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Participatory Action Research in a Time of COVID and Beyond

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This Research Topic aims to pool different approaches, experiences, and resources for facilitating Participatory Action Research (PAR) by practitioners working in a range of country and cultural contexts. For scholar-activists or researcher-practitioners researching in rural areas, whether using participatory ...

Keywords : remote research, sustainable food systems, collaborative knowledge co-production, transformative social change

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Vol. 25 No. 3: Special Issue: Community Engagement in the COVID-19 Reality

Impact of COVID-19 on a Participatory Action Research Project: Group Level Assessments with Undergraduate Women in Engineering

• Batsheva Guy  ▸  ▾
• Brittany Arthur

As participatory action researchers during the COVID-19 pandemic, we struggle with maintaining meaningful collaboration with our community partners while navigating social distancing guidelines. For the past several years, we have been working with undergraduate women in engineering at a large, public, mid-western research university to assess their experiences on campus and during their co-op rotations in order to influence equitable programming and inclusive practices at our institution. We have been primarily using Group-Level Assessment, a qualitative, participatory research method that is rooted in inclusivity, stakeholder engagement, and instigating actionable change. When our university went remote, we were faced with the challenge of transitioning our community research partnership online and continuing to use our chosen method. The current article compares participant experiences in both an in-person and remote environment, in order to assess the effectiveness of moving our participatory research practices to an online platform. Findings indicated that while both in-person and virtual Group-Level Assessments allowed participants to better understand others’ experiences and allowed their voices to be heard, the in-person method was more engaging. However, the virtual method allowed for more time to do action planning.

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Volume 26, Number 8—August 2020

Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease

Cite This Article

By April 2, 2020, >1 million persons worldwide were infected with severe acute respiratory syndrome coronavirus 2. We used a mathematical model to investigate the effectiveness of social distancing interventions in a mid-sized city. Interventions reduced contacts of adults > 60 years of age, adults 20–59 years of age, and children < 19 years of age for 6 weeks. Our results suggest interventions started earlier in the epidemic delay the epidemic curve and interventions started later flatten the epidemic curve. We noted that, while social distancing interventions were in place, most new cases, hospitalizations, and deaths were averted, even with modest reductions in contact among adults. However, when interventions ended, the epidemic rebounded. Our models suggest that social distancing can provide crucial time to increase healthcare capacity but must occur in conjunction with testing and contact tracing of all suspected cases to mitigate virus transmission.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, in December 2019 ( 1 ), and in March 2020, the World Health Organization declared coronavirus disease (COVID-19) a pandemic ( 2 ). By April 2, 2020, COVID-19 had spread to >181 countries worldwide, and >1 million confirmed cases of COVID-19 and >50,000 deaths had been reported globally ( 3 ).

On January 21, 2020, the first case of COVID-19 in the United States was identified in a traveler who had recently returned to Washington from Wuhan ( 4 , 5 ). By March 14, Washington had reported 642 confirmed cases and 40 deaths associated with COVID-19 ( 6 ). In response to the rapid spread of the virus, on March 12, 2020, approximately 7 weeks after the first confirmed case in the state, the governor of Washington announced a set of interventions in 3 counties ( 7 , 8 ). More stringent prohibitions were soon imposed, followed by a shelter-in-place order lasting > 6 weeks beginning on March 25, 2020 ( 9 ). Similar interventions have been enacted in other US states and in countries in Europe ( 10 , 11 , 12 ).

We used an epidemic mathematical model to quantify the effectiveness of social distancing interventions in a medium-sized city in the United States or Europe by using Seattle, Washington, as an example. We provide estimates for the proportion of cases, hospitalizations, and deaths averted in the short term and identify key challenges in evaluating the effectiveness of these interventions.

Figure 1 . Mathematical model illustrating study population divided into 10 age groups and stratified as susceptible ( S ), exposed ( E ), infectious ( I ), and removed ( R ) from coronavirus disease epidemic. Susceptible persons become exposed at...

We developed an age-structured susceptible-exposed-infectious-removed model to describe the transmission of SARS-CoV-2 ( Appendix ). We divided the population into 10 age groups: 0–5, 6–9, 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, and > 80 years of age. We calibrated the model to the age distribution of the population of the Seattle metropolitan area by using data from the US Census Bureau ( 13 ). For each age group, we divided the population into compartments: susceptible ( S ) for persons who could be infected; exposed ( E ) for persons who have been exposed but are not yet infectious; infectious ( I ); and removed ( R ) for persons who have recovered or died ( Table ; Figure 1 ). We only considered symptomatic infections on the basis of estimates that <1% of infections are asymptomatic ( 15 ). We assumed only 20% of the cases would be identified because 80% of cases are reported to be mild and would probably be undocumented ( 16 , 17 ). We used previously reported case-fatality and hospitalization rates by age group ( 16 , 18 ). We used the contact matrix for 6 age groups computed by Wallinga et al. ( 19 ) and extended it to 10 age groups ( Appendix ).

We used January 21, 2020, the day the first case was identified in Washington, as the first day of our simulation on the basis of the analysis by T. Bedford ( 20 ). By using genomic epidemiology of the first 2 COVID-19 cases identified in Washington, Bedford found that SARS-CoV-2 had been circulating locally for 6 weeks before the second case was identified in the state ( 20 ).

We modeled social distancing by reducing the contact rates in an age group for 6 weeks, corresponding to the policy in Washington in mid-March ( 7 , 8 , 21 ). We divided the population into 3 major groups for social distancing interventions: children, persons < 19 years of age; adults 20–59 years of age; and adults > 60 years of age.

We investigated the effectiveness of 4 scenarios of social distancing. The first was distancing only for adults > 60 years of age, in which contacts for this group were reduced by 95%. The rationale for this scenario is that older adults are at highest risk for hospitalization and death and should have the most drastic restrictions in their contacts. Similar policies were implemented in early April in some countries, such as Sweden ( 22 ). In the second scenario, adults > 60 years of age would reduce social contacts by 95% and children would reduce contacts by 85%, assuming that most of the contacts of children occur at school and would be reduced due to school closures. This scenario corresponds to an intervention in which the high-risk group is fully protected. In addition, it reduces the contact rates for children, who are known to be a major part of the chain of transmission for other respiratory infectious diseases. Research indicates that children are infected with SARS-CoV-2 as often as adults (Q. Bi, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.03.20028423v3 ) but seem to have much milder symptoms ( 23 ). At this point, whether their infectiousness also is reduced is unclear. In the third scenario, adults > 60 years of age reduce contacts by 95% and adults <60 years of age reduce contacts by 25%, 75%, or 95%. This scenario corresponds to a policy in which high-risk age groups still are protected and younger adults are somewhat restricted in their contacts. However, persons in essential businesses can continue working and children can resume school, which is crucial considering school closures have been shown to have an adverse effect on the economy ( 24 ). In the fourth scenario, contacts are reduced for every group; adults > 60 years of age reduce contacts by 95%, children by 85%, and adults <60 years of age by 25%, 75%, or 95%. This scenario represents many interventions currently in place throughout the world.

To quantify the uncertainty around our results, we performed 1,000 simulations varying 3 parameters: the basic reproduction number (R 0 ), the latent period, and the duration of infectiousness ( Appendix ). For each statistic in the results, we computed the error bars by removing the top and bottom 2.5% of the simulations.

Estimates for the duration of infectiousness for SARS-CoV-2 range from 5 to 20 days ( 25 ; Q. Bi, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.03.20028423v3 ). Therefore, we analyzed the influence of the duration of infectiousness on the effectiveness of the social distancing interventions. We kept all other parameters fixed but considered an epidemic with infectious periods of 5, 6, 7, or 8 days, which correspond to the most plausible values ( 25 ; Q. Bi, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.03.20028423v3 ).

Figure 2 . Number of ascertained coronavirus disease cases over 180 days (identified cases over time calculated by mathematical model) using varying infectious periods: A) 5 days; B) 6 days; C) 7 days; D)...

In our model, when the infectious period was set to a shorter time of 5 days, the epidemic peaked at 100 days after the introduction of the first case. As we extended the infectious period, the epidemic took much longer to take off ( Figure 2 ) because we kept a fixed R 0 , so that a longer infectious period implied a smaller infectious rate. When we used the longest infectious period of 8 days, we noted the epidemic peaked 128 days after the first case was introduced. Therefore, early interventions delay the epidemic but do not substantially change the pool of susceptible persons, which allows similar-sized epidemics to occur later ( Figure 2 ).

We then considered the delay of the epidemic under the 4 social distancing interventions and different infectious periods ( Figure 2 ). As expected, the fourth social distancing strategy, the one applied to all age groups, delayed the epidemic the longest, >50 days, compared with a baseline of using no interventions. Targeting adults > 60 years of age and children delayed the epidemic by ≈10 days, regardless of infectious period. Targeting adults <60 and > 60 years of age delayed the epidemic by 41 days when we set the infectious period to 8 days and delayed it 39 days when we set the infectious period to 5 days. Social distancing of only adults > 60 years of age only delayed the epidemic by 2 days, regardless of infectious period ( Appendix Table 1). The infectious period did not substantially affect the peak epidemic height compared with baseline.

Figure 3 . Number of ascertained coronavirus disease (identified cases over time calculated by mathematical model) with adults reducing their contact by 25% (A, B); 75% (C, D); and 95% (E, F). We used...

We examined the effectiveness of the 3 social distancing interventions in adults and the timeframe in which interventions began. We considered social distancing interventions starting 50 days ( Figure 3 , panels A, C, E) and 80 days ( Figure 3 , panels B, D, F) after the first case was identified and reduction in adult contacts by 25% ( Figure 3 , panels A, B), 75% ( Figure 3 , panels C, D), and 95% ( Figure 3 , panels E, F). We found that the effect of interventions was dramatically different when started early in the epidemic curve, before the exponential phase of the epidemic, rather than later.

When we started interventions on day 50, we saw a delay in the epidemic regardless of the level of reductions in contact in the adult population, with little change in the magnitude of the epidemic peak. In comparison, when we began the interventions later, during the exponential phase of the epidemic, all interventions flattened epidemic curve. The strategy of reducing the contacts only of adults > 60 years of age resulted in a moderate reduction of 5,000 (21%) fewer cases at the epidemic peak compared with baseline. Limiting contact for adults > 60 years of age, as expected, is the only intervention for which there was minimal rebound after the intervention was lifted ( Figure 3 , panels B, D, F) because older adults make up only 16% of the population and have substantially fewer contacts than the other age groups.

We found that the strategy targeting adults > 60 years of age and children resulted in 10,500 (45%) fewer cases than baseline at the epidemic peak ( Figure 3 , panels B, D, F), emphasizing the fact that children are the age group with the highest number of contacts in our model. By comparison, when we applied the adults-only strategy, we saw 11,000 (47%) fewer cases than baseline at the epidemic peak for 25% reduction in contacts in adults <60 years of age ( Figure 3 , panel B). When we reduced contact by 75% in this age group, the peak epidemic cases dropped by 21,000 (91%). When we reduced contact by 95% in this age group, we noted 22,500 (98%) fewer cases ( Figure 3 , panels D, F), and the epidemic curve grew at a slower rate in both instances. Of the 4 intervention scenarios, the strategy involving all age-groups decreased the epidemic peak the most and showed the slowest growth rate, which we expected because contacts in all age groups are reduced. Even when we used a lower reduction in contacts of 25% in adults <60 years of age, we noted 16,000 (69%) fewer cases at the epidemic peak ( Figure 3 , panel B). With higher reduction in contacts (95%) in adults <60 years of age, the strategy involving all age groups mitigated nearly all cases at the epidemic peak ( Figure 3 , panel F). However, our results suggest that all interventions can result in new epidemic curves once the interventions are lifted.

Figure 4 . Proportion of coronavirus disease cases, hospitalizations, and deaths averted during 100 days for various social distancing scenarios in which adults reduce their contact by 25% (A–C); 75% (D–F); and 95% (G–I)....

Next, we considered the effects of social distancing interventions over the first 100 days of the epidemic and assumed that the social distancing interventions started on day 50, which corresponds to the approximate date when social distancing interventions started in Washington. To investigate the sensitivity of the model to the chosen parameters, we ran 1,000 simulations ( Appendix ). We obtained curves that varied widely for both the number of cases and the duration and timing of the epidemic ( Appendix Figures 1–3). We ran simulations with the mean parameter values (R 0 = 3, an infectious period lasting 5 days, and a latent period of 5.1 days). We then observed the number of cases and proportion of cases, hospitalizations, and deaths averted during the first 100 days. We noted that reducing the contacts of adults > 60 years of age averted only 18% of cases for the whole population ( Figure 4 ) but averted 50% of cases for this age group ( Appendix Figure 4). In addition, this intervention reduced the overall number of hospitalizations by 30% and reduced deaths by 39% for the whole population ( Figure 4 ) and hospitalizations and deaths by > 49% for the adults > 60 years of age ( Appendix Figures 5, 6). Adding a social distancing intervention in children slowed the epidemic curve ( Figure 3 ) and reduced the overall hospitalizations by > 64% (Figure 4) and by > 53% across all age groups ( Appendix Figures 5, 6).

When only 25% of adults <60 years of age changed their contact habits, all interventions rebounded as soon as the intervention was lifted ( Figure 3 , panel A). Surprisingly, cases, and hence hospitalizations and deaths, can be reduced by 90% during the first 100 days if all groups reduce their contacts with others, even when adults do so by only 25% ( Figure 4 , panel A). In this scenario, the reduction in the number of cases, hospitalizations, and deaths was evenly distributed across all age groups ( Appendix Figure 4 , panel A, Figure 5, panel A, Figure 6, panel A). When adults <60 years of age reduced contacts by 75%, cases, hospitalizations, and deaths rebounded immediately after the end of the intervention, except in the intervention in which contact was reduced for all groups ( Figure 3 , panel C). As expected, adult groups had the greatest reductions in cases, hospitalizations, and deaths from this intervention ( Appendix Figure 5, panel B, Figure 6, panel B). When adults <60 years of age reduced contacts by > 75%, the strategies that reduced the contacts of adults only and that reduced the contacts of everyone averted > 98% of cases, hospitalizations and deaths during the first 100 days ( Figure 4 , panels E, F). Further, when we reduced the contact rate of adults by > 75%, the strategy targeting all adults and the strategy targeting everyone mitigated the outbreak ( Figure 3 , panels C, E; Figure 4 ; Appendix Figure 4, panels B, C, Figure 5, panels B, C, Figure 6, panels B, C). However, our model suggests that the epidemic would rebound even in these scenarios. Of note, the error bars were much larger when adults reduced their contact rates by 25%, and this uncertainty tended to smooth out as the adults further reduced their contact rates.

The term “flatten the curve,” originating from the Centers for Disease Control and Prevention ( 26 ), has been used widely to describe the effects of social distancing interventions. Our results highlight how the timing of social distancing interventions can affect the epidemic curve. In our model, interventions put in place and lifted early in the epidemic only delayed the epidemic and did not flatten the epidemic curve. When an intervention was put in place later, we noted a flattening of the epidemic curve. Our results showed that the effectiveness of the intervention will depend on the ratio of susceptible, infected, and recovered persons in the population at the beginning of the intervention. Therefore, an accurate estimate of the number of current and recovered cases is crucial for evaluating possible interventions. As of April 2, 2020, the United States had performed 3,825 tests for SARS-CoV-2 per 1 million population ( 27 ). By comparison, Italy had performed 9,829 tests/1 million population ( 27 ). Expanding testing capabilities in all affected countries is critical to slowing and controlling the pandemic.

Some evidence suggests that persons who recover from COVID-19 will develop immunity to SARS-CoV-2 ( 28 ). However, at this point the duration of immunity is unclear. If immunity lasts longer than the outbreak, then waning immunity will not affect the dynamics of the epidemic. Furthermore, persons who recover from COVID-19 could re-enter the workforce and help care for the most vulnerable groups. However, if immunity is short-lived, for instance on the order of weeks, persons who recover could become re-infected, and extensions to social distancing interventions might be necessary.

We used a mathematical model to quantify short-term effectiveness of social distancing interventions by measuring the number of cases, hospitalizations, and deaths averted during the first 100 days under 4 social distancing intervention scenarios and assuming different levels of reduction in the contacts of the adult population. When we investigated the short-term effects of social distancing interventions started early in the epidemic, our models suggest that the intervention involving all age groups would consistently decrease the number of cases considerably and delay the epidemic the most. Of note, with > 25% reduction in contact rates for the adult population, combined with 95% reduction in older adults, the number of hospitalizations and deaths could be reduced by >78% during the first 100 days, a finding that agrees with previous reports ( 29 , 30 ).

Our results must be interpreted with caution. Hospitalizations and deaths averted during the first 100 days in our model would likely occur later if the interventions are lifted without taking any further action, such as widespread testing, self-isolation of infected persons, and contact tracing. As in any model, our assumptions could overestimate the effect of the interventions. However, quantifying the short-term effects of an intervention is vital to help decision makers estimate the immediate number of resources needed and plan for future interventions.

Our simulations suggest that even in the more optimistic scenario in which all age groups reduce their contact rates by > 85%, the epidemic is set to rebound once the social distancing interventions are lifted. Our results suggest that social distancing interventions can give communities vital time to strengthen healthcare systems and restock medical supplies, but these interventions, if lifted too quickly, will fail to mitigate the current pandemic. Other modeling results also have suggested that extended periods of social distancing would be needed to control transmission ( 18 ). However, sustaining social distancing interventions over several months might not be feasible economically and socially. Therefore, a combination of social distancing interventions, testing, isolation, and contact tracing of new cases is needed to suppress transmission of SARS-CoV-2 ( 31 , 32 ). In addition, these interventions must happen in synchrony around the world because a new imported case could spark a new outbreak in any given region.

Our results suggest that the SARS-CoV-2 infectious period has an extraordinary influence in the modeled speed of an epidemic and in the effectiveness of the interventions considered. However, current estimates of the infectious period range from 5 to 20 days ( 25 ; Q. Bi, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.03.20028423v3 ). Of note, all estimates of the infectious period were made on the basis of PCR positivity, which does not necessarily translate to viability or infectivity of the virus ( 33 ). We urgently need studies to definitively define the duration of infectiousness of SARS-CoV-2.

Our work has several limitations and should be interpreted accordingly. First, deterministic mathematical models tend to overestimate the final size of an epidemic. Further, deterministic models always will predict a rebound in the epidemic once the intervention is lifted if the number of exposed or infectious persons is >0. To avoid that problem, we forced our infected compartments to 0 if they had <1 person infected at any given time. Second, we considered the latent period to be equal to the incubation period, but others have suggested that presymptomatic transmission is occurring (L. Tindale, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.03.20029983v1 ) and SARS-CoV-2 is shed for a prolonged time after symptoms end ( 34 ). Whether virus shed by convalescent persons can infect others currently is unclear. Further, we considered that mild and severe cases would be equally infectious and our model could be overestimating the total number of infections, which would amplify the effect of social distancing interventions. We also considered infected children and adults to be equally infectious, and our model could be overestimating the effect of social distancing in persons < 19 years of age. Strong evidence suggests that children have milder COVID-19 symptoms than adults and might be less infectious ( 23 ). More studies are needed clarify the role children play in SARS-CoV-2 transmission. In our models, we assumed death and hospitalization rates would be similar to those experienced in Wuhan, where older age groups have been the most affected. Because different countries have different population structures and different healthcare infrastructure, including varying numbers of hospital beds, ventilators, and intensive care unit beds, effects of social distancing interventions could vary widely in different places.

Our results align with an increasing number of publications estimating the effects of interventions against COVID-19. Several researchers have investigated how social distancing interventions in Wuhan might have affected the trajectory of the outbreak ( 30 , 35 , 36 ; J. Zhang, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.19.20039107v1 ). Others have investigated the effect of similar measures elsewhere and concluded that social distancing interventions alone will not be able to control the pandemic ( 37 , 38 ; M.A. Acuña-Zegarra, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.28.20046276v1 ; N.G. Davies, unpub. data, medrxiv.org/content/10.1101/2020.04.01.20049908v1 ; S. Kissler, unpub. data, https://www.medrxiv.org/content/10.1101/2020.03.22.20041079v1 ).

Taken together, our results suggest that more aggressive approaches should be taken to mitigate the transmission of SARS-CoV-2. Social distancing interventions need to occur in tandem with testing and contact tracing to minimize the burden of COVID-19. New information about the epidemiologic characteristics of SARS-CoV-2 continues to arise. Incorporating such information into mathematical models such as ours is key to providing public health officials with the best tools to make decisions in uncertain times.

Dr. Matrajt is a research associate at the Fred Hutchinson Cancer Research Center. Her research interests include using quantitative tools to understand infectious disease dynamics and to optimize public health interventions.

Dr. Leung is a postdoctoral research fellow at the Fred Hutchinson Cancer Research Center. Her research interests include using mathematics to understand infectious disease transmission.

Acknowledgments

This article was preprinted at https://www.medrxiv.org/content/10.1101/2020.03.27.20044891v2 .

We thank Dobromir Dimitrov for helpful discussions. We also thank the team in the Biostatistics, Bioinformatics & Epidemiology Program at the Fred Hutchinson Cancer Research Center for supporting this work.

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• Weiss  P , Murdoch  DR . Clinical course and mortality risk of severe COVID-19. Lancet . 2020 ; 395 : 1014 – 5 . DOI PubMed Google Scholar
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• Figure 1 . Mathematical model illustrating study population divided into 10 age groups and stratified as susceptible (S), exposed (E), infectious (I), and removed (R) from coronavirus disease epidemic. Susceptible persons become...
• Figure 2 . Number of ascertained coronavirus disease cases over 180 days (identified cases over time calculated by mathematical model) using varying infectious periods: A) 5 days; B) 6 days; C) 7...
• Figure 3 . Number of ascertained coronavirus disease (identified cases over time calculated by mathematical model) with adults reducing their contact by 25% (A, B); 75% (C, D); and 95% (E, F)....
• Figure 4 . Proportion of coronavirus disease cases, hospitalizations, and deaths averted during 100 days for various social distancing scenarios in which adults reduce their contact by 25% (A–C); 75% (D–F); and...
• Table . Description of parameters used in the susceptible-exposed-infectious-removed mathematical model for evaluating the effectiveness of social distancing interventions on coronavirus disease

DOI: 10.3201/eid2608.201093

Original Publication Date: April 21, 2020

Table of Contents – Volume 26, Number 8—August 2020

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Spreading the word about virology research in an age of misinformation

A virus of interest this year: H5N1 bird flu, which has been causing outbreaks in poultry and U.S. dairy cows. Photo: Adobe Stock

Article by: Emily Caldwell

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The American Society for Virology ’s 43rd annual meeting hosted by The Ohio State University this week features some firsts for the organization: It is the largest meeting ever, attracting over 2,000 attendees from 50 countries, and the keynote speaker was a social scientist – rather than a specialist in a biological or medical field.

Adam Berinsky , the Mitsui Professor of Political Science at MIT, opened the meeting Monday night with a lecture about misinformation – and ways to combat it. He has studied the problem in the context of politics, but the work is easily adapted to virology in an age of heightened anti-science sentiment and lack of trust in experts of all kinds.

ASV President Anne Moscona, who selected the keynote speaker, urged audience members to consider working alongside Berinsky to address misinformation that casts doubt on scientific evidence, which she called “a problem so worrisome to our community.”

Ohio State expertise

Shan-Lu Liu , a virology professor at Ohio State and co-director of the Viruses and Emerging Pathogens Program of the Infectious Diseases Institute , chaired the meeting’s organizing committee. Liu, a longtime HIV researcher who more recently has led numerous studies of SARS-CoV-2 variants’ immune evasion, transmissibility and infectivity , has been named president-elect of ASV at this year’s meeting and will become president of the society in 2025.

The conference blends symposium presentations by leaders in a range of virology disciplines with poster sessions and dozens of trainee workshops on science, career development and communication.

Linda Saif , Distinguished University Professor in Ohio State’s Center for Food Animal Health with faculty appointments in animal sciences and veterinary preventive medicine, opened Wednesday morning’s symposium with detailed documentation of coronaviruses’ ability to spill over from animals to humans – which has been happening for centuries. An international expert on coronaviruses, Saif described the wily strains as diverse infectious agents that are skilled at accommodating change thanks their large RNA genome.

Like the coronavirus that leapt from wild animals to cattle and from cattle to poultry that Saif’s lab was the first to document in 1995, the virus that causes COVID-19 is known to infect mink, hamsters, cats and white-tailed deer. She’s among a team of researchers who found that white-tailed deer were infected by humans with SARS-CoV-2 across Ohio and that the animals function as reservoirs promoting ongoing viral mutation.

“If we end up with a wildlife reservoir, will it persist, adapt and mutate in wildlife with new variants going back to humans, livestock or other animals?” Saif said. “The concern is, will we end up with a scenario with SARS-CoV-2 where we have two-way transmission to wildlife species and back into humans?”

The World Health Organization has predicted the next pandemic will be caused by what it calls “Disease X” – and some coronaviruses currently circulating in animals are potential candidates, she said.

The studies detecting SARS-CoV-2 in Ohio deer were co-led by Andrew Bowman , professor of veterinary preventive medicine at Ohio State, who presented an update on that research program at the meeting. Bowman has also led influenza surveillance at the swine-human interface in commercial settings and agricultural fairs for years, and recently made news by finding H5N1 (bird flu) viral RNA in over a third of commercial milk products he and a graduate student bought in the Midwest based on a hunch that viruses detected in dairy cows were making their way to grocery stores.

The annual conference presents “an important opportunity to educate the public about virology research,” said Liu, also associate director of the Center for Retrovirus Research and professor of veterinary biosciences . “And we expect that attendees will also benefit from Adam Berinsky’s insights about the general phenomenon of misinformation.”

Fighting misinformation

Misinformation that circulated during the COVID-19 pandemic lockdown – conspiracy theories that the illness’ severity was exaggerated or that the virus was deliberately released for sinister reasons – persists to this day. Berinsky, who has extensively studied Americans’ beliefs in false political rumors (such as claims Barack Obama wasn’t born in the United States), has found that rumors are “sticky” – countering them is not just about sharing the facts, but requires a messenger that people will be willing to trust.

His research has found that about 70% of people will endorse at least one false rumor when given a list to choose from and only 30% of people reject them all, and that misinformation correction should focus primarily on reaching the undecideds – people who aren’t sure about what’s true. His work has also shown that correcting misinformation is not a “one-and-done” endeavor – it requires repeated messaging as well as a series of strategies targeting people who share fake information online.

Working with Facebook and Google, Berinsky and colleagues advanced interventions encouraging people through “accuracy nudges” to think before they share information – advising users to evaluate information before sharing it and investigate its sources – or even asking them how important it is to them to share only accurate information.

“Most people don’t want to share bad information,” he said. “We want to remind them of this.”

They found in experiments that at the individual level, these nudges turned the knob toward more sharing of true information. Though the prompts won’t stop people who are posting lies maliciously, the added “friction” makes it a little more difficult for casual users to do an easy thing that has negative effects.

A megastudy of nine interventions designed to reduce online sharing of misinformation revealed that all of them work – but each one’s effect is relatively small, on the order of 2 to 3 percentage points. Hence, the frame of mind in combating misinformation should not be a search for a single solution, but deployment of the best bundle of solutions depending on the context.

“There’s been this long search for a magic bullet. It doesn’t exist,” Berinsky said. “But don’t give up on the enterprise.”

Reopening K-12 Schools During the COVID-19 Pandemic: Prioritizing Health, Equity, and Communities (2020)

Chapter: 6 recommendations and urgent research, 6 recommendations and urgent research.

Whether to reopen school buildings for the 2020–2021 school year is one of the most consequential and complex decisions many education leaders will ever have to make. While the benefits of reopening for students, families, and communities are clear, leaders must also take into account the health risks to school personnel and students’ families, as well as the practicality and cost of the mitigation strategies that will be needed to operate safely. These decisions are made more difficult by the lack of definitive evidence about transmission in children or about which mitigation measures are most effective for limiting the spread of the virus in schools.

Recognizing these challenges and the difficult choices faced by education leaders, the committee offers a set of eight recommendations intended both to provide guidance as leaders make these choices and to serve as a call to action for other stakeholders to provide support for educators in this difficult time. We also offer a ninth recommendation identifying four areas of research we believe need urgent attention so that decision-makers can soon have the evidence base they need for making more informed choices.

Recommendation 1: The Decision to Reopen

Districts should weigh the relative health risks of reopening against the educational risks of providing no in-person instruction in Fall 2020. Given the importance of in-person interaction for learning and development, districts should prioritize reopening with an emphasis on providing full-time, in-person instruction in grades K – 5 and for students with special needs who would be best served by in-person instruction.

A complex set of risks and trade-offs surrounds decisions about reopening school buildings. Reopening schools for in-person learning will necessarily bring a number of risks related to health and safety. Not reopening schools, however, also carries a number of risks that need to be considered. Distance learning, while an essential tool for ensuring continuity of instruction when school buildings are closed, cannot fully take the place of in-person interaction. Moreover, disparities in access to reliable Internet and appropriate electronic devices could compound already existing educational inequities. The risks of not having face-to-face learning are especially high for young children, who may suffer long-term consequences academically if they fall behind in the early grades.

Recommendation 2: Precautions for Reopening

To reopen during the pandemic, schools and districts should provide surgical masks for all teachers and staff, as well as supplies for effective hand hygiene for all people who enter school buildings.

In order to open for in-person learning, schools and districts will need to leverage the strengths and talents of teachers and school staff by attending to their health and safety concerns. As discussed in Chapter 3 , a significant portion of the teacher workforce is over the age of 65, signaling that these individuals are both at increased risk related to COVID-19, and eligible for retirement. This reality, combined with the fact that many schools and districts may choose to limit interaction among students by assigning students to smaller cohorts or pods, poses a serious human capital challenge for education stakeholders to consider. To make returning to work a safe and desirable option, stakeholders will need to take the health and safety concerns of teachers and staff seriously.

Recommendation 3: Partnerships Between School Districts and Public Health Officials

Local public health officials should partner with districts to

• assess the readiness of school facilities to ensure that they meet the minimum health and safety standards necessary to support COVID-19 mitigation strategies;
• consult on proposed plans for mitigating the spread of COVID-19;
• develop a protocol for monitoring data on the virus in order to (a) track community spread and (b) make decisions about changes to the mitigation strategies in place in schools and when future full school closures might be necessary;
• participate in shared decision-making about when it is necessary to initiate closure of schools for in-person learning; and
• design and deliver COVID-19–related prevention and health promotion training to staff, community, and students.

Not only will decisions related to when and how to reopen schools for in-person learning need to reflect a school district’s priorities and constraints, but also plans for reopening will need to include careful monitoring of the prevalence of COVID-19 in the community. In light of the rapidly changing circumstances surrounding what is known about COVID-19, the committee emphasizes that it is unreasonable to expect school districts to have the requisite in-house public health expertise to make ongoing decisions about reopening and operating schools.

Recommendation 4: Access to Public Health Expertise

States should ensure that, in portions of the state where public health offices are short staffed or lack personnel with expertise in infectious disease, districts have access to the ongoing support from public health officials that is needed to monitor and maintain the health of students and staff.

Not all school districts will be able to immediately access the appropriate public health expertise locally. In many parts of the United States, especially rural areas, public health offices may be short staffed or may lack staff with deep expertise in infectious disease. Yet public health expertise is necessary for making the myriad ongoing decisions described in this report, and it is incumbent upon states to ensure that this need is addressed.

Recommendation 5: Decision-Making Coalitions

State and local decision-makers and education leaders should develop a mechanism, such as a local task force, that allows for input from representatives of school staff, families, local health officials, and other community interests to inform decisions related to reopening schools. Such a cross-sector task force should

• determine educational priorities and community values related to opening schools;
• be explicit about financial, staffing, and facilities-related constraints;
• determine a plan for informing ongoing decisions about schools;
• establish a plan for communication; and
• liaise with communities to advocate for needed resources.

While public health expertise is a critical component of making smart decisions related to reopening schools, it is just one perspective necessary for outlining a plan that reflects the needs and priorities of a community. As discussed in Chapter 4 , many different stakeholders are invested in K–12

education. In order to approach reopening schools in ways that reflect a community’s collective values, it is critical that state and local decision makers engage a range of different constituencies in the process of delineating a plan for reopening schools and monitoring their ongoing safety.

Recommendation 6: Equity in Reopening

In developing plans for reopening schools and implementing mitigation strategies, districts should take into account existing disparities within and across schools. Across schools, plans need to address disparities in school facilities, staffing shortages, overcrowding, and remote learning infrastructures. Within schools, plans should address disparities in resources for students and families. These issues might include access to technology, health care services, ability to provide masks for students, and other considerations.

As this report discusses throughout, decisions around reopening schools are occurring in the context of a deeply inequitable public school system. Unless school districts directly address equity in their planning process, reopening schools during the COVID-19 pandemic will undoubtedly exacerbate existing disparities in educational access and outcomes. As part of the planning process, districts will need to understand how existing inequities (in school facilities, staffing, access to resources, etc.) are likely to interact with the lived realities of communities disproportionately affected by COVID-19, so that the plans can identify where additional resources or special considerations are necessary.

Recommendation 7: Addressing Financial Burdens for Schools and Districts

Schools will not be able to take on the entire financial burden of implementing the mitigation strategies. Federal and state governments should provide significant resources to districts and schools to enable them to implement the suite of measures required to maintain individual and community health and allow schools to remain open. Under-resourced districts with aging facilities in poor condition will need additional financial support to bring facilities to basic health and safety standards. In addition, state departments of education should not penalize schools by withholding statewide school funding formula monies for student absences during the COVID-19 pandemic.

The various strategies for mitigating the transmission of COVID-19 reviewed in Chapter 5 will be the primary tools used by schools to support the health of their staff and students as they reopen school buildings. This list of strategies is long and complex, and implementing them will require a substantial investment of financial and human capital resources. These considerable expenditures are coming at a time when many districts are

looking at uncertain financial futures as a result of the pandemic. While the size of the funding shortfall will depend on how well resourced a school district is, many districts will be unable to afford implementing the entire suite of mitigation measures, potentially leaving students and staff in those districts at greater risk of infection. In the absence of substantial financial support from the federal government and state governments, it is likely that the communities most impacted by COVID-19 will see even worse health outcomes in the wake of reopening schools.

As noted throughout this report, districts within the same state are likely to have significantly different resources (financial, human capital, etc.) to put toward reopening schools. States will need to have a role in ensuring an equitable distribution of resources so that districts can implement the measures required for a strategic reopening in their local contexts. Further, in order to equitably support districts and schools, states should not withhold funds or otherwise penalize districts if families choose remote or distance learning options for their children in Fall 2020.

Recommendation 8: High-Priority Mitigation Strategies

Based on what is currently known about the spread of COVID-19, districts should prioritize mask wearing, providing healthy hand hygiene solutions, physical distancing, and limiting large gatherings. Cleaning, ventilation, and air filtration are also important, but attending to those strategies alone will not sufficiently lower the risk of transmission. Creating small cohorts of students is another promising strategy.

Although it is impossible to eliminate the risk of transmission of COVID-19 in schools completely, the mitigation strategies recommended by the Centers for Disease Control and Prevention and described in this report are showing promise for reducing transmission when implemented effectively. The lack of evidence about the relative effectiveness of different strategies, especially given the considerable costs involved in implementing them all, is a challenge for districts and schools, which are left largely on their own to prioritize which of the mitigation strategies to implement and how to make judgments about any necessary modifications due to practical constraints. The committee drew on its collective expertise and the limited available evidence to identify a few mitigation strategies that appear to show promise for districts looking to leverage limited resources.

Recommendation 9: Urgent Research

The research community should immediately conduct research that will provide the evidence needed to make informed decisions about school reopening and safe operation. The most urgent areas for inquiry are

• children and transmission of COVID-19 ,
• the role of reopening schools in contributing to the spread of COVID-19 in communities ,
• the role of airborne transmission of COVID-19, and
• the effectiveness of mitigation strategies.

Children and Transmission of COVID-19

The fact that as of this writing there was no scientific consensus on the role of children in transmitting COVID-19—to one another or to adults—poses a serious challenge for decision-makers. Although it is known that children are less likely both to contract COVID-19 and to experience significant consequences if they do, it is simply impossible to ascertain how likely children are to transmit the disease to school staff or adults at home. Clarity on this point would offer much-needed guidance for decision-makers regarding the necessity of various mitigation measures, and could potentially alleviate considerable anxiety for adults in proximity to students slated to attend school in person. Therefore, research is urgently needed to help understand the role of children in transmitting COVID-19.

The Role of Reopening Schools in Contributing to the Spread of COVID-19 in Communities

In addition to uncertainty around the role of children in transmitting COVID-19, much of the anxiety around reopening schools relates to how schools as a large gathering place for individuals will factor into the spread of COVID-19 in a community. To date, research on this question has produced mixed results ( Hsiang et al., 2020 ). Clarity in this area could provide further insight into what kinds of mitigation strategies might be of the highest priority for schools. As a result, research is urgently needed that looks specifically at how the reopening of schools matters (or does not) for the prevalence of COVID-19.

The Role of Airborne Transmission of COVID-19

In the process of writing this report, the committee repeatedly returned to conversations around the role of airborne transmission of COVID-19. As described in Chapter 3 , indoor air quality in U.S. public schools is notoriously poor, which can have innumerable deleterious health impacts on students and staff. However, because there is not yet scientific consensus on the role of airborne transmission in the spread of the virus, it is also unclear how the indoor air quality of schools matters in the spread of COVID-19. Given the considerable cost associated with updating aging

facilities, it is particularly important to understand the exact role of airborne transmission such that stakeholders can assess the relative value and utility of that investment.

Effectiveness of Mitigation Strategies

Although this committee was expressly tasked with assessing the effectiveness and practicality of the various mitigation strategies intended to reduce the transmission of COVID-19, we were repeatedly thwarted in that endeavor by the lack of clarifying evidence. If this committee of experts was unable to reach consensus on the best direction for schools, it is likely to be extremely challenging for education stakeholders to navigate the plethora of guidance documents to determine what is best for their schools and district. Research on the effectiveness of mitigation strategies and their specific utility in school settings is needed immediately. The committee also suggests that as schools reopen to in-person learning in Fall 2020, researchers leverage the occasion to conduct research in real time, and provide guidance as soon as it becomes available.

This page intentionally left blank.

The COVID-19 pandemic has presented unprecedented challenges to the nation's K-12 education system. The rush to slow the spread of the virus led to closures of schools across the country, with little time to ensure continuity of instruction or to create a framework for deciding when and how to reopen schools. States, districts, and schools are now grappling with the complex and high-stakes questions of whether to reopen school buildings and how to operate them safely if they do reopen. These decisions need to be informed by the most up-to-date evidence about the SARS-CoV-2 virus that causes COVID-19; about the impacts of school closures on students and families; and about the complexities of operating school buildings as the pandemic persists.

Reopening K-12 Schools During the COVID-19 Pandemic: Prioritizing Health, Equity, and Communities provides guidance on the reopening and operation of elementary and secondary schools for the 2020-2021 school year. The recommendations of this report are designed to help districts and schools successfully navigate the complex decisions around reopening school buildings, keeping them open, and operating them safely.

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A study by researchers at Stanford Graduate School of Education (GSE) provides new evidence about the pandemic’s impact on learning among students in the earliest grades, showing distinct changes in the growth of basic reading skills during different time periods over the past year.

Ben Domingue (Image credit: Courtesy Graduate School of Education)

Results from a reading assessment given to first- through fourth-graders nationwide show that the students’ development of oral reading fluency – the ability to quickly and accurately read aloud – largely stopped in spring 2020 after the abrupt school closures brought on by COVID-19. Gains in these skills were stronger in fall 2020, but not enough to recoup the loss students experienced in the spring.

“It seems that these students, in general, didn’t develop any reading skills during the spring – growth stalled when schooling was interrupted and remained stagnant through the summer,” said Ben Domingue , an assistant professor at Stanford GSE and first author on the study , which was released by Policy Analysis for California Education (PACE), a nonpartisan research network housed at Stanford.

“It picked up in the fall, which is a testament to the work that educators did in preparing for the new school year and their creativity in coming up with ways to teach,” Domingue said. “But that growth was not robust enough to make up for the gaps from the spring.”

Second- and third-graders were most affected, the study found. Overall, students’ reading fluency in second and third grade is now approximately 30 percent behind what would be expected in a typical year.

Reading fluency is fundamental for academic development more broadly, the researchers said, because problems with this skill can interfere with students’ ability to learn other subjects as they make their way through later grades.

“Reading is kind of a gateway to the development of academic skills across all disciplines,” said Domingue. “It’s a key that opens all of the doors. If a kid can’t read effectively by third grade or so, they’re unlikely to be able to access content in their other courses.”

Measuring periodically, not annually

The new study differs from previous research on COVID-19 learning loss in that students’ skills were measured periodically throughout the year, making it possible to assess growth at different stages of the pandemic.

“Most studies on learning loss so far have looked at fall-to-fall changes to show how students have been affected by COVID,” said Domingue. “But just measuring the cumulative effect doesn’t help us understand what was going on between those two time points. There were a lot of changes in what school looked like during different periods between those two points, and it seemed likely there would be some differences in the patterns of learning.”

The study’s focus on students in early elementary grades also distinguishes it from others on learning growth and loss, which typically look at the impact on students in grades 3 through 8 – the ages most often included in annual standardized exams and other routine assessments.

A fundamental skill

The findings were based on data generated by an oral assessment measuring reading fluency in more than 100 school districts nationwide. The reading assessment used in the study takes only a few minutes, and though normally administered in a classroom, it was also conducted remotely during the pandemic. Students were recorded while reading aloud from a device, and their score was based on a combination of human transcription and speech recognition.

The researchers examined trends in the students’ long-run growth back to 2018, observing fairly steady growth until the onset of the pandemic in the spring of 2020. The trajectory flattened at that point and remained flat throughout the summer, indicating that children’s reading abilities had stopped. “It was flat in an absolute sense, not just relative to years past,” said Domingue.

Growth resumed in the fall at levels similar to what the researchers saw before the pandemic. But those gains weren’t enough to make up for the ground lost earlier in the year.

The researchers also observed inequitable impact: Students in historically lower-achieving districts (based on data from the Stanford Education Data Archive ) developed reading skills at a slower rate than those in higher-achieving ones. Schools that typically score low on annual standardized tests often serve a greater share of low-income and minority students – populations disproportionately affected by the pandemic in ways that impinge on their readiness to learn, including lack of access to computers, reliable internet access or a parent at home.

“It’s quite likely that lower-achieving schools are dealing with a whole battery of problems that educators in more affluent districts aren’t facing,” said Domingue. “But there was still growth. The teachers were probably moving heaven and earth to help their kids learn to read, and it’s reflected in the gains. But it’s important to recognize the differential impact on students.”

The researchers also found that about 10 percent of students who were tested before the pandemic were not observed in fall 2020. It’s not clear why they were missing, but the researchers suggest that if these students had trouble accessing the assessment remotely, they may be less engaged with school overall and could be falling even further behind than students who were tested.

The researchers caution that, while their analysis provides important evidence on learning loss in the early grades, it doesn’t include information about whether students attended school in person, remotely or in some hybrid form.

They also note that their findings should not be applied to other academic subjects, largely because of the focus on reading in the early grades and the likelihood that it was a centerpiece of many schools’ instruction for the fall of 2020.

While the full extent of COVID-19’s impact on learning won’t be clear for months or even years, this study provides evidence that – after the initial shock of the pandemic –educators found ways to teach and assess young students’ reading skills. And even in the midst of continued uncertainty and disruption, these students were able to achieve gains in the fall similar to pre-pandemic times.

“We can build on this research by identifying practices that accelerate learning for students who’ve fallen behind, and by making sure schools have the resources they need,” said Heather Hough, executive director of PACE and coauthor of the study. “These findings are worrisome, but they do not need to be catastrophic.”

Other co-authors on the study include Jason Yeatman , an assistant professor at Stanford GSE and the School of Medicine and David Lang, a GSE doctoral student.

Media Contacts

Carrie Spector, Stanford Graduate School of Education: (650) 724-7384; [email protected]

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A research and policy agenda for the post-pandemic world

Affiliation.

• 1 Tony Blair Institute for Global Change, London, UK.
• PMID: 34286185
• PMCID: PMC8285152
• DOI: 10.7861/fhj.2021-0082

The COVID-19 pandemic response has engaged the academic, public, private and health sectors in the real-time development of technologies and practices to enable predictive, preventive, personalised and participatory (P4) health. Myriad cases of collaborative innovation across these sectors have emerged throughout the pandemic response (despite certain observed technical, social and institutional barriers) that serve as examples to address post-pandemic health system challenges. In this paper, we propose a joint research and policy agenda to generate the knowledge and practices to identify and extend these acute gains toward chronic health system challenges in the post-pandemic era. We identify three key themes for post-pandemic research and policy: the dialectic between novel and traditional techniques, the tension between centralised and local decision-making, and cooperation across academic disciplines, sectors and borders. Going forward, attention to these three themes by researchers and policymakers will help align our health, policy, academic and technological systems to provide better health for all.

Keywords: COVID-19; health policy; health systems; healthcare delivery; innovation policy.

© Royal College of Physicians 2021. All rights reserved.

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Mental Health Research During the COVID-19 Pandemic: Focuses and Trends

Yaodong liang.

1 Law School, Changsha University, Changsha, China

2 Department of Psychology, University of Toronto St. George, Toronto, ON, Canada

3 Centre for Mental Health and Education, Central South University, Changsha, China

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The COVID-19 pandemic has profoundly influenced the world. In wave after wave, many countries suffered from the pandemic, which caused social instability, hindered global growth, and harmed mental health. Although research has been published on various mental health issues during the pandemic, some profound effects on mental health are difficult to observe and study thoroughly in the short term. The impact of the pandemic on mental health is still at a nascent stage of research. Based on the existing literature, we used bibliometric tools to conduct an overall analysis of mental health research during the COVID-19 pandemic.

Researchers from universities, hospitals, communities, and medical institutions around the world used questionnaire surveys, telephone-based surveys, online surveys, cross-sectional surveys, systematic reviews and meta-analyses, and systematic umbrella reviews as their research methods. Papers from the three academic databases, Web of Science (WOS), ProQuest Academic Database (ProQuest), and China National Knowledge Infrastructure (CNKI), were included. Their previous research results were systematically collected, sorted, and translated and CiteSpace 5.1 and VOSviewers 1.6.13 were used to conduct a bibliometric analysis of them.

Authors with papers in this field are generally from the USA, the People's Republic of China, the UK, South Korea, Singapore, and Australia. Huazhong University of Science and Technology, Hong Kong Polytechnic University, and Shanghai Jiao Tong University are the top three institutions in terms of the production of research papers on the subject. The University of Toronto, Columbia University, and the University of Melbourne played an important role in the research of mental health problems during the COVID-19 pandemic. The numbers of related research papers in the USA and China are significantly larger than those in the other countries, while co-occurrence centrality indexes in Germany, Italy, England, and Canada may be higher.

We found that the most mentioned keywords in the study of mental health research during the COVID-19 pandemic can be divided into three categories: keywords that represent specific groups of people, that describe influences and symptoms, and that are related to public health policies. The most-cited issues were about medical staff, isolation, psychological symptoms, telehealth, social media, and loneliness. Protection of the youth and health workers and telemedicine research are expected to gain importance in the future.

Introduction

Although the impacts of the COVID-19 pandemic will be recorded in human medical history and in socio-economic history, various psychological consequences regarding mental health among populations cannot be ignored, including stress, anxiety, depression, frustration, insomnia, and so on. Researchers from universities, hospitals, communities, and medical institutions worldwide have been focusing on mental health problems during the pandemic. They have used questionnaire surveys, telephone-based surveys, online surveys, cross-sectional surveys, systematic reviews and meta-analysis, and systematic umbrella reviews to investigate mental health problems during the pandemic. Two years after the outbreak of the COVID-19, the pandemic has gradually subsided in some countries, while others have adopted a strategy of coexisting with the virus. If more deadly mutant strains do not appear in the future, it is very likely that the pandemic will not climax again. It is pertinent to summarize and study mental health research during the pandemic, because many psychological problems have arisen as a result, and there has been significant interest in research on such issues in the previous two years.

As an effective quantitative analysis method, bibliometrics can be used not only to assess the quality and quantity of published papers, but also to explore research focuses and trends, the distribution of authors and institutions, the impact of publications, journals, and different countries regarding research contributions to the theme. Due to the rapid growth in research in this area, there are now over 1,000 academic papers, and accordingly, it would appear necessary to investigate important, valid, and meaningful information from large databases to guide scientific research. The authors used CiteSpace and VOSviewers to determine the focuses and trends in this regard.

Data Analysis and Visualization

The authors searched the Web of Science (WOS), ProQuest Academic Database (ProQuest), and China National Knowledge Infrastructure (CNKI) to extract publications related to mental health and COVID-19. Their previous research results were systematically collected, sorted, and translated, and CiteSpace 5.1 and VOSviewers 1.6.13 were used to conduct a bibliometric analysis of them.

Data Source and Search Strategy

Our team selected 1,226 papers from 2019 to 2022 using three combinations of keywords, mental health and COVID-19, mental health and new coronavirus, and mental health and novel coronavirus, from the three academic paper databases, WOS, ProQuest, and CNKI. Two explanations are necessary here, the first is about the keywords and the second is about the databases. (1) The reason we used new or novel coronavirus as keywords was that the name COVID-19 has not been determined about 2 years ago. In order not to miss relevant research results, we also included these synonyms as keywords for the search. (2) Among the three databases, WOS and ProQuest, in which most of the English-language papers were published, are well-known to scholars all around the world. However, the CNKI database is not as popular as WOS or ProQuest given that most of the papers in CNKI were published in Chinese. We chose to use the CNKI data for the following three reasons: first, China was the most affected country during the COVID-19 outbreak and Chinese academic journals published significant research on mental health. Second, CNKI is the largest Chinese academic database. Third, after the outbreak, the Chinese government's virus clearance policy has been implemented and continues to date. Strict control has helped suppress the spread of the virus, but has also likely had mental health implications, given the severe reduction in social interactions. Therefore, we think that the Chinese database is appropriate and useful in this study.

About 50% of the articles were from the WOS, about 10% of the articles from ProQuest, and about 40% from CNKI. Basic information such as title, author, institution, country, abstract, keywords, methods, results, and conclusions of all articles, if not in English, are translated into English and analyzed using SiteSpaceII and VOSviewers. Since the keywords include COVID-19 and mental health, synonyms such as novel coronavirus and psychological distress spontaneously appeared while searching. Words that are closely related to the subject, such as public health, quarantine, and insomnia, were most frequently mentioned.

Most articles were published during the period from February 2020 to July 2022, including those pre-published online from April to July, and only one article that had been published in 2019 was included. Judging from the line chart above, since the volume of COVID-19 and mental health-related articles had already risen two times in June 2020 and June 2021 and then remained low until now, it is high time to conclude a previous study on COVID-19 and mental health, to sort out the foci of those studies, and to analyze and predict future trends ( Figure 1 ).

The volume of COVID-19 and mental health-related articles in 2020–2022.

Scholars from around the world have contributed to the study of mental health issues during the COVID-19 pandemic. The top 10 countries with the largest quantum of publications related to mental health during COVID-19 are the USA, People's Republic of China, England, Canada, Australia, India, Italy, Japan, Iran, and Germany. Wide and active participation of several countries has laid a solid foundation for its future development. Universities, hospitals, communities, and medical institutions around the world have conducted sample surveys of patients, students, community residents, medical workers, and other sample populations of considerable sample sizes since the outbreak. Survey and research methods include questionnaire survey, telephone-based survey, online survey, cross-sectional survey, systematic review and meta-analyses, and systematic umbrella review ( Table 1 ).

Top 20 countries.

Most papers are from the USA, the People's Republic of China, England, Australia, Canada, India, Italy, Iran, Japan, and Germany. Judging from the country or region co-occurrence graph, England and Canada are in the center of this graph, with India, Poland, Denmark, Spain, South Korea, Portugal, Italy, and Canada around them. England, Australia, Canada, Japan, Brazil, India, Iran, and Germany have done significant research work in this field. In addition, the number of related research papers in the USA and China is significantly larger than that in all other countries ( Figure 2 ).

Country or region co-occurrence.

In Table 2 , we can see that most names of the top 20 authors are Asian names, and they are mainly from China. Six of them published more than 10 articles by the end of 2021. In the extended ranking, we find that the authors who have published a large number of papers are generally from the USA, China, the UK, South Korea, Singapore, and Australia. The authors Griffiths MD, Cheung T, Xiang Y, Lin C, Wang Y, and Zhang L were very active in this field of study.

Top 20 authors.

In the abovementioned graphs, we can see six groups of related authors. The VOSviewer was used to describe the partnership between them. Though six colors were used to separate these groups, there were still lines connecting the groups to represent the partnership between them. We can take Cheung T and Xiang Y as the center of the largest group. Another group with Griffiths MD and Lin C as its center was also significant ( Figures 3 , ​ ,4 4 ).

Author co-occurrence.

Author co-occurrence groups.

The top five institutions are Huazhong University of Science and Technology, Hong Kong Polytechnic University, Shanghai Jiao Tong University, Columbia University, and the University of Toronto. Meanwhile, the top five institutions in centrality are the University of Macau, the University of Melbourne, Columbia University, Wuhan University, and the University of Toronto. It is worth mentioning that Huazhong University of Science and Technology and Wuhan University are located in the city of Wuhan, one of the areas most affected by the virus through the outbreak. The society and economy of the city temporarily stagnated at the time, and its medical system was once paralyzed. Eventually, Wuhan City's medical system was fully recovered. The University of Toronto, Columbia University, and the University of Melbourne have played an important role in the research of mental health problems during the COVID-19 pandemic ( Table 3 and Figure 5 ).

Top 20 institutions.

Institutions' co-occurrence.

As can be seen in Figure 6 , Huazhong University of Science and Technology has led Chinese universities and research institutions, such as Shanghai Jiao Tong University and Peking University, in conducting research on COVID-19 and mental health. Hong Kong Polytechnic University, Fudan University, and the University of Melbourne acted as bridges, connecting famous universities and research institutions in Europe, America, and other countries in the world, such as Kings College London and Harvard Medical School, to jointly study issues in this field. In particular, they conduct joint research, directly or indirectly, through Hong Kong Polytechnic University, which display the important communication and joint role of Hong Kong Polytechnic University.

Keyword clustering.

Judging from Table 4 , the most mentioned keywords, in addition to COVID-19 and mental health, can be roughly divided into three categories: (1) keywords representing specific groups of people, such as adolescents, young adults, doctors, nurses, medical staff, and healthcare workers; (2) keywords describing influences and symptoms, such as isolation, loneliness, anxiety, depression, stress, and insomnia; and (3) keywords related to public health policies, such as lockdown, social distancing, telehealth, telemedicine, and quarantine.

Keyword clustering I.

In Graph 7, we can judge that COVID-19, mental health, pandemic, and coronavirus are represented by larger red dots as their centrality indexes are naturally higher. In this bibliometric network map, other keywords emerged next to them and together formed this visualization bibliometric network. Occupational and sociodemographic characteristics are clustered together, while symptoms of mental health problems are clustered next to them. Specific groups of people and their typical symptoms and causes occupy certain areas on the map. For example, typical symptoms of university students and the possible causes of these symptoms are grouped together on the map. Similarly, quarantine policy and its influence are also classified in certain areas. In addition, research methods and solutions appeared sporadically on this map.

Table 5 shows eight groups of core keywords separated from keyword clustering I. Each of these groups contains three keywords, which proves that these keywords appear at the same time in a considerable part of the research, and are more closely related. Keyword ClusteringII cannot only present the outline of existing mental health research in academia, but also highlights the focus of research. In addition, SiteSpaceII and VOSviewers also gave us some clues about the research trends and further development.

Keyword clustering II.

Research Focuses

Medical staff.

The COVID-19 pandemic has exacerbated mental health problems among populations, especially medical staff, patients with COVID-19, chronic disease patients, and isolated people. Doctors, nurses, and other medical staff have significantly higher rates of insomnia than other populations ( 1 ). The researchers obtained the relevant demographic data through the WeChat questionnaire survey. Questions in the questionnaire are related to insomnia, depression, anxiety, and stress-related symptoms during the pandemic. Their research found that, since the outbreak, more than one-third of the medical staff suffered from symptoms of insomnia. Psychological intervention measures were necessary for those people ( 2 ). Research within medical institutions shows that the psychological pressure of medical staff in isolation wards was greater, but had also attracted greater attention from hospital administrators. The concern of hospital managers alleviated the pressure of medical staff to a certain extent. Further, concern for the public also reduced their psychological burden. In terms of anxiety about infection and fatigue factors, the research results showed that the psychological burden of nurses was heavier than that of doctors. Healthcare workers who lived with their own children showed more obvious fatigue and anxiety, which might be due to the fear of their children becoming infected. In terms of workload and work motivation, medical staff who have been working for more than 20 years have a heavier workload, but they can still maintain their enthusiasm to fight against the pandemic ( 3 ). Another survey showed that 73.4% of healthcare workers, mainly physicians, nurses, and auxiliary staff, reported post-traumatic stress symptoms during outbreaks, with symptoms persisting for up to 3 years in 10–40% of the cases. Depressive symptoms were reported in 27.5–50.7%, insomnia symptoms in 34–36.1%, and severe anxiety symptoms in 45% ( 4 ). A subgroup analysis revealed gender and occupational differences, with female health care practitioners and nurses exhibiting higher rates of affective symptoms compared to men and medical staff, respectively ( 5 ).

As a result, depressive symptoms (21%) and anxiety symptoms (19%) are higher during the COVID-19 pandemic compared to previous epidemiological data. About 16% of the subjects suffered from severe clinical insomnia during the lockdown. The pandemic and lockdown seemed to be particularly stressful for younger adults who were under 35 years old, women, people out of work, or those with low incomes ( 6 ). In the fight against the pandemic, China adopted measures to restrict population aggregation, such as the blockade of pandemic areas, individual patient isolation, and restrictions on the movement of people in non-pandemic areas. These measures effectively prevented the spread of the pandemic. At the same time, the use of health codes, grid-like community management, and the operational efficiency of infectious disease information networks have greatly improved. However, quarantine has also brought with it a number of problems, such as increasing psychological pressure on the population, affecting the daily lives of families, and hindering social and economic development ( 7 ). A large sample size study with wide coverage published in 2021 showed that young people quarantined at home in different provinces had different rates of anxiety and depression due to different severity of pandemic situations in different regions. The risk of anxiety and depression was statistically significantly higher in girls than in boys. The rate of anxiety and depression was affected by factors, such as gender, age, and area, as well as the existence of COVID-19 cases in the surrounding area ( 8 ).

Psychological Symptoms

The impact of the aforementioned isolation measures on mental health is only part of the impact of the COVID-19 on mental health. Psychological symptoms brought about by the pandemic have also been systematically sorted out by scholars. These studies show two clues. First, certain people have special psychological symptoms; second, psychological symptoms in different countries of the world are roughly the same. Several factors were associated with a higher risk of psychiatric symptoms or low psychological wellbeing, including female gender and poor self-related health ( 9 ). Relatively, severe symptoms of anxiety, depression, post-traumatic stress disorder, psychological distress, and stress were reported in the general population during the COVID-19 pandemic in China, Spain, Italy, Iran, the USA, Turkey, Nepal, and Denmark. Risk factors associated with measures of distress include female gender, younger age group, the presence of chronic or psychiatric illnesses, unemployment, student status, and frequent exposure to social media or news concerning COVID-19. The pandemic is associated with significant levels of psychological distress that, in many cases, will meet the threshold for clinical relevance. Mitigating the hazardous effects of COVID-19 on mental health is an international public health priority ( 1 ). Infectious disease pandemics often cause some people to act irrationally. The results of a survey based on psychological symptoms and irrational behaviors have drawn some conclusions. First, the vast majority of people remain in good physical and mental health, but some exhibit irrational behaviors. Second, women, elderly people, and those with confirmed cases showed more physical and mental symptoms and irrational behaviors. Finally, paradoxically, people with high education levels showed more mental symptoms, but fewer irrational behaviors ( 10 ).

Telemedicine

Just as the pandemic has enabled the rapid development of online education, the prospects of telemedicine are also favored by experts, observers, and investors. However, there are two restrictive aspects, namely, telemedicine equipment and telemedicine human resources. The application of 5G communication technology, telemedicine equipment, remote monitoring equipment, remote physical sign monitoring equipment, and medical artificial intelligence triage equipment all need to be urgently developed and improved. Jiangsu, a province in China, is a model province of the national project called “Internet + Medical and Health.” During the pandemic, the telemedicine by public hospitals in Jiangsu Province helped improve the efficiency of diagnosis and treatment, alleviating the pressure of offline diagnosis and treatment, and reducing the risk of cross-infection. Subsequently, medical staff were fully supportive of telemedicine. However, there was a shortage of medical staff in fever clinics, obstetrics and gynecology, pediatrics, and psychiatrists that provided telemedicine services, and they lacked corresponding incentive mechanisms ( 11 ). Effective mitigation strategies to improve mental health were developed by public health management experts. To control the rapid spread of COVID-19 and manage the crisis better, both developed and developing countries have been improving the efficiency of their health system by replacing a proportion of face-to-face clinical encounters with telemedicine solutions ( 12 ).

Social Media

There were rumors in various kinds of media during the COVID-19 pandemic. Although we can regard rumors as a disturbing error for psychological measurement, if they are not strictly controlled, their impact on people's mental health and behavior cannot be ignored. A study focusing on the spread of WeChat rumors has explored the psychological perception mechanism of audiences affected by rumor spreading in emergency situations. The study has significant results in the following terms: the form characteristics of the rumors in COVID-19, the ranking of susceptible age groups, the degree of dependence of the test subject on certain media and its psychological impact, and the follow-up behavior of the test subjects related to psychological variables ( 2 ). In 2021, another interesting study based on the data of TikTok videos released by three mainstream media in China showed that they inevitably caused some psychological trauma to the public. However, from the perspective of overall emotional orientation, short-format videos with positive reporting emotional tendencies had an advantage in attracting likes from TikTok users. Positive government responses to pandemic information were very important, and those responses could be recognized and praised by most social media users. Some of the TikTok videos, such as The Plasma of a Recovered Patient Cured 11 Other ICU Patients, The First COVID-19 Test Kit Passed Inspection, and A Frenchman Named Fred gave up Returning to Home to Join China's Anti-COVID-19 Battle, are extremely popular among social media users. Most social media users have been providing spiritual sustenance for people in the pandemic ( 13 ). When a public health crisis occurs, social media plays an important role in increasing public vigilance, helping the public identify rumors, and boosting public morale.

University Students and Loneliness

A study that assessed the adverse impact on the mental health of university students has drawn some conclusions. First, the severity of the outbreak has an indirect effect on negative emotions by affecting sleep quality. Second, a possible mitigation strategy to improve mental health includes ensuring suitable amounts of daily physical activity and deep sleep. Third, the pandemic has reduced people's aggressiveness, probably by making people realize the fragility and preciousness of life ( 14 ). Another research focused on social networks and mental health compared two cohorts of Swiss undergraduate students who were experiencing the crisis, and made an additional comparison with an earlier cohort who did not experience the pandemic. The researchers found that interaction and co-study networks had become sparser, and more students were studying alone. Stressors shifted from fear of missing out on social life to concern about health, family, friends, and their future ( 15 ). Young adults, women, people with lower education or lower income, the economically inactive, people living alone, and urban residents were at greater risk of being lonely during the pandemic. Being a student emerged as a higher than usual risk factor for loneliness during the lockdown ( 16 ). A study to explore the relationship between loneliness and stress among undergraduates in North America showed that the loneliness and stress among college students increased. On one hand, stress plays a key role in the deterioration of college students' mental health; on the other hand, reducing the loneliness of college students is expected to reduce the negative impact of stress on college students' mental health ( 17 ).

Research Trends

Due to the limited training sample of academic papers at present, it is difficult to predict the outcomes accurately. Though we cannot exactly predict the hot issues in the future, we can sort out some possible research trends in this field by analyzing existing research approaches. Psychological symptoms that affected people's mental health during the COVID-19 pandemic will be discovered further, especially those that probably continued to affect people's mental health even after the pandemic is controlled.

Studies on mild psychological symptoms, such as mild insomnia and anxiety, tend to decrease slowly, and in the case of severe problems caused by the pandemic, or severe psychological symptoms, such as clinical insomnia, depression, bipolar disorder, the corresponding in-depth research will continue. The impact of a global pandemic on the mental health of the global population must be profound and worthy of study. Due to the rapid development of COVID-19, many famous universities and research institutions have not had enough time to collect sufficient data and relevant research materials. The different effects on populations in different countries with different pandemic prevention policies are not yet fully displayed.

Regardless of how research on mental health develops, the COVID-19 pandemic has indeed brought us some new insights. As mentioned in many articles on mental health interventions for adolescents and college students, the mental health of specific populations and the development of telemedicine all deserve continued academic attention. Mental health intervention for adolescents and college students is a means to consider and prepare for the future. To ensure responsible and accountable behavior for future generations, we should all pay attention to the research and application of this method. Caring for specific groups of people, such as doctors, nurses, and other healthcare workers, and studying how to protect them in a global pandemic is a topic that global academia must study in the future, or we will lose protection the next time the virus sweeps the world. In addition, telemedicine is the trend in the future, and face-to-face diagnosis and treatment will undoubtedly increase the risk of cross-infection during the pandemic. Therefore, the development of telemedicine is an important way to avoid contact between the patients. The COVID-19 pandemic has accelerated the research and development of telemedicine.

Limitations

(1) Though we have selected three databases for analysis, there are still some databases that may be related to this field that are not covered in this study. (2) Since COVID-19-related research was started just 2 years ago, the results of the bibliometric analysis may vary after adding new data. (3) The citation frequency of articles is influenced by the time of publication, thus previously published articles should be cited more frequently than new ones. (4) Bibliometric data change over time, and different conclusions may be drawn over time. Therefore, this study should be updated in the future.

Conclusions

The most mentioned keywords, in addition to COVID-19 and mental health, can be roughly divided into three categories: keywords representing specific groups of people, keywords describing influences and symptoms, and keywords related to public health policies. The most mentioned issues were about medical staff, quarantine, psychological symptoms, telemedicine, social media, and loneliness. Mild psychological symptoms, such as insomnia, depression, and anxiety, tend to decrease slowly, while severe ones, such as severe clinical insomnia, depression, and bipolar disorder, are yet to be discovered. The importance of studies on the protection of youth medical staff and telemedicine studies will become even more significant in the future. While physical health is threatened by the pandemic, human mental health also suffers. Judging from the current situation of pandemic prevention and control, if severe prevention and control measures are taken, the impact of COVID-19 on the health of the social population is controllable; if a strategy of coexistence with the virus is adopted, as long as a new deadly mutation of COVID-19 does not emerge, the outcomes can be controllable. However, the impact of the pandemic on human mental health is not easy to predict. In addition to the abovementioned papers on mental health, the author also noted that some papers focused on neuromedicine pointed out that the virus might have some damage to the normal working mechanism of the human nervous system, but these studies are outside the scope of mental health research, at least for now. This study aims to summarize the observations, analysis, and research of scholars on mental health during the pandemic from 2020 to early 2022, with a view to provide more clues for future researchers. We hope that more researchers will build on our research to discover new research areas and new questions to help more countries, groups, and individuals affected by the COVID-19 pandemic.

Data Availability Statement

Author contributions.

YL was responsible for the concept and design, drafting this article, and bibliometric analysis. YL, LS, and XT were responsible for the revision and data collection. All authors contributed to this article and approved the submitted version.

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.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors thank the study participants for their time and effort.