCR, or quantitative PCR, qPCR, sometimes referred to as “real-time PCR” or RT-PCR, causing confusion with another term, “reverse transcriptase PCR”) (Figure 5). In this test, the virus’s single-stranded RNA is converted to its complementary DNA by reverse transcriptase; specific regions of the DNA, marked by so-called primers , are then amplified. This is done by synthesizing new DNA strands from deoxynucleoside triphosphates using DNA polymerase. Occasional false negatives have been reported .
Figure 5 . Reverse Transcriptase Polymerase Chain Reaction (RT-PCR)
- A primer is attached to the 3 prime end of a single strand of viral RNA
- Deoxynucleoside triphosphates are added stepwise …
- … creating a DNA copy of the viral RNA
- The single strand of DNA is separated …
- … and double-stranded complementary DNA (cDNA) is prepared …
- … copies of which are synthesized using primers and DNA polymerase
Step 6 can be repeated many times, doubling the numbers of DNA molecules created each time; 30 steps, for example, will yield 2 30 (i.e. 1,073,741,824) or about 10 9 molecules
An immunoassay has also been described , but it has a high false omission (or exclusion) rate (Table 1).
Table 1 . Diagnostic features of an immunoassay for SARS-CoV-2
Efforts are currently being made to develop and implement an immunoassay for antiviral antibodies to determine whether infection has previously occurred.
Appendix 1: Varieties of coronaviruses
Coronaviridae is the name given to a family of viruses with two subfamilies, Letovirinae and Coronavirinae. The latter has four genera, Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus, These include seven coronaviruses that can infect humans (Table 2). Coronaviruses can also infect non-human mammals (Table 3), they can be carried by birds or infect them (Table 4), and they can be carried by bats (Table 5).
Table 2 . Taxonomy of coronaviruses that can cause disease in humans
Table 3 . Some non-human mammals that can be infected by coronaviruses
Table 4 . Some birds that can carry or be infected by coronaviruses (gammacoronaviruses and deltacoronaviruses)
Table 5 . Some coronaviruses carried by bats
Appendix 2: Meanings of the word “corona”
I have listed the many different meanings of “corona” and some of its derivatives in Table 6 below.
Table 6 . Different meanings of “corona” and some derivatives (based on definitions in the Oxford English Dictionary )
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The Impact of the Global COVID-19 Vaccination Campaign on All-Cause Mortality
The global COVID-19 vaccination campaign is the largest public health campaign in history, with over 2 billion people fully vaccinated within the first 8 months. Nevertheless, the impact of this campaign on all-cause mortality is not well understood. Leveraging the staggered rollout of vaccines, we find that the vaccination campaign across 141 countries averted 2.4 million excess deaths, valued at $6.5 trillion. We also find that an equitable counterfactual distribution of vaccines, with vaccination in each country proportional to its population, would have saved roughly 670,000 more lives. However, this distribution approach would have reduced the total value of averted deaths by $1.8 trillion due to redistribution of vaccines from high-income to low-income countries.
Funding provided by NIA R01AG073286 (Whaley) and the Peter G. Peterson Foundation Pandemic Response Policy Research Fund (Agrawal). We thank Coady Wing and seminar participants at the 2023 ASHE conference for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Neeraj Sood reports personal fees from Amazon outside the submitted work.
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Coronaviruses are a large family of viruses that are common in many different species of animals, including camels, cattle, cats, and bats. Rarely, animal coronaviruses can infect people and then spread between people such as with MERS-CoV, SARS-CoV, and now with this new virus (named SARS-CoV-2).
The SARS-CoV-2 virus is a betacoronavirus, like MERS-CoV and SARS-CoV. All three of these viruses have their origins in bats. The sequences from U.S. patients are similar to the one that China initially posted, suggesting a likely single, recent emergence of this virus from an animal reservoir.
~CDC Coronavirus Disease 2019 (COVID-19) Situation Summary
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Effect of negative emotions in consumption during the COVID-19 pandemic: A study from Peru
Roles Conceptualization, Data curation, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation ESAN Graduate School of Business, Universidad ESAN, Lima, Peru
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Facultad de Economía y Empresa, Universidad Católica de Santiago de Guayaquil, Guayaquil, Ecuador
Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Universidad Espíritu Santo, Samborondón, Ecuador
Roles Conceptualization, Data curation, Formal analysis, Investigation, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing
* E-mail: [email protected]
Affiliation Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
Roles Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Ana G. Mendez University, San Juan, Puerto Rico
Roles Conceptualization, Data curation, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliation Instituto Tecnológico de Puerto Rico, Manatí, Puerto Rico
- Otto Regalado-Pezúa,
- Orly Carvache-Franco,
- Mauricio Carvache-Franco,
- Wilmer Carvache-Franco,
- Maribel Ortiz-Soto,
- Guisell Larregui-Candelaria
- Published: November 3, 2023
- Reader Comments
The research examines the negative consumer emotions generated by the perception of social networks or traditional media with consumer behavior during the covid_19 pandemic. The study was developed in Peru with a sample of 220 consumers; the design is quantitative and structural equations were used for data processing. The results indicate that social networks and traditional media are not related to negative emotions, but are related to the change in consumer behavior in the purchase of more products and new products. The research has theoretical implications since it provides evidence to the literature that the negative emotions generated during the covid_19 pandemic are related to changes in consumer behavior, which affect the purchase of more products and new products. The practical implications of the research is for businessmen on the causes of changes in consumer behavior generated during crises. like the COVID-19 pandemic.
Citation: Regalado-Pezúa O, Carvache-Franco O, Carvache-Franco M, Carvache-Franco W, Ortiz-Soto M, Larregui-Candelaria G (2023) Effect of negative emotions in consumption during the COVID-19 pandemic: A study from Peru. PLoS ONE 18(11): e0293932. https://doi.org/10.1371/journal.pone.0293932
Editor: Anat Gesser-Edelsburg, University of Haifa, ISRAEL
Received: June 1, 2023; Accepted: October 21, 2023; Published: November 3, 2023
Copyright: © 2023 Regalado-Pezúa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data will be published with the manuscript.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
The context of the covid_19 pandemic generated negative emotions such as unhappiness, sadness, shame, fear and anger that have developed influenced by social and traditional media. In crisis conditions such as during pandemics, consumers adopt changes in their behavior to protect themselves from crises and negative emotions such as fear, anger, anguish and anxiety arise.+
Consumers receive information from family and friends about covid_19 through different communication channels, such as social networks and traditional media. This information generates negative emotions, which drive consumers to change their purchasing behavior in different categories.
Panic buying due to the pandemic has increased the demand for products in several nations and economies during the covid_19 pandemic [ 1 ], and its effects have been studied from various dimensions: such as the effect of fear and dread [ 1 ] environmental stimuli such as reflective thinking [ 2 ], uncertainty, severity and scarcity [ 3 ], threat of crisis and scarcity, fear of the unknown and social psychological factors [ 4 ].
Negative emotions such as fear, restlessness and uncertainty created panic buying during the covid_19 pandemic, which are purchases of larger quantities of products and new products. There is a gap in the literature on whether the negative emotions generated in consumers by social networks or traditional media affect or modify the purchasing behavior of new products and products in greater quantity during the pandemic.
The objective of the research is to find out if these negative emotions generated during the pandemic are related to changes in consumer behavior that produce changes in the purchase of products in quantity and new products. This research is carried out in Peru, an emerging economy, to contribute to the literature gap since studies in this type of countries are scarce in developing countries since consumers can adopt different behaviors from consumers in developed countries.
2. Literature review
People tend to feel nervous and insecure when experiencing especially environmental changes [ 5 ]. In the case of infectious disease outbreaks, when the causes of disease progression and outcomes are uncertain, they give room for rumor spreading and closed-minded attitudes [ 6 ]. These responses lead to fear and uncertainty, bringing negative social feelings and behaviors as consequences [ 7 ]. Fear, anxiety, or perceived scarcity may prompt panic buying or hoarding as mitigation mechanisms for perceived risk and negative emotions aroused by the prevailing situation [ 8 , 9 ].
Negative feelings can alter consumer behavior as consumption is a habit, and social context can disrupt consumer habits [ 9 ]. Likewise, sociologists and psychologists have researched and documented that life transition periods are critical phases in a person’s life and are associated with significant behavioral changes [ 10 , 11 ].
Recent significant developments and trends [ 12 ] argue that the consumer landscape operates, in the new times, in a rapidly changing environment and can be described as turbulent and disruptive. Likewise, in these scenarios, significant events are taking place that alter how consumers behave. It is surprising to realize how much the consumer landscape can change from the routine. Stressful events result in the initiation, intensification, or changes in consumption habits to manage the stress caused by social change [ 13 ].
One of the changes that can be observed is panic buying, which occurs when consumers purchase vast quantities of products in anticipation of, during, or after a disaster or in anticipation of an increase or decrease in the prices of needed products [ 4 ]. Panic alludes to intense collective fear and connotes primitive, disorderly, and even violent actions in a catastrophe [ 14 ].
Panic buying is a socially undesirable behavior in which large quantities of necessities and medical supplies are purchased in markets, often giving rise to situations of shortages [ 15 ]. Panic buying by consumers has the potential to exaggerate the consequences of supply disruption [ 16 ]. They increase consumer anxiety about supply shortages and worsen panic buying [ 17 ]. Sheu & Kuo [ 18 ] pointed out that panic buying, or other behaviors, is more about mass behaviors than mixing rational aspects with irrational and emotional ones.
Regarding stockpiling more than current consumption needs, Sheth [ 9 ] noted that consumers have two motives for stockpiling items: (i) as stock to protect against stock-outs given uncertainty about future usage needs, and (ii) for economic reasons, i.e., the convenience of stocking up on storable goods when a supply becomes available, i.e., when retailers offer goods at a relatively low price.
The covid_19 pandemic increased depression in people, and in this state receiving negative messages from news or advertisements caused a greater negative impact on people, causing them greater anxiety and panic or fear, which alters their consumption behavior [ 19 ]. The main changes in consumer behavior during the pandemic were: abnormal purchasing behavior, changes in product preferences, greater use of technology and digital media to make purchases [ 20 ]. Various intentions of consumption behaviors have emerged with covid_19 influenced by fear and hope, such as behavior focused on health, conscious consumption and support for local products, this mainly due to the vulnerability they perceived, which is why they acted with actions of protection. protection [ 21 ]. Covid_19 caused an impact on the change in the lifestyle and purchasing behavior of consumers influenced by the socioeconomic environment of consumers and has been proven to have a greater impact on consumers from less organized sectors, which caused an increase in substitute products for daily activities [ 22 ]. Consumer purchasing behavior mainly impacts sustainable products. During the pandemic, there was greater awareness, concern and environmental habits, so consumers were predisposed to pay more for sustainable products, but the change is affected by the demographic variables such as gender, age, income level and education [ 23 ].
The theory of panic-created behavior by Schultz [ 24 ], was compared with other theories put forward by colleagues/experts of the time [ 25 ]. Scholars have widely mentioned and studied this behavior in the wake of the covid_19 pandemic [ 26 – 29 ]. Addo et al. [ 30 ] further noted that panic buying is expected to lead to price changes in times of crisis, such as the current one caused by covid_19.
Furthermore, it is imperative to understand the impact and course of the pandemic caused by covid_19 on panic buying [ 30 ]. There are psychological and economic explanations for this stockpiling behavior in a crisis. A common psychological explanation is that accumulating storable goods gives consumers a sense of control over the risky situation created by a crisis [ 31 ].
Panic buying represents a relatively unexplored area in consumer behavior research, where purchase decisions are affected by emotions, such as fear of the unknown, anxiety, and social influences [ 32 , 33 ]. During the covid_19 pandemic, panic and fear due to the excessive increase in prices of various products and the fear of greater shortages caused social influence to change consumer behavior to increase their purchases of some products [ 34 ].
Cohen [ 35 ] substantiates the theory of moral panic based on the reaction of a group of people to a perception that creates fear. A moral panic occurs when a condition, episode, person, or group of people emerges to be defined as a threat to social values and interests [ 31 ]. In extreme cases, moral panic creates mass hysteria within society, and the general public begins to believe that everything reported is happening everywhere [ 31 ].
Cohen [ 10 ] established five stages of moral panic: (1) something or someone is defined as a threat to values or interests, (2) this threat is represented in an easily recognizable form by the media, (3) there is a rapid build-up of public concern, (4) there is a response from authorities or opinion makers, and finally (5) the panic recedes or results in social change.
2.1. Effect of culture on consumer purchasing behavior
According to Sanz Blas et al. [ 36 ], there are factors involved in adopting online shopping innovation; one of them is culture, as it represents a set of shared values that can influence consumer perceptions, attitudes, preferences, and responses. Consumers can be affected by high or low-context cultures and collectivist or individualistic cultures [ 37 ].
The Latin American consumer differs in many ways from consumers in other parts of the world. One of the characteristics is the attachment or bond they have with the things or objects they acquire; this is because they are conservative, and it is difficult for them to get rid of an object even if it has gone out of fashion or is obsolete. They consider their belongings extensions of themselves, and affective bonds are generated towards what they acquire [ 38 ].
The main concern of Latin American consumers is economic uncertainty. Over and above the covid_19 health crisis, many consumer attitudes have changed, and five behavioral changes have been observed: the first is Mindful Consumption, which refers to the consumer being more attentive to the value of the products they consume; the second is Always Mobile which refers to the new consumer making more purchases on digital platforms; the third is Eco Doing, which is based on the concern for environmental and social sustainability; and the fourth is Responsumers, which reflects that people are more demanding with brands, companies, institutions, the fifth is Wellbeing Reloaded, which refers to new habits of integral wellbeing, such as concern for the food’s origin [ 39 ].
2.2. Consumer behavior of Peruvian shoppers
Bardales and Herrera [ 40 ] considered that the Peruvian consumer had become a net prosumer, he identifies with brands, but now he wants brands to identify with him. It can even destroy a brand, as it did with the case of Domino’s Pizza ® , except love brands unfaithful by knowledge and increasingly more rational when choosing offers and comparing with the information they have at hand. Peruvian consumers are more demanding when buying and have more power than before, especially in social networks. They have the information and a more remarkable ability to demand and therefore believe that a future trend will be the increase of this demand.
According to Alvarez [ 41 ], confinement and social distancing interrupted interpersonal relationships and missed family and friends meetings. Outdoor activities are increasingly revalued; returning to shopping malls and restaurants with the confidence and security of the case would be highly appreciated. Most Peruvians state that a vaccine passport should be required to enter them, even in large spaces such as stadiums or in small ones such as stores and offices.
Navarro [ 42 ] points out that during the crisis, many consumers changed several of their habits: trying new brands/products instead of the ones they used to buy, and these changes may make them try other products, evaluate the price and performance to probably consider them in the future within their usual shopping list. What will be then their new shopping habits in the future?
2.3. Communication channels (traditional social networks) and their influence on Peruvian consumers’ purchasing behavior
For Okazaki et al. [ 43 ], the emergence of social networks has significantly impacted how companies promote their products and services and consumers’ decision-making process regarding their purchases—using the application and extension of the proposed models. Consumer behavior during the covid_19 pandemic was affected by various environmental stimuli, such as social networks that, together with other stimuli such as the economic recession, partial lockdown regulations and restrictions on some services, influenced a change in behavior in the purchase of consumer goods, less impulsive and more planned companies, less frequent purchases [ 44 ].
According to Pfeiffer & Zinnbauer [ 45 ], recipients also have communication channel preferences. It is difficult for advertisers to measure the effectiveness and results of marketing campaigns, especially when using traditional communication channels in the service sector, because it creates a challenge for the marketing decision-maker to allocate the marketing budget most efficiently.
2.4. Effect of the covid_19 pandemic crisis on the Peruvian consumer’s purchase behavior (new products, quantity of products)
Nielsen [ 46 ] explained that consumer habits in Latin America were mainly marked by socioeconomic factors affected by rising unemployment and change in the economy. They identified five predictive factors in the purchase process: readjustment of the basket, increased purchase of digital formats, increased consumption at home, more empathetic brands, and search for reactivation. Similarly, [ 47 ] noted that, concerning consumption habits, the arrival of covid_19 brought changes in purchasing behavior, including a 29% increase in spending on food, 15% on dairy products, and 12% on home care items. The most consumed food is flour.
The behavior of Peruvian consumers during covid_2019 was different, since they tried to avoid waste, when making purchases they were oriented on cost-benefits, carrying out prior purchasing planning with knowledge of the labels and storage of products, they also considered their own culinary skills for these products [ 48 ]. The covid_19 pandemic shows the resilience to everyday consumption rooted in the family to the extent that new rules and norms were imposed in society, so new consumption habits were acquired by consumers [ 49 ].
According to Kantar [ 39 ] on household consumption habits, the most critical finding was the comparison of purchased tickets in comparison of the years 2019, 2020, and 2021. The first comparison of the first quarter between the years 2019 and 2021 showed a positive transformation; however, in the same comparison of tickets for the same period (1st quarter) of the years 2020 and 2021, the growth was higher (25%) even achieved before the pandemic.
In a previous study in the Peruvian market during the covid_19 pandemic, it was found that there was a change in consumer purchasing behavior influenced by social factors, that is, by external influences on society, and by psychological factors in the population, while no incidence was found. of cultural factors and personal factors in purchasing behavior [ 50 ]. In other Latin American countries, it has been found that the covid_19 pandemic had an impact on the flow of sustainable consumption, the consumer purchases with greater environmental awareness and social responsibility [ 51 ].
2.5 Study hypothesis
Because consumers’ prolonged exposure to adverse reports on social media during crises can impact fears and negative emotions such as fear and panic [ 52 ], social media can alter consumers’ emotions, as risky situations are perceived during crises [ 53 ], the following hypothesis is proposed.
H1 = Social media positively affects negative emotions.
Traditional media during crises contribute to fear and panic and produce emotions in consumers influenced by panic [ 54 ], producing emotions and influencing the audience [ 55 ]. The following hypothesis is proposed.
H2 = Traditional media positively impact negative emotions.
Considering that fear, anxiety, or perceived scarcity can propitiate change in consumer behavior. Emotions, such as panic buying or hoarding, act as mitigation mechanisms of perceived risk and prevailing situations [ 8 , 9 ], so negative emotions can alter consumer behavior [ 18 ]. The following hypothesis is proposed.
H3 = Negative emotions impact changes in consumer behavior.
It is considered that negative emotions produced by fear produce panic purchases that generally correspond to the acquisition of more products to mitigate the risk and situation [ 8 , 9 ]. The following hypothesis is proposed.
H4 = Changes in consumer behavior impact the purchase of new products.
It is considered that negative emotions produced by fear produce panic purchases that generally correspond to the accumulation of products to mitigate the risk and situation [ 8 , 9 ]. The following hypothesis is proposed.
H5 = Changes in consumer behavior impact the purchase of more products.
Fig 1 below shows the conceptual model for observing the variables and hypotheses.
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In order to evaluate the various relationships of the hypotheses, the following conceptual model of Fig 1 is proposed, which shows the various relationships mentioned in the hypotheses.
3.1. Design and instrument
The design used is quantitative, non-experimental, and cross-sectional. The questionnaire is composed of the following parts (1) demographic part with questions on gender, age range, degree of schooling, education level, and income range (2) communications through social media (3) communications through traditional media, (4) negative emotions (5) changes in consumer behavior (6) new products and (7) more quantity of products. The scale used is the 5-point Likert scale. The evaluation instrument was developed by the researchers after analyzing the literature review looking for the statements included to answer the objectives of the study. After the instrument was designed, it was validated by 5 experts in marketing and research methodology. These experts evaluated the validity of the instrument, ensuring comprehension, proper order of the questions, and that the instrument measured the proposed objectives. The recommendations given by this group were incorporated and reviewed in the instrument.
The validity and reliability of the questionnaire was evaluated with a representative group of the population using a pilot test with 40 people. With the results obtained from the test, a PLS was carried out to preliminarily evaluate the behavior of the variables and the model. Using the results of the PLS, the reliability and initial validity of the instrument were calculated. Firstly, the loads obtained (Factor Loading) were analyzed. The results of the loads are obtained from the calculation of the SmartPLS algorithm, all the results less than 0.40 are eliminated to comply with the rule of Hair et al. [ 56 ], and the process was repeated, computing the SmartPLS algorithm again, obtaining results where all the charges were above 0.40. Then the Alpha coefficients and the convergent validity of each variable were analyzed and the results reflected that the majority met the .70 criterion as established by Hair et al. [ 56 ] and Henseler et al. [ 57 ]. The behavior change variable obtained a Cronbach’s Alpha of 0.63. However, Hair et al. [ 58 ] clarify that a value between 0.60 and 0.69 indicates that the value, although it is a weak one, may be acceptable; a value below 0.59 is considered an unacceptable value to carry out the investigation. In the same way, the AVE values mostly reflected results above 0.50, concluding that the latent variables explained more than half of the variance on their indicators, according to the 0.50 criterion of Hair et al. [ 56 ], with the exception of the variables products with more quantity (0.45) and new products (0.47). However, Hair et al. [ 58 ], clarifies that the values can be between 0.40 to 0.70.
3.2. Data collection
The questionnaire was also ethically approved by the ESAN Graduate School of Business of Peru and included the participants’ written informed consent. The sample comprised 220 people and was taken in Peru in 2021. The sampling used was non-probabilistic and of convenience. The questionnaire was administered online via SurveyMonkey.
The population object of the investigation will be men and women over 21 years of age residing in Peru. To determine the research sample, "10 times the rule" was used [ 59 ], which indicates that the sample size must be equal to the greater than 10 times the largest number of formative indicators used to measure a single construct or 10 times the largest number of structural pathways in a particular construct targeted in the structural model [ 58 ]. Therefore, it is equivalent to saying that the minimum sample size should be 10 observations for each relationship. On this rule, it was determined that the sample size will be 200 duly completed questionnaires for the investigation. The researchers have established a 95% confidence level and a 5% confidence interval. Among the inclusion criteria for this research must be men or women over 21 years of age. In the exclusion criteria is that the participants are under 21 years of age.
3.3. Data processing
A confirmatory factor analysis is applied, and criteria of reliability, convergent and discriminant validity are considered. Reliability is assessed based on composite reliability (CR), that is, the degree to which items are free of error and therefore produce reliable results, using CR > = 0.70 as the appropriate value [ 60 ]. Convergent validity is verified using factor loadings greater than 0.5 and a minimum variance (AVE) of 0.5, while discriminant validity is determined by a minimum value of AVE = 0.5 [ 60 ].
Subsequently, the method of structural equations is applied to evaluate and test the relationships proposed in the hypotheses and to evaluate the model, indices are selected as Comparative Fit Index CFI, Goodness of Fit Index (GFI), and Normed Fit Index (NFI), used as a measure of comparison that CFI > = 0.9 [ 61 ], GFI > = 0.9 [ 62 ] and NFI > = 0.9 [ 61 ]. Likewise, the root mean square error of approximation (RMSEA) index was used, considering an appropriate measure of 0.05 to 0.08 [ 62 – 64 ].
The AMOS software is used to perform path analysis or the analysis of the relationships between variables, determining the coefficient (β) and the standard error (S.E) and the p-value. To accept the hypotheses, those with a p-value or significance less than 0.05 are considered supported or accepted.
Descriptive results were determined and are shown in Table 1 , in which it is determined that in the sample, broken down by gender, men represent 55.90%. The age group that predominates in the sample is 41–55 years old, with 38.6%. Regarding schooling level, those with an associate or technical degree predominate with 34.5%, and those with a high school degree with 42.3%. Regarding annual income level, most earn between USD20,001 and USD35,000 (25.50%).
In relation to the confirmatory factorial analysis, Table 2 shows the CR values obtained between 0.75 and 0.94, which are acceptable values for CR > 0.70, AVE value and values of factor loads that are greater than 0, 50, which meets the criterion of good convergent validity and verifies that the values of the explained mean variance are more significant than 0.5, which indicates good convergent validity.
The fit of the model was verified. The χ2/df ratio was calculated to be 3.01, considered adequate since an acceptable value of χ2/df < = 3 [ 53 ]. The CFI, GFI, and NFI indices were checked and obtained values of CFI = 0.822, close to the comparison value of 0.9 [ 50 ], GFI = 0.82, close to the comparison value of 0.9 [ 51 ], and NFI = 0.756 close to the comparison value 0.9 [ 50 ] so they are considered acceptable values and the RMSEA value was 0.09 also a value close to the reference value of 0.08 [ 51 – 53 ]. The model is considered to have a good fit level with these values obtained. Table 3 shows the values obtained from the model fit.
Each hypothesis was evaluated by obtaining the coefficient β and the p-value or probability value for each relationship. Table 4 shows the result of the path analysis. It is shown that the negative path emotions and social media, with a coefficient β = 0.054 and a p-value of 0.489 greater than 0.05, does not support hypothesis H1. While the negative path emotions and traditional media, with a coefficient β = -0.008 and a p-value of 0.915 greater than 0.05, supports hypothesis H2.
In contrast, the path changes in consumer behavior and negative emotions, with a coefficient β = 0.126 and a p-value less than 0.05; therefore, hypothesis H3 is supported. The path of new products and changes in consumer behavior, with a coefficient β = 1.505 and a p-value less than 0.05, shows that hypothesis H4 is supported. Finally, the path more products and changes in consumer behavior, with a coefficient β = 1.668 and a p-value less than 0.05, supports hypothesis H5. The structural model is shown in Fig 2 .
The research aims to examine the relationship between social media and traditional media on consumers’ negative emotions during the covid_19 pandemic and whether consumers’ negative emotions are related to the change in consumer behavior that affects the purchase of products in terms of quantity and new products.
The results of hypotheses H1 and H2 indicate that social media and traditional media are not related to consumers’ negative emotions during the covid_19 pandemic; this is justified because other causes could have contributed to generating negative emotions during the pandemic, such as environmental stimuli and reflective thinking [ 2 ], perception of uncertainty, severity, and scarcity [ 3 ], crisis threat and scarcity, fear of the unknown, social psychological factors [ 4 ] generated in the social environment that not necessarily that originate from social media or traditional media.
The results of hypotheses H3, H4, and H5 indicate that the negative emotions generated by consumers during the pandemic covid_19 changed consumer behavior that affected the purchase of new products and more products. These results agree with Sheth [ 9 ], who mentioned that fear and dread as negative emotions generated during the covid_19 pandemic caused changes in consumer buying behavior as they resorted to impulse purchases and are in agreement with [ 4 ], who mentioned that there are psychological causes that produce panic in consumers during crises. These change their purchasing behavior in acquiring more significant quantities of products because of the crisis and agree with [ 65 ], who mentioned that consumers, as a form of defense, buy larger quantities of products than usual during crises. Similarly, they also buy new products because they perceive that products may be in short supply.
The changes in consumer behavior are explained by George and Dane [ 66 ], who indicated that emotions in consumers produce changes in their buying behavior and [ 67 ] that emotions can vary rationality in people. Additionally, Willman-Iivarinen [ 68 ] mentions that consumer buying choice is affected by factors such as time pressure and buying opportunities, both identified in crises such as covid_19.
According to Amalia et al. [ 69 ], people are different, as is their perception of a situation, and risk perception reflects the buyer’s interpretation of their consumption. As mentioned by Bagozzi et al. [ 70 ], the messages and perceptions that consumers receive produce negative and positive emotions before purchases, which are mixed and form the anticipated emotions that impact the consumer’s purchase decision.
This research contributes to the literature because although it is known that the covid_19 pandemic generated panic purchases produced by fear, panic, and negative emotions [ 1 ], little is known if social media and traditional media related to negative emotions in consumers during the covid_19 pandemic, and if these negative emotions generated in the context of the covid_19 pandemic are related to higher quantity purchases and purchases of new products, in other economies.
Fear is a great motivator that, depending on its effect, can cause very specific changes in consumer buying behavior. The interpretation of fear can be a very particular purchase motivator. Therefore, the emotion of fear must be understood from its different dimensions. Each crisis is different and the peculiarity of each one of them is what can define the new purchasing behavior of the consumer.
The COVID 19 pandemic generated great fear in the consumer, inducing a change in behavior in terms of increasing the amount of product and purchasing new products. The results show that each crisis can prompt the consumer to change their purchasing behavior according to their interests and concerns.
The research concludes that in Peru social networks and traditional media are not related to the negative emotions of consumers during the covid_19 pandemic, that is, these media did not have a strong influence on the perception of risk or the generation of fear due to the crisis of the pandemic. In addition, the research finds that the negative emotions that consumers had during the covid_19 pandemic are related to changes in consumer behavior and have a positive effect on the purchase of more products and purchases of new products.
The research has theoretical implications because it contributes evidence in the context of Peru. The effect of social media and traditional media, such as newspapers and radio, on negative emotions during the covid_19 pandemic contributes to the evidence that the negative emotions of consumers during the covid_19 pandemic affect purchases of a higher quantity of products and new products.
This research has practical implications for business managers and academics, as they can learn about the changes in consumer behavior resulting from negative emotions that consumers may have and how this affects product quantity purchases and new product purchases, which can be helpful for sales planning during crises such as the covid_19 pandemic.
The study contributes from the perspective that store owners and marketing specialists must understand the type of emotion that consumers feel in a crisis such as the covid_19 pandemic so that they can design strategies that meet their expectations and the needs of companies and of the clients.
This research has limitations due to the temporality of the data that was taken during the year 2021. Further research on negative emotions, such as panic and fear, and their effect on consumer behavior in other crisis contexts and economic contexts are suggested as future research to understand the influence of negative emotions on consumer purchasing decisions.
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This paper is in the following e-collection/theme issue:
Published on 2.11.2023 in Vol 25 (2023)
Temporal and Emotional Variations in People’s Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data
Authors of this article:
- Jing Dai 1 * , PhD ;
- Fang Lyu 1 * , MS ;
- Lin Yu 1 ;
- Yunyu He 2 , MM
1 Kunming University of Science and Technology, Kunming, China
2 The First People’s Hospital of Yunnan Province, Kunimg, China
*these authors contributed equally
Yunyu He, MM
The First People’s Hospital of Yunnan Province
57 Jinbi Road
Phone: 86 18987253562
Email: [email protected]
Background: The COVID-19 pandemic has had profound impacts on society, including public health, the economy, daily life, and social interactions. Social distancing measures, travel restrictions, and the influx of pandemic-related information on social media have all led to a significant shift in how individuals perceive and respond to health crises. In this context, there is a growing awareness of the role that social media platforms such as Weibo, among the largest and most influential social media sites in China, play in shaping public sentiment and influencing people’s behavior during public health emergencies.
Objective: This study aims to gain a comprehensive understanding of the sociospatial impact of mass epidemic infectious disease by analyzing the spatiotemporal variations and emotional orientations of the public after the COVID-19 pandemic. We use the outbreak of influenza A after the COVID-19 pandemic as a case study. Through temporal and spatial analyses, we aim to uncover specific variations in the attention and emotional orientations of people living in different provinces in China regarding influenza A. We sought to understand the societal impact of large-scale infectious diseases and the public’s stance after the COVID-19 pandemic to improve public health policies and communication strategies.
Methods: We selected Weibo as the data source and collected all influenza A–related Weibo posts from November 1, 2022, to March 31, 2023. These data included user names, geographic locations, posting times, content, repost counts, comments, likes, user types, and more. Subsequently, we used latent Dirichlet allocation topic modeling to analyze the public’s focus as well as the bidirectional long short-term memory model to conduct emotional analysis. We further classified the focus areas and emotional orientations of different regions.
Results: The research findings indicate that, compared with China’s western provinces, the eastern provinces exhibited a higher volume of Weibo posts, demonstrating a greater interest in influenza A. Moreover, inland provinces displayed elevated levels of concern compared with coastal regions. In addition, female users of Weibo exhibited a higher level of engagement than male users, with regular users comprising the majority of user types. The public’s focus was categorized into 23 main themes, with the overall emotional sentiment predominantly leaning toward negativity (making up 7562 out of 9111 [83%] sentiments).
Conclusions: The results of this study underscore the profound societal impact of the COVID-19 pandemic. People tend to be pessimistic toward new large-scale infectious diseases, and disparities exist in the levels of concern and emotional sentiments across different regions. This reflects diverse societal responses to health crises. By gaining an in-depth understanding of the public’s attitudes and focal points regarding these infectious diseases, governments and decision makers can better formulate policies and action plans to cater to the specific needs of different regions and enhance public health awareness.
Over the past century, COVID-19 has emerged as one of the most widespread and impactful diseases. During the COVID-19 pandemic, the rapid transmission and extensive reach of the novel coronavirus, as well as the potentially fatal symptoms of the disease, were a cause for great concern. This not only had a profound impact on people’s lives and economies but also triggered widespread panic, which could lead individuals to experience fear, anxiety, and panic-driven behaviors, such as hoarding supplies or avoiding public places. Therefore, effective risk communication and emotional management are of paramount importance in mitigating the panic effect. In the backdrop of the COVID-19 pandemic, studying people’s focus and emotional orientation when confronted with new large-scale infectious diseases becomes crucial.
Research has shown that effective information dissemination can alleviate people’s fear of infectious diseases. Consequently, public awareness and an understanding of large-scale infectious diseases play a pivotal role in alleviating panic and prompting individuals to take action against this challenge. Personal perceptions of risk are often influenced by emotions. Positive emotions can make people more attentive and inclined to take proactive protective measures. When individuals have a more positive attitude toward pandemics, the recovery rate and control tend to be higher [ 1 ]. Conversely, negative emotions may lead to avoidance or inaction. Therefore, discussing the public’s attention to outbreaks of contagious diseases and the emotional shifts after the COVID-19 pandemic not only provides insights into changes in public attention to contagious diseases but also helps identify positive and negative emotions, as well as provides a more comprehensive understanding of the public’s stance on large-scale infectious diseases.
Simultaneously, discussing the public’s focus on large-scale infectious diseases and emotional orientation can not only help clarify the public’s perspectives on this issue but also assist in identifying the factors influencing emotions, both positive and negative, toward infectious diseases. Social media platforms play a vital role in disseminating information and shaping public opinion. Governments, health organizations, and public intellectuals can use these platforms to convey accurate information, reduce the spread of false information, and actively engage the public’s attention and actions regarding large-scale infectious diseases.
People are now more willing to express their opinions web-based and there is an abundance of data on social media platforms. Because of the convergence of opinions on the web, researchers can explore the changes in public discussion during the time change and likewise can focus on the public’s changing emotions about it. Considering current trends in technology, especially the role of computer science, it must be acknowledged that computer technology has made a major contribution to medical decision-making with regard to, for example, infectious diseases and epidemics [ 2 , 3 ]. The accurate and logical access sources of these data include social media platforms, which provide more valuable data than ever.
Weibo is among the largest and most influential social media sites in China. Weibo users can share their opinions, discuss current events, and express their emotions via PC using text, share pictures, and upload videos. Therefore, Weibo is an ideal platform for obtaining data sources of popular opinion texts. In addition, the opinions expressed on social networks are highly emotionally oriented; therefore, it is essential to analyze the emotions in the texts and content posted by users. Positive emotions are critical for motivation, perseverance, and prosocial behavior [ 4 , 5 ].
The existing literature on influenza sentiment orientation is mainly about COVID-19; for example, the study by Yin et al [ 6 ] is based on 13 million posts related to COVID-19 pneumonia collected over 2 weeks on Twitter (subsequently rebranded X), and the study by Harba et al [ 7 ] investigated how consumer sentiment evolved during the COVID-19 outbreak through content analysis and sentiment analysis of the texts of web-based restaurant reviews. Other mass infectious diseases have been studied to a lesser extent. Ng et al [ 8 ] studied public sentiment on the global outbreak of monkeypox on Twitter and analyzed 352,182 posts via unsupervised machine learning.
However, it is rare for an analysis of emotional orientation to analyze people’s attitudes toward other mass infectious diseases after experiencing the COVID-19 pandemic. Therefore, analyzing the public’s sentiment and changing views on the currently prevalent mass infectious disease, influenza A, through content posted on Weibo can accurately reflect the importance of public opinion in promoting policies related to epidemic prevention, increasing public awareness and participation in protective actions against the epidemic, and advancing the epidemic management process.
In surveys about emotions (questionnaires or interviews), respondents or interviewees may be influenced by the content of the questions or consider privacy issues and negative impacts, leading to difficulties in assessing emotions accurately and reasonably. Moreover, questionnaires do not allow access to, say, real-time influenza A sentiment, and data collection takes a long time and has high economic costs [ 9 ]. Therefore, we chose text mining as the research method to ensure the spatial and temporal diversity of data. Moreover, text mining applications have been used in various areas, including tourism [ 10 , 11 ], business [ 12 , 13 ], education [ 14 , 15 ], and health care [ 16 - 18 ] for a variety of beneficial purposes.
On the basis of the analysis described in the previous subsection, this study used a web crawler approach to obtain people’s opinions about influenza A. Thematic model analysis and sentiment analysis were used to explore people’s attention, concerns, and sentiments about the recent epidemic of the mass infectious disease. The topic analysis used latent Dirichlet allocation (LDA) to extract latent topics from comment text data. For comment text sentiment analysis, deep learning, that is, the bidirectional long short-term memory (BiLSTM) model, was chosen to classify sentiment. This study attempts to answer the following questions:
- How concerned are people about the recently prevalent infectious disease, influenza A, after experiencing the COVID-19 pandemic? How does the concern differ from province to province?
- What are the spatiotemporal differences between the total number of blog posts and the public’s attention to influenza A?
- What are the changes in the public’s attitude toward infectious diseases after experiencing the COVID-19 outbreak? What are the most critical concerns of the people when a new infectious disease is spreading?
- What is the public sentiment toward epidemic infectious diseases? How does it vary by region?
- What are the drivers of positive and negative emotions?
We primarily used web crawling techniques to acquire data. After preprocessing the data, we further explored and analyzed the data using 2 models, LDA and BiLSTM, and obtained some meaningful conclusions, as shown in Figure 1 .
We used web crawling techniques to collect 9351 posts on Weibo related to “influenza A” from November 1, 2022, to March 31, 2023. These data were used to create a data set that included user names, locations, posting times, content, repost counts, comments, likes, user types, and more.
To ensure the validity and stability of the data set, we removed duplicate data, deleted posts with <6 characters, and eliminated meaningless stop words, expressions, punctuation marks, and numbers. We also conducted semantic integration by summarizing words with similar meanings in the vocabulary.
Text Mining Analysis
Lda topic model.
The standard topic models are latent semantic analysis, probabilistic latent semantic analysis, LDA, and hierarchical Dirichlet process. On the basis of text features and research needs, this study used LDA to extract latent topics from comment text data. LDA is an unsupervised machine learning technique that can identify potential topic information in large document sets or corpora. It uses a bag-of-words approach that treats each document as a vector of word frequencies, thereby converting textual information into numerical information that can be easily modeled. The model was first proposed by Blei et al [ 19 ] in 2003, along with the concepts and ideas of the LDA model. It is a 3-level Bayesian probabilistic model containing words, topics, and documents, and the document generation process is shown in Figure 2 . In this study, the LDA model was used to investigate public attention to potential topics and understand the focus of public attention.
Comment text data are typically categorized into positive and negative sentiments for comment text sentiment analysis. There are 3 approaches to text sentiment analysis: sentiment analysis based on sentiment lexicon, sentiment analysis based on machine learning, and sentiment parsing based on deep knowledge. However, when it comes to sentiment analysis of medical service reviews, using a sentiment dictionary constructed based on electronic commerce reviews may lead to significant errors. Deep learning methods have shown clear advantages in sentiment analysis, breaking free from complex rule-based setups and demonstrating superior recognition performance, with the evaluation metrics and results significantly outperforming those achieved using traditional rule-based learning models. Research into deep learning models for sentiment recognition has primarily focused on the field of neural networks. However, owing to the large number of parameters in deep neural networks, they tend to overfit on limited data sets. To address this challenge, Vaswani et al [ 20 ] introduced the transformer deep learning model, which combines self-attention mechanisms, achieving fast and parallelized training and effectively addressing the issues of slow training and overfitting. Pretrained models have found extensive application in natural language processing tasks, particularly in domain-specific sentiment analysis. Nevertheless, regular corpora often fail to cover various domain-specific terminologies, resulting in certain limitations in the application of pretrained models such as bidirectional encoder representations from transformers in sentiment analysis research within the field of web-based sentiment. Research results indicate that, compared with other traditional sentiment analysis methods such as long short-term memory (LSTM), recurrent neural network, convolutional neural network, and naïve Bayes, BiLSTM models exhibit higher efficiency because they can effectively capture semantic information, achieving >90% accuracy in context understanding [ 21 ]. In sentiment analysis, positive and negative sentiments are typically the core focus because they directly relate to emotional polarity, which is crucial for many applications, such as sentiment trend analysis. Although some sentiment analysis tasks may include the classification of neutral sentiments, this choice often depends on specific application scenarios. Nevertheless, to maintain the research’s focus and clarity, we opted to solely concentrate on positive and negative sentiments. After careful consideration, we selected deep learning, specifically the BiLSTM model, for sentiment classification and categorized sentiment values into positive and negative emotions.
BiLSTM is a bidirectional recurrent neural network that takes the entire sentence’s words as input and considers the contextual information of the text. This allows information to be processed in both forward and backward directions [ 22 , 23 ]. As illustrated in Figure 3 , BiLSTM combines forward LSTM and backward LSTM. Compared with convolutional neural network and LSTM, the BiLSTM model demonstrates superior performance, achieving an accuracy rate of >90% [ 21 ]. The internal structure of LSTM is depicted in Figure 4 .
This study was approved by the medical ethics committee of the First People’s Hospital of Yunnan province (2022ZYFB001). The study used open-access social media data and excluded all personal information; therefore, informed consent was not required.
Basic Information About Blog Posts and Public Attention
Trends in the number of blog posts and public attention over time.
First, a fundamental descriptive statistical analysis of blog post volume was conducted to analyze the trend in public concern about influenza A. As seen in Figure 5 , the public concern about the change in influenza A showed a significant increasing trend over time. In November 2022, there were 231 posts on Weibo related to the influenza A. In December, this number increased significantly to 1073 posts. Moving into January 2023, there were 194 posts, and in February, the number surged to 1703 posts. By March, the conversation intensified further, with a total of 5910 posts on the topic. After the COVID-19 pandemic, influenza A is a recent epidemic that has received much attention.
Spatial Difference Analysis of Blog Posts and Public Attention
There are considerable regional variations in the levels of concern about influenza A among Chinese provinces. This paper investigated the correlation between the number of blog posts and concerns about a potential mass epidemic in different areas of China, revealing some intriguing findings.
The study involved calculating the number of blog posts and the level of worry about influenza A for each of the 34 provinces. The findings, as depicted in Figure 6 , highlight a substantial disparity in the number of blog posts between China’s eastern and western regions. Specifically, the Yellow River basin (including the provinces of Henan, Shandong, Hebei, and Shanxi) exhibits a relatively high number of blog posts, whereas the northwest region demonstrates the lowest. This pattern corresponds to the trapezoidal downward development trend observed in China, where the number of blog posts gradually diminishes from the eastern coastal areas to the western inland regions. Furthermore, the analysis identifies Beijing as the province with the highest number of published blog posts. In terms of ranking, Beijing, Zhejiang, Jiangsu, Shandong, and Sichuan occupy the top 5 positions. This indicates that these provinces expressed greater concern about influenza A than the others.
Comparative Analysis of Influenza A Attention Among Different Genders and User Types
Among different genders, there are 2122 male users and 5997 female users. It is evident that the number of posts made by female users surpasses that of male users. This observation suggests that women exhibit a higher level of concern about influenza A and actively engage in discussions on the internet regarding this topic. Their willingness to participate indicates a significant interest in the subject matter. Furthermore, Weibo classifies its users into 4 categories: blue V users, yellow V users, red V users, and regular users (the “V” label is akin to a verification symbol). Blue V users typically represent businesses or departments affiliated with certified institutions. These entities are required to undergo certification processes involving recognized organizations such as governments, businesses, schools, and media. Yellow V users, by contrast, are certified accounts belonging to renowned individuals in fields such as entertainment, sports, media, finance and economics, science and technology, literature and publishing, humanities and arts, games, military aviation, animation, tourism, and fashion, as well as government officials. Finally, red V users are certified accounts that achieve a minimum of 10 million monthly reads, granting them the red V certification. This distinction is a testament to the users’ popularity and influence on the platform.
Among all users, regular users make up the majority, accounting for 83% (7562/9111); following them are yellow V users at 10% (911/9111); blue V users at 5% (455/9111); while red V users comprise only 2% (183/9111). This observation implies that the topic of influenza A holds significant importance and captures the interest of the general public. The fact that ordinary users, who represent the majority of users on Weibo, display the greatest interest in this topic further emphasizes its relevance and the widespread concern among the public. It suggests that discussions and information related to influenza A are highly valued and sought after by ordinary individuals, highlighting the significance of this topic in the public discourse.
Influenza A Topic Analysis
Word frequency analysis.
The word frequency analysis, shown in Textbox 1 , is used to analyze the concerns that people have about influenza A. The textbox shows that the words “Covid-19,” “infection,” “virus,” “influenza,” “flu,” and “feeling” are the main focus of people’s attention. This indicates that people will compare influenza, fever, and COVID-19 when concerned about influenza A and that the symptoms after falling sick are the most important. In addition to the aforementioned words, words such as “hospital,” “mask,” and “vaccine” appear more frequently. This indicates that people are also very worried about the related protective and treatment measures and the distribution of medical resources when concerned about influenza A.
- Covid-19: 4445
- Infection: 3437
- Virus: 2946
- Influenza: 2447
- Fever: 2107
- Symptoms: 1843
- Hospitals: 1537
- Schools: 1324
- Student: 1324
- Children: 1273
- Vaccine: 1206
- Outbreak: 1122
- Feeling: 1003
- Health: 904
Hot Topic Analysis
In this study, the LDA topic model was used for topic mining. The hyperparameters α and β were set as symmetric Dirichlet priors with values of 50/T and .01, respectively. The number of iterations for Gibbs sampling was set to 100, and the document contribution threshold ε was set to 1/k. The LDA model plays a crucial role in determining the number of potential topics and assigning meaningful labels to these topics. We used perplexity values to identify the optimal number of topics for the LDA model.
To determine the optimal number of topics, we conducted experiments with topic values ranging from 1 to 25 and generated a consistency curve fit, as depicted in Figure 7 . On the basis of the results, 23 topics were identified as the most suitable for analysis. The right-hand section of Figure 8 illustrates the top 30 words with the highest frequency associated with each of the 23 topics.
From Figure 8 , it is evident that there is a high degree of overlap among topics 1, 5, and 8. Textbox 2 reveals that these topics share common keywords such as “school,” “students,” and “classes.” The topics discussed revolve around the suspension of classes at primary and secondary schools owing to a rise in influenza A cases. In addition, topics 7 and 21 also exhibit a significant overlap. Moreover, there is a substantial crossover between topics 7 and 21, as well as among topics 6, 9, and 17. In Figure 8 , the larger the circle in the left-hand section, the more critical the topic. Therefore, the blog posts with the highest attention paid to influenza A are topics 1, 2, 3, 4, 6, and 7. These highly discussed topics will be further analyzed.
Topics and content
- Covid-19, school, students, class, prevention, control, outbreak, command, primary school, virus, symptoms, children, testing, antigens, and precautions
- granules, Pfizer medicine, western medicine, clinical, price, efficacy, Covid-19, national, Pfizer, pharmaceuticals, pharmacy, treatment, protocol, drug, and patients
- pneumonia, epidemic, patient, patients, research, immunity, disease, symptoms, virus, situation, capsules, clinical, methods, traditional Chinese medicine, and time
- influenza, symptoms, influenza virus, oseltamivir, drugs, antiviral, population, general, high Incidence, virus, dosing, patients, video, taking, and influenza vaccine
- parents, students, Covid-19, positive, children, symptoms, virus, antigen, news, outbreak, school, class, test, virus infection, and elementary school
- virus, influenza, Covid-19, human, transmission, everyone, virus strain, medical, nucleic acid, variant, positive, data, avian influenza, general, and mortality
- company, pharmaceuticals, vaccines, limited company, pharmaceutical industry， national, work, Chinese Yuan, center, products, market, projects, production, sales, and hospitals
- school, student, Covid-19, class, influenza, infectious disease, outbreak, primary school, symptoms, education bureau, situation, part, oral disease, and parents
- vaccine, Covid-19, link, web page, biological, cell, protein, population, antibody, virus, variant, level, elderly, research, and antigen
- feeling, throat, symptoms, sore throat, runny nose, slight, headache, body, body aches all over, stuffy nose, snot, whole body, pharynx, dizziness, and taste
- hospital, viral, infection, influenza, patients, reporters, symptoms, disease, people, virus, situation, feelings, antigen, Covid-19, people’s daily, and nausea
- sickness, infection, home, friends, teacher, classmates, school, exam, record, dormitory, colleague, good night, infection to, and almost
- hospital, doctor, nucleic acid, test, check, outpatient, home, negative, symptoms, oseltamivir, community, queue, Covid-19 reinfection, daughter, and influenza
- mom, son, dad, sister, adult, world, child, brother, life, diarrhea, family members, school, family, medicine, and housemate
- body temperature, a little bit, all over the body, mental, state, bed, oseltamivir, hour, special medicine, antipyretic, day, affect, appetite, weakness, and aunt
- child, fever medicine, hour, cooling, wave influenza A, All, warm water, disinfection, temperature, physical, everyone, situation, parents, moisture, and children
- mask, month, personnel, ventilation, work, Covid-19, subtype, home, everyone, mobile, personal, time, first wave, and diligent hand washing
- Covid-19, video, influenza B, school, aftermath, news, experiences, experts, positive rate, prevention, help, national, infectiousness, large number, and events
- body, people, antigen, race, licorice, cold medicine, problem, situation, outrageous, advice, acute, magic, medicine, weight, ingredients, and pharyngitis
- symptoms, soreness, general, advice, whole body, food nourishment, muscle, nasal congestion, infection period, inflammation, healthy, sore throat, throat, runny, and stomach
- start school, infectious disease, basic, epidemic, family, spread, awareness, times, infectiousness, unable, probability, eyes, task, period, and science
- life, kids, roommates, resistance, exercise, experts, parent, programs, moms, professors, gym, nutrition, dad, chief, physician, good, and news
- kids, colleague, throat, influenza, vaccine, nose, infusion, go out, play, leader, thing, diary, neighborhood, oseltamivir, cake, care, and what
In topic 1, the keywords include “Covid-19,” “school,” “students,” “class,” “prevention and control,” “outbreak,” “command,” “primary school,” “virus,” “symptoms,” “children,” and “testing.” This topic focuses on the outbreak of influenza A in primary and secondary schools. Because of the gathering of people and the relative vulnerability of children, who are more susceptible to influenza A than adults, there is heightened societal concern about large-scale infections.
On social media platforms, individuals often express their concerns when their children contract influenza A. Parents who have not been infected themselves are worried about what preventive measures they can take against influenza A. These prevention efforts encompass a range of measures, including enhancing hygiene management and supervision at educational institutions; implementing disinfection and ventilation protocols; promptly identifying and isolating patients who have fallen ill and providing necessary treatment; raising awareness about protection measures among teachers, students, and parents; and reinforcing virus and antibody testing. Overall, the outbreak of influenza A at primary and secondary schools poses a significant public health challenge that necessitates collaborative efforts from the government, schools, parents, and the community to prevent and control its spread. Given the impact of the COVID-19 pandemic, there is heightened societal concern regarding mass infectious diseases, emphasizing the need for increased attention toward prevention and response to safeguard the health and safety of our children.
Compared with topic 1, topic 2 places more emphasis on the drugs used for combating influenza A, including their prices, efficacy, and the pharmaceutical manufacturers involved. Keywords associated with this topic include “Chinese medicine,” “Western medicine,” “clinical,” “treatment,” “Pfizer,” and “pharmacy.” The scarcity of drugs during the COVID-19 pandemic made it imperative to focus on drug-related aspects when addressing a new large-scale epidemic. Simultaneously, people want the pharmaceutical industry to develop specific drugs for contagious diseases, aiming to help individuals avoid illness. As is evident from the keywords, traditional Chinese medicine (TCM) holds a significant role in addressing the recent outbreaks of contagious diseases, garnering appreciation from the public. However, the issue of antibiotic misuse persists, particularly among patients with respiratory infections. Although various studies have been conducted to address the reduction of irrational antibiotic use, only a few have been multicenter or randomized trials. Exploring novel and innovative methods of administering medications is crucial to achieving the societal objectives of reducing irrational antibiotic use and eliminating unreasonable drug use. Therefore, providing education on the appropriate use of antibiotics during large-scale epidemic outbreaks is critical. Moreover, attention should be directed toward the drugs used to treat influenza A and their pricing to ensure that the public can access effective treatment promptly. This focus also aims to promote the research, development, and production efforts of pharmaceutical manufacturers in this field.
As depicted in Figure 8 , topic 3 exhibits overlap with topic 2, sharing common areas of focus such as clinical aspects, methodologies, and TCM. Topic 3 specifically concentrates on the rational use of medications for managing influenza A symptoms. In light of the recent outbreak of the novel coronavirus, there has been heightened interest in mass infectious diseases, leading to a deeper understanding of the influenza A virus. This includes comprehending the symptoms caused by the virus, the human immune system’s response, and making comparisons with the novel coronavirus. Furthermore, individuals are likely to express concerns regarding the transmission of the influenza A virus, the efficacy of herbal treatments, and available clinical treatment options. Consequently, the outbreak of the novel coronavirus has significantly elevated the public’s awareness and comprehension of epidemic infectious diseases.
The topic 4 keywords encompass “influenza,” “symptoms,” “influenza virus,” “oseltamivir “ “drugs,” “antiviral,” “population,” “general,” “high incidence,” “virus,” “dosing,” “patients,” “video,” “taking,” and “influenza vaccine,” highlighting the focus on influenza A itself. Simultaneously with rapid ecological changes, accelerated urbanization, the impact of influenza A, and increased risks associated with travel and globalization, epidemics are becoming more frequent, complex, and challenging to prevent and control. In recent years, the general public has become increasingly aware of the health implications of epidemics, as evidenced by the appearance of keywords such as “antiviral,” “high incidence,” and “influenza vaccine” in topic 4. In conclusion, effectively responding to large-scale infectious diseases such as influenza A necessitates collaborative efforts among the government, medical institutions, pharmaceutical manufacturers, academia, and the public. By enhancing public education, improving preventive measures, and promoting rational drug use, the incidence and transmission risks of epidemics can be reduced, thereby ensuring public health and safety. In addition, it is crucial to learn from the experiences of the COVID-19 pandemic, enhance the public’s awareness and understanding of mass infectious diseases, and drive continual improvement and progress in epidemic prevention and control.
Keywords for topic 6 include “virus,” “influenza,” “Covid-19,” “human,” “transmission,” “everyone,” “viral strain,” “medical,” “nucleic acid,” “variant,” “positive,” “data,” “avian influenza,” “general,” and “mortality.” The focus is on the discussion of viruses. Influenza A is an influenza virus that belongs to the family Orthomyxoviridae, a different family of viruses than the novel coronavirus. The virulence of the influenza A virus is relatively low, but it spreads quickly and is easily disseminated among the population. The main symptoms of influenza A include fever, cough, sore throat, muscle pain, fatigue, and headache, which usually appear within 2 to 3 days after infection. The mortality rate of the influenza A virus is low. However, it may cause more severe complications in specific populations, such as older adults, young children, pregnant women, and people with weakened immune systems. It is important to note that the influenza A virus and the novel coronavirus have different characteristics and impacts on public health. It is worth noting that topic 6 mentions comparisons with previous major infectious viruses when discussing influenza A viruses, including the ones responsible for COVID-19 and avian influenza.
Under topic 7, the keywords include “company,” “pharmaceuticals,” “vaccines,” “limited company,” “pharmaceutical industry,” “national,” “work,” “Chinese Yuan” “center,” “products,” “market,” and “projects.” This topic focuses on the public’s interest in pandemic vaccines. Influenza viruses are classified into 3 serotypes: A, B, and C. Type A has the potential to cause large-scale epidemics owing to the variation in the structure of its antigens, which occurs approximately once every 10 to 15 years. Type B epidemics are typically milder and more limited in scope, whereas type C generally causes milder epidemics. Humans are universally susceptible to all 3 types, and all 3 types can cause various respiratory conditions such as laryngitis, bronchitis, bronchiectasis, capillary bronchitis, and pneumonia.
In Figure 9 , the left-hand side represents the 4 provinces with the highest posting activity, whereas the right-hand side shows the number of posts corresponding to negative emotional themes. The research findings indicate that the topic of greatest concern among users is topic 12, which revolves around infections in schools. This is primarily because of the closure of schools after the influenza A outbreak, and the susceptibility of children in school environments to infection. The next topic of interest is topic 10, which includes keywords such as “feeling,” “throat,” “symptoms,” “sore throat,” “runny nose,” “slight headache,” “body aches over all,” “stuffy nose,” “snot,” “whole body,” “dizziness,” and “taste.” This topic pertains to postinfection symptoms because the symptoms associated with influenza A infections are prominent, leading individuals to experience physical and emotional distress, thereby contributing to more negative sentiment.
Influenza A Change Sentiment Orientation Analysis
Spatial difference analysis.
Emotional distribution can reflect the public’s attitude and sentiment toward relevant issues. In this paper, we categorized emotional orientation as positive or negative. According to our analysis, the public’s emotional exposure toward influenza is mainly negative, with negative emotions accounting for 83% (7562/9111), whereas positive emotions account for only 17% (1549/9111). This indicates that although the COVID-19 pandemic has brought many adverse effects, the public’s attitude toward influenza still needs to be more optimistic. We also analyzed the emotional orientation of different provinces toward influenza A. Figure 10 shows that each region holds a negative attitude toward influenza A, and there is little difference in the ratio of positive and negative emotions. We mainly focused on 3 regions—Qinghai, Yunnan, and Tibet—and found that they have a stronger negative emotional orientation than other sites.
The Factors Influencing Sentiment Orientation
On the basis of the study’s analysis, further exploration was conducted to understand the reasons behind positive and negative emotions among the public regarding influenza A. In the word frequency analysis concerning positive emotions, the following terms hold significance within the data set: “COVID-19” appears 1863 times, “influenza A” is documented 1768 times, “Infection” occurs 1596 times, “Virus” is mentioned 1033 times, “Influenza” is noted 1008 times, “Symptoms” is found 1001 times, “Control” is used 909 times, “Prevention” appears 854 times, “Outbreak” is mentioned 829 times, “Vaccine” occurs 818 times, “Fever” is referenced 719 times, “Children” is included 709 times, “Hospital” appears 706 times, “Malaise” is used 681 times, and “Classes” is seen 622 times. In addition to the high-frequency term “influenza A,” the public often discussed terms such as “prevention,” “control,” and “vaccine,” an indication of their concern and focus on influenza A. This suggests that the positive sentiment toward the influenza A epidemic primarily stems from effective prevention and control measures and the availability of a reliable vaccine. Furthermore, discussions about antiviral drugs and treatment reflect the public’s trust and expectation of scientific treatment options.
In the word frequency analysis pertaining to negative emotions, the following terms play a significant role in the data: “influenza A” appears 6069 times, “COVID-19” occurs 1729 times, “fever” is found 1373 times, “infection” appears 903 times, “myself” is present 820 times, “today” is mentioned 816 times, “influenza” is documented 778 times, “symptoms” is noted 759 times, “cold” is mentioned 730 times, “feeling” appears 721 times, “virus” occurs 679 times, “hospital” is seen 616 times, “uncomfortable” is used 599 times, and “child” is included 527 times. Finally, “cough” is listed 522 times. The common words associated with negative sentiment in blog posts, including “infection,” “fever,” and “symptoms” reflect the negative emotions stemming from public concern about the influenza A outbreak and the discomfort experienced by those who fall sick. In addition, inaccurate rumors and misunderstandings can contribute to negative emotions among the public. Therefore, it is crucial to disseminate scientific and accurate information while implementing timely epidemic prevention and control measures. These actions can effectively alleviate negative emotions, enhance public confidence and resilience, and collectively address the challenges posed by the influenza A epidemic.
The level of concern regarding the recently prevalent infectious disease, influenza A, has shown variations across Chinese provinces, influenced by the experience of the COVID-19 pandemic. Notably, the central region of China seems to display a heightened level of concern, whereas the northwest region exhibits a lower level of attention. This geographic disparity is reflected in both the total number of blog posts and the public’s attention to influenza A, demonstrating fluctuations over time. These fluctuations underscore the dynamic nature of public attention to infectious diseases and emphasize the necessity for region-specific communication strategies. Furthermore, the research findings suggest that individuals become more sensitized with regard to infectious diseases and exhibit increased levels of concern, especially in the face of the spread of a new infectious disease.
Spatiotemporal differences between blog posts and public attention.
From November 1, 2022, to March 31, 2023, there was an increase in the number of posts related to influenza A, indicating a growing concern among the public regarding this issue. Figure 6 illustrates that the number of posts is relatively lower in the western region and higher in the eastern part of the country, with a concentration in the central area. This pattern may be attributed to the higher population density and greater mobility in the eastern part, leading to a faster spread of influenza A and prompting more people to pay attention to the topic and discuss it. In addition, the central region, characterized by a more densely populated area, facilitates frequent information exchange among its residents, resulting in an increased number of posts on Weibo. Notably, Beijing has the highest number of posts among all provinces. This can be attributed to Beijing being a region with high population density and significant mobility, which may contribute to a faster spread of influenza A and generate more attention and discussion on the topic. Moreover, Beijing’s advanced internet infrastructure and the widespread adoption of social media platforms also contribute to the higher number of posts.
Difference Analysis of Influenza A Attention Among Different Genders and User Types
This study of the genders and types of users shows that female users are much more concerned about influenza A than men. This can be explained in several ways. First, women are more concerned about health and personal hygiene issues [ 24 ], which makes them more worried about the influenza A outbreak. Second, more women than men work in medical and nursing professions [ 25 ], which means that diseases such as influenza A are top of mind for them. This also contributes to their higher level of concern about influenza A. In addition, information about influenza A is usually more widely disseminated by women in the family [ 26 ]. Women typically play more active roles in the family as primary family caregivers, guardians of children, and so on [ 27 ]. Therefore, they are more likely to spread information about influenza A within the family. In addition, some studies show that women are better at expressing emotions and empathy [ 28 ]. Women’s risk perception ability is sharper when faced with a public health event [ 29 ], and they are more likely to pay attention to information about influenza A. From another perspective, there is a reason why there are many Weibo users with posts related to influenza A. First, influenza A is a prevalent infectious disease that can affect most people. Therefore, many people are concerned about information related to influenza A. Second, the symptoms of influenza A are similar to those of some common diseases, such as cold and influenza, which makes many people search for influenza A–related information when they have similar symptoms.
Building from previous studies that focus on influenza [ 30 - 32 ], this study highlights that the health topic of greatest public concern in China is influenza A and its characteristics. As a highly contagious disease, influenza A, which shares similarities with influenza, is known to be more painful than influenza and prone to severe complications, including death. Consequently, the public is eager to acquire more information about influenza A to safeguard their health and that of their families. Furthermore, both the novel coronavirus and the influenza A virus are respiratory viruses, prompting comparisons between the two. Consequently, understanding the differences and similarities between influenza A and COVID-19 can empower the public to comprehend both diseases better and adopt more effective preventive and control measures.
The next topic of interest is viruses. As influenza A is a virus-transmitted disease, it is essential to understand its virulence, symptoms, mortality rate, and transmission rate to understand the disease. This is also because the spread of the virus directly affects public health and social stability; therefore, naturally, the public is concerned about the virus.
Another topic is the public’s concern about preventing mass epidemic infectious diseases through the use of vaccines. The reasons for this are easy to understand. First, as vaccines are one of the most effective measures to prevent disease [ 33 , 34 ], the public began to pay more attention to the development of a vaccine in the hope that reliable preventive measures would be available early. Considering the role played by vaccines during the COVID-19 pandemic, we can see the importance of vaccines in controlling the spread of diseases and providing the public with effective measures to prevent the spread of epidemics. Second, public concern is also related to the safety and efficacy of vaccines [ 35 , 36 ] because vaccinating oneself is a significant decision involving everyone’s health and life. Finally, the public’s concern about epidemic vaccines is also related to health care systems and policies [ 36 , 37 ]. Vaccine development, production, and distribution require the support and regulation of health care systems and policies.
In addition to prevention, people are also concerned about the drugs used to treat influenza A, the price and efficacy of the drugs, and the drug manufacturers. This may be related to the COVID-19 outbreak. As the COVID-19 outbreak continues to pose a threat to people’s physical and mental health, there is still concern among the public about contracting the virus. In addition, people are also worried about the efficacy and side effects of antiviral medications and want to know details about their safety and applicability to make the proper treatment choice [ 38 ]. Pharmaceutical manufacturers have also become the focus of public attention because they are essential players in producing influenza A treatment drugs. Many TCM institutions and physicians actively responded during the COVID-19 pandemic and achieved some significant treatment results [ 39 - 41 ]. This also drew public attention to TCM’s role during the epidemic, and people increasingly value TCM; in fact, the treatment of influenza A by TCM has received much attention [ 42 - 45 ].
The next concern is the rational use of medication after contracting influenza A. This may be related to the COVID-19 outbreak, in the sense that the public is more concerned now about using the correct medications to relieve influenza A symptoms. In this context, the public is more concerned about using medications to relieve influenza A symptoms correctly. In addition, owing to the popularity of the internet and social media, public health awareness is gradually increasing, and people are more willing now to actively seek health information and treatment advice [ 46 - 48 ]. At the same time, the continuous advancement of medical technology has made the treatment methods for influenza A more and more diversified and precise, making the public more concerned about the rational use of medication to treat influenza A.
One fascinating topic was the influenza A outbreak in primary and secondary schools. This relates to the closure of primary and secondary schools in China during the COVID-19 pandemic when the government took several measures to prevent the spread of the disease. This resulted in students being unable to attend school, and many students began to study independently or receive distance learning at home. This situation has led to an increase in parents’ concerns about the safety and hygiene standards prevalent in schools and other educational institutions [ 49 - 51 ]. Besides, it is known that schools can become a source of mass infections among children.
Sentiment Orientation Analysis
Understanding public sentiment regarding the influenza A epidemic in light of the COVID-19 outbreak is crucial because it reflects public perceptions and attitudes toward health and disease, as well as their level of confidence and trust in outbreak prevention and control measures. The results of our study indicate that 83% (7357/9111) of Chinese individuals hold negative attitudes toward influenza A. These negative emotions are not primarily directed at the government or official institutions but rather stem from people’s psychological distress and anxiety regarding physical discomfort because influenza A can cause physical pain, fever, cough, and weakness, leading to individuals feeling unwell and physically burdened; in addition, given the recent experience of the COVID-19 pandemic, the emergence of influenza A exacerbates people’s psychological exhaustion and weariness.
At the same time, there are also positive emotions associated with confronting influenza A; for example, the efforts of the Chinese government in implementing various measures to address the influenza A outbreak, including vaccination programs, have helped the public to better cope with the outbreak and instilled confidence in the government’s response.
Data source limitations.
It is important to acknowledge the limitations of our data source. Weibo users, primarily composed of the younger demographic, may not provide a comprehensive representation of society as a whole. Furthermore, the attitudes and sentiments expressed on Weibo may not be entirely reflective of the broader societal attitude. It is crucial to recognize that Weibo users’ opinions may not necessarily encompass the perspectives of the entire community.
Spatiotemporal Analysis Constraints
Our study’s spatiotemporal analysis is subject to certain constraints. Specifically, we focused on analyzing people’s attitudes toward influenza A during a specific time frame after the COVID-19 pandemic. Unfortunately, we did not conduct a comparative analysis of attitudes before and during the COVID-19 pandemic. This limitation restricts our ability to provide insights into how the pandemic might have influenced changes in attitudes over time. In future research, comparing prepandemic and pandemic-era attitudes could yield valuable additional insights.
The COVID-19 pandemic has significantly increased public awareness of mass infections and the importance of preventive and control measures. In this context, this study on influenza A and its analysis of public sentiment provide valuable insights into the changing attitudes and concerns of the public. These findings can positively affect epidemic prevention and control efforts in the following ways.
Effective Communication Policies
Understanding public sentiment regarding the influenza A epidemic empowers the government and health organizations to devise communication policies tailored to the public’s perceptions and concerns. By addressing these, they can enhance the public’s comprehension of the outbreak and encourage the adoption of suitable protective measures. This proactive communication strategy plays a pivotal role in effectively curbing the spread of the epidemic; for instance, the government may implement a comprehensive communication plan, including daily updates on infection rates, guidelines for mask wearing, and information on vaccination centers, all designed to keep the public well informed.
Promoting Vigilance and Preventive Awareness
Positive public attitudes toward the influenza A epidemic can heighten public vigilance and awareness of preventive measures. A positive outlook encourages individuals to proactively engage in protective behaviors such as regular handwashing, consistent mask use, and avoidance of crowded areas. These actions reduce the risk of infection and contribute significantly to slowing down the transmission of the virus. To promote this, public health campaigns can emphasize the role of these behaviors in reducing transmission rates and saving lives.
Promptly Addressing Public Concerns
Understanding public attitudes and concerns about the influenza A epidemic equips health organizations and government authorities to promptly respond to public inquiries and address worries. By strengthening public information campaigns and educational initiatives focused on influenza A, they can bolster the public’s confidence and willingness to cooperate with recommended control and prevention measures; for example, they may establish hotlines or web-based forums where experts provide real-time answers to common questions, alleviating public concerns and building trust in official guidance.
Risk Assessment and Adaptive Policies
Negative public sentiment regarding influenza A in China indicates the necessity for a comprehensive risk assessment. Understanding public opinion allows health organizations and government entities to swiftly adapt prevention and control measures. This includes the development of targeted policies and guidelines that align with the evolving public sentiment; for instance, if negative sentiment arises owing to perceived vaccine shortages, authorities can swiftly adjust vaccine distribution strategies and communicate these changes transparently to rebuild trust.
Enhancing Management Capacity and Public Cooperation
A profound understanding of public sentiment helps enhance the management capacity of health organizations and government bodies. It strengthens communication channels and cooperation with the public, fostering a more robust social collaboration mechanism. This, in turn, facilitates the seamless implementation of epidemic prevention and control measures; for example, regular public engagement forums can be established, allowing citizens to voice concerns and provide input into decision-making processes, ultimately leading to more effective and inclusive policies.
By actively considering public sentiment, health organizations and the government can not only engage the public more effectively but also tailor their strategies and policies to better address the challenges presented by the influenza A epidemic.
This study was supported by the National Natural Science Foundation of China (71764014).
The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.
JD and FL contributed equally to this work. JD acquired funding, provided a mock peer review, and supervised the investigation. FL analyzed the data and wrote the original draft. LY performed a mock peer review and helped write the Discussion section. YH provided a mock peer review and supervised the investigation.
Conflicts of Interest
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Edited by A Mavragani; submitted 24.05.23; peer-reviewed by R Gore, P Brzustewicz; comments to author 01.09.23; revised version received 20.09.23; accepted 11.10.23; published 02.11.23
©Jing Dai, Fang Lyu, Lin Yu, Yunyu He. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.11.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
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Change requires change. If we want to dramatically change how fast students learn, schools need to dramatically change schooling.”
Jonathan Guryan and Jens Ludwig Co-Directors of the University of Chicago Education Lab
The federal funds allotted to school districts to combat pandemic-induced learning loss will end in less than a year, but the U.S. is stalled on progress to reverse the once-a-century education crisis.
Remote learning and chronic absenteeism between 2019 and 2022 led students to lose an average of three quarters of a year of schooling, and disadvantaged children, who experienced existing disparities the pandemic only exacerbated, fell even further behind.
In a new paper and an op-ed in the Hill, IPR economist Jonathan Guryan and University of Chicago economist Jens Ludwig seek to understand why such little progress has been made on overcoming the substantial learning losses for most of America’s K–12 students—and they propose solutions to move forward.
Compared to before the pandemic, students in most grades showed slower growth in math and reading, and most states reported troubling setbacks in math scores last year for almost all demographic groups. Because education is cumulative, Guryan and Ludwig, co-directors of the University of Chicago Education Lab , state that the losses could have permanent effects if not soon reversed. And if they are not, they may set a whole generation of students off track for the rest of their lives.
As educators grappled with the sudden switch to remote learning in March 2020, Congress sent $189.5 billion to schools through the Elementary and Secondary School Emergency Relief (ESSER) Fund through March 2021. While some of the funding was used to replace lost tax revenue, schools also had to set aside at least 20% for evidence-based interventions to address learning loss, such as tutoring.
“High-dosage” tutoring, an intervention both authors have studied previously, is proven to double, or even triple, the amount a student learns in a year. The individualized, intensive, and in-school tutoring intervention, which costs $3,500 to $4,300 per student per year, was developed by Saga Education, a nonprofit organization. In the program, a tutor works with two students at a time for a full class period every school day.
Many school districts have struggled to implement high-dosage tutoring in ways that are most effective. Some schools have provided tutoring after school or even virtually with students at home, but Guryan and Ludwig find this to be ineffective. Tutoring is the most effective when it’s integrated into the daily curriculum, the researchers find, but it’s difficult for schools to carve out the time during the day to devote to it, and they often lack the funding. The researchers find that high-dosage tutoring, or what they refer to as the closest thing we have to a “COVID learning loss” vaccine, is the most effective, but requires radical changes to the traditional school-day structure.
“Change requires change. If we want to dramatically change how fast students learn, schools need to dramatically change schooling,” Guryan and Ludwig wrote in the op-ed. “Unfortunately, the federal government gave school districts too little time, and too little money, to address the scale of the learning loss problem.”
To get students to participate, the researchers say tutoring must be done during the school day, and effective tutoring requires a structured curriculum to help students learn content they don’t know that’s below their current grade level. Schools also typically assume that successfully teaching children requires extensive prior experience and training, but Saga’s use of paraprofessionals instead of teachers is also effective and costs less, the researchers explain. They acknowledge that the changes needed to dramatically accelerate student learning are hard but must be done to avoid the alternative.
While the schools need to be willing to implement change, they also need the funds to make it possible. The authors call on Congress to extend funding and avoid “squandering the potential of an entire generation of 50 million students,” as well as possibly increasing economic inequality due to worse learning losses for students of color and those from lower-income households. Policymakers also should provide additional resources beyond the initial funding, and push schools to take those difficult steps.
“The failure to give schools more time and money would be the equivalent of calling it quits on overcoming pandemic learning loss,” Guryan and Ludwig wrote. “That would be like quitting a race just when you get to the starting line.”
Read the paper from the Aspen Strategy Consulting Group.
Jonathan Guryan is the Lawyer Taylor Professor of Education and Social Policy and an IPR fellow. Jens Ludwig is the Edwin A. and Betty L. Bergman Distinguished Service Professor at the University of Chicago. They are co-directors of the Education Lab .
Photo credit: iStock
Published: November 1, 2023.