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Mapping covid data, peer-reviewed publications about covid-19 (coronavirus) by yale authors.

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Laboratory Tests for COVID-19: A Review of Peer-Reviewed Publications and Implications for Clinical Use

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Diagnostic tests for the coronavirus infection 2019 (COVID-19) are critical for prompt diagnosis, treatment and isolation to break the cycle of transmission. A positive real-time reverse-transcriptase polymerase chain reaction (RT-PCR), in conjunction with clinical and epidemiologic data, is the current standard for diagnosis, but several challenges still exist. Serological assays help to understand epidemiology better and to evaluate vaccine responses but they are unreliable for diagnosis in the acute phase of illness or assuming protective immunity. Serology is gaining attention, mainly because of convalescent plasma gaining importance as treatment for clinically worsening COVID-19 patients. We provide a narrative review of peer-reviewed research studies on RT-PCR, serology and antigen immune-assays for COVID-19, briefly describe their lab methods and discuss their limitations for clinical practice.

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Peer-reviewed report on Moderna COVID-19 vaccine publishes

Data from Phase 3 clinical trial confirm vaccine is effective.

The investigational vaccine known as mRNA-1273 was 94.1% efficacious in preventing symptomatic coronavirus disease 2019 (COVID-19), according to preliminary results from a Phase 3 clinical trial reported in the New England Journal of Medicine. The vaccine also demonstrated efficacy in preventing severe COVID-19. Investigators identified no safety concerns and no evidence of vaccine-associated enhanced respiratory disease (VAERD).

The vaccine was co-developed by Moderna, Inc., a biotechnology company based in Cambridge, Massachusetts, and the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health. Moderna and NIAID previously shared initial results from the COVE trial. On Dec. 18, 2020, the FDA issued an Emergency Use Authorization allowing Moderna to make the vaccine available for the prevention of COVID-19 in adults in the United States.

The trial was led by principal investigators Lindsey R. Baden, M.D. of Brigham and Women’s Hospital in Boston, Hana M. El-Sahly, M.D. of Baylor College of Medicine in Houston, and Brandon Essink, M.D., of Meridian Clinical Research. The trial was implemented under the U.S. government’s Operation Warp Speed program and supported by NIAID and the Biomedical Advanced Research and Development Authority (BARDA) of the U.S. Department of Health and Human Services’ Office of the Assistant Secretary for Preparedness and Response.

The trial began on July 27, 2020, and enrolled 30,420 adult volunteers at clinical research sites across the United States. Volunteers were randomly assigned 1:1 to receive either two 100 microgram (mcg) doses of the investigational vaccine or two shots of saline placebo 28 days apart. The average age of volunteers is 51 years. Approximately 47% are female, 25% are 65 years or older and 17% are under the age of 65 with medical conditions placing them at higher risk for severe COVID-19. Approximately 79% of participants are white, 10% are Black or African American, 5% are Asian, 0.8% are American Indian or Alaska Native, 0.2% are Native Hawaiian or Other Pacific Islander, 2% are multiracial, and 21% (of any race) are Hispanic or Latino.

From the start of the trial through Nov. 25, 2020, investigators recorded 196 cases of symptomatic COVID-19 occurring among participants at least 14 days after they received their second shot. One hundred and eighty-five cases (30 of which were classified as severe COVID-19) occurred in the placebo group and 11 cases (0 of which were classified as severe COVID-19) occurred in the group receiving mRNA-1273. The incidence of symptomatic COVID-19 was 94.1% lower in those participants who received mRNA-1273 as compared to those receiving placebo.

Investigators observed 236 cases of symptomatic COVID-19 among participants at least 14 days after they received their first shot, with 225 cases in the placebo group and 11 cases in the group receiving mRNA-1273. The vaccine efficacy was 95.2% for this secondary analysis.

There were no concerning safety issues with vaccination, according to the authors. Local reactions to the vaccine were generally mild. About 50% of participants receiving mRNA-1273 experienced moderate-to-severe side effects — such as fatigue, muscle aches, joint pain and headache — after the second dose, which resolved in most volunteers within two days.

Investigators also observed no evidence of VAERD among those who received mRNA-1273. This rare complication was seen in individuals vaccinated with a whole-inactivated respiratory syncytial virus (RSV) vaccine in the 1960s, before there was a capacity to define protein structures and measure immune responses with precision. VAERD can occur when a vaccine induces an immune response that is not strong enough to protect against infection.

Although mRNA-1273 is highly efficacious in preventing symptomatic COVID-19, there is not yet enough available data to draw conclusions as to whether the vaccine can impact SARS-CoV-2 transmission. Preliminary trial data suggests there may be some degree of prevention of asymptomatic infection after a single dose. Additional analyses are underway of the incidence of asymptomatic infection and viral shedding post-infection to understand the vaccine’s impact on infectiousness.

The authors concluded by discussing the unprecedented efficiency of the candidate vaccine’s development, noting, “this process demonstrates what is possible in the context of motivated collaboration among key sectors of society, including academia, government, industry, regulators and the larger community.”

LR Baden, et al . Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine. The New England Journal of Medicine . DOI: 10.1056/NEJMoa2035389.

NIAID Director Anthony S. Fauci, M.D. is available to comment on this study. John R. Mascola, M.D., director of NIAID’s Vaccine Research Center, is also available to comment.

To schedule interviews, please contact the NIAID News & Science Writing Branch, (301) 402-1663, [email protected] .

NIAID conducts and supports research — at NIH, throughout the United States, and worldwide — to study the causes of infectious and immune-mediated diseases, and to develop better means of preventing, diagnosing and treating these illnesses. News releases, fact sheets and other NIAID-related materials are available on the NIAID website .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

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Do three new studies add up to proof of COVID-19’s origin in a Wuhan animal market?

Preprints unlikely to end debate over how sars-cov-2 began the pandemic, but some scientists say lab-leak hypothesis has taken a “blow”.

Three new studies offer one indisputable conclusion about the origin of SARS-CoV-2: Despite the passage of 2 years and the Chinese government’s lack of transparency, data that can shed light on the pandemic’s greatest mystery still exist. And although these new analyses don’t all reach the same conclusion for how COVID-19 was sparked, each undercuts the theory that the virus somehow escaped from the Wuhan Institute of Virology, long a focus of suspicions.

The studies examine different aspects of the viral spread at the Huanan Seafood Market in Wuhan, China, the city where the first cases were detected. Two international efforts build the case that SARS-CoV-2 jumped to people from infected animals—a zoonotic leap—at the market, likely twice, at the end of 2019. A third, largely Chinese effort details early signs of the coronavirus in environmental and animal samples from the market but suggests the virus was imported there, perhaps from outside the country—a conclusion the University of Arizona’s Michael Worobey, an evolutionary biologist who is a corresponding author of the two international studies, calls “a huge disconnect.”

The studies were posted as preprints and are not peer reviewed, but scientists, biosecurity experts, journalists, and others are already intensely examining their details. “I have been brought closer to the zoonosis side with these preprints,” says Flo Débarre, an evolutionary biologist at the French national research agency, CNRS, who has followed the origin debate closely and not thrown her lot with either the natural-origin or the lab-leak camp. Evolutionary biologist William Hanage of Harvard University agrees these studies “will be taken as a blow” to the lab-leak hypothesis. “They substantially move the needle on the origins in the direction of the market,” Hanage says.

Skeptics of the natural origin theory maintain the market cluster could merely be a superspreader event touched off when a person infected with a lab-escaped coronavirus visited it. But Worobey thinks further data could make that contention even less tenable. A more transparent analysis of the market’s genetic sampling data, in particular, might identify exactly which species of animals sold there carried the virus.

In one study, Worobey and colleagues describe two subtly different lineages of SARS-CoV-2 that were found in people at the Huanan Seafood Market in late 2019, which they take as a sign that the virus jumped twice from animals to humans there. Their other study offers a geospatial analysis of the earliest human cases that pinpoints the market as the “epicenter” of SARS-CoV-2’s emergence , showing both lineages infected people who had links to the market or lived near it. It also connects the specific stalls at the market where live animals were sold to environmental samples known to have tested positive for the virus. “Together, these analyses provide dispositive evidence for the emergence of SARS-CoV-2 via the live wildlife trade and identify the Huanan market as the unambiguous epicenter of the COVID-19 pandemic,” they conclude.

Worobey and colleagues had hoped to release their preprints in the next week but sped up their plans, choosing a preprint server that posts without any delays, when the Chinese study was posted on 25 February on the Research Square site. Led by George Gao of the Chinese Academy of Sciences and co-authored by 37 other scientists (one is from Canada), that research—which builds on data earlier leaked to the media but never officially published—offers the most detailed description yet of the environmental samples the Chinese Center for Disease Control and Prevention obtained at the Huanan Seafood Market between 1 January and 2 March 2020.

In the new preprint, Gao and colleagues analyzed 1380 samples from 188 animals in the market and the environment, including sewer wells, the ground, feather removing machines, and “containers.” They found SARS-CoV-2 in 73 samples. But because all were from the environment, not the animals themselves, they assert that humans introduced the virus to the market. The authors call the market an “amplifier,” not the source, of SARS-CoV-2.

Hewing closely to government assertions on COVID-19’s origin, the preprint by Gao and colleagues notes studies that have reported evidence of SARS-CoV-2 in other countries before it surfaced in Wuhan, making no mention of critiques that attribute that evidence to contamination. It also floats a widely disputed theory that frozen food imported to China might have been the original source. (Authors of the paper, including Gao, did not respond to requests to discuss the work.)

The coronavirus lineage analysis from Worobey and colleagues refines an argument posited by virologist Robert Garry last year. In data on the early human cases, Garry had identified two different forms of SARS-CoV-2, differing by just two mutations, which he argued surfaced at different Wuhan markets in December 2019. The new work, which includes Garry as a co-author and cites evidence from the Gao study, reshapes that scenario significantly. It concludes that both lineages, dubbed A and B, originated at the Huanan Seafood Market and soon spread in nearby neighborhoods. B likely jumped from animals to humans in late November 2019, leading to the first detected case on 10 December, and lineage A a few weeks later, the group concludes. Either way, the team argues the almost simultaneous emergence of two lineages challenges the lab-origin thesis, as it would require two different viruses leaking at roughly the same time. (Gao and colleagues also found both SARS-CoV-2 lineages in their environmental samples.)

The second preprint from the international team builds on a June 2021 Chinese-led study that spent 2 years documenting a tick fever disease in mammals for sale at a specific stall in the market. The new study pinpoints for the first time where species susceptible to SARS-CoV-2—including raccoon dogs, hedgehogs, badgers, red foxes, and bamboo rats—were sold and maps those sites to the positive environmental samples, including in one “container” the authors believe was a cage. “To anyone who really grasps what is in all of those three papers, I think it’s very hard to dismiss that this is a very, very, very strong case that this pandemic started at that market,” Worobey says.

Others say they are not definitive. “They are interesting studies, but I don’t think they close the case on what happened with the origins of the virus,” says Jesse Bloom, an evolutionary biologist at the Fred Hutchinson Cancer Research Center who has criticized colleagues for too blithely dismissing the lab-origin hypothesis. “I’m especially skeptical of the conclusion that there must have been two zoonotic jumps.”

He notes that in about 10% of human transmissions of SARS-CoV-2, the virus acquires two mutations, which means a second lineage could have emerged after the infection of the first human rather than two zoonotic jumps. Worobey, Garry, and colleagues did a computer simulation that challenges Bloom’s assertion. They modeled what would have happened if there was an introduction of a single lineage and compared that with the viruses sequenced from Wuhan cases through 23 January 2020. By matching the sequence data from the actual epidemic, they found there was only a 3.6% chance that a single lineage mutated into a second one.

The environmental samples from the Wuhan market that tested positive for SARS-CoV-2 might resolve the stalemate over the virus’ origin if they can reveal a specific animal source of the virus. “If you find a positive sample with, say, lots of raccoon dog DNA, you’ve got a hit,” on the likely source of SARS-CoV-2, says evolutionary biologist David Robertson of the University of Glasgow, who co-authored the epicenter paper.

But the preprint by Gao and colleagues only notes that those samples contain DNA from many animals without specifying which one—other than humans. “The authors have already done the analysis, they have just not put all the results needed to interpret them in their paper,” says evolutionary biologist Andrew Rambaut of the University of Edinburgh, a co-author of both studies. “This will undoubtedly be fixed if the paper gets through peer review.”

Still, Worobey and his co-authors concede, even that evidence might not be enough to end this polarizing debate. “With the way that people have been able to just push aside any and all evidence that points away from a lab leak, I do fear that even if there were evidence from one of these samples that was full of red fox DNA and SARS-CoV-2 that people might say, ‘We still think it actually came from the handler of that red fox,’” Worobey says.

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doi: https://doi.org/10.1038/d41586-020-03564-y

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Clarification 17 December 2020 : This story now notes that preprints were posted on multiple sites, so estimates may represent slight overcounts.

Squazzoni, F. et al . Preprint at SSRN http://doi.org/10.2139/ssrn.3712813 (2020).

Aviv-Reuven, S. & Rosenfeld, A. Preprint at https://arxiv.org/abs/2010.02594 (2020).

Cai. X, Fry, C. V. & Wagner, C. Preprint at SSRN https://doi.org/10.2139/ssrn.3729672 (2020).

Huang, C. et al. Lancet 395 , 497–506 (2020).

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Ferguson, N. M. et al . Preprint at Spiral https://doi.org/10.25561/77482 (2020).

Vincent, M. J. et al. Virology J. 2 , 69 (2005).

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Andersen, K. G., Rambaut, A., Lipkin, W. I., Holmes, E. C. & Garry, R. F. Nature Med. 26 , 450–452 (2020).

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The varying impacts of COVID-19 and its related measures in the UK: A year in review

Roles Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Sociology, University of Oxford, Oxford, United Kingdom

Roles Funding acquisition, Writing – review & editing

We examine how the earnings, time use, and subjective wellbeing of different social groups changed at different stages/waves of the pandemic in the United Kingdom (UK). We analyze longitudinal data from the latest UK Household Longitudinal Survey (UKHLS) COVID study and the earlier waves of the UKHLS to investigate within-individual changes in labor income, paid work time, housework time, childcare time, and distress level during the three lockdown periods and the easing period between them (from April 2020 to late March 2021). We find that as the pandemic developed, COVID-19 and its related lockdown measures in the UK had unequal and varying impacts on people’s income, time use, and subjective well-being based on their gender, ethnicity, and educational level. In conclusion, the extent of the impacts of COVID-19 and COVID-induced measures as well as the speed at which these impacts developed, varied across social groups with different types of vulnerabilities.

Citation: Zhou M, Kan M-Y (2021) The varying impacts of COVID-19 and its related measures in the UK: A year in review. PLoS ONE 16(9): e0257286. https://doi.org/10.1371/journal.pone.0257286

Editor: Florian Fischer, Charite Universitatsmedizin Berlin, GERMANY

Received: October 13, 2020; Accepted: August 27, 2021; Published: September 29, 2021

Copyright: © 2021 Zhou, Kan. 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: All data files are available from the UK Data Service database (study number(s) 6641, 8644). Dat file URL: https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=8644 https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=6641 .

Funding: This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (awardee: Man-Yee Kan, grant number 771736). Funding website: https://ec.europa.eu/programmes/horizon2020/en . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

More than one year has passed since the United Kingdom (UK) officially announced its first national lockdown on 23 March 2020 due to the rapid spread of COVID-19. The outbreak of COVID-19 and the massive lockdown measures have greatly changed people’s lives. When people were instructed to stay at home and maintain physical distancing, the lives of millions of people were affected. For months, many people were unable to go to work or school, nor could they meet friends and relatives. What was unexpected was that people in the UK experienced a total of three national lockdowns over the past year. Now, people’s lives are far from what they were before the first lockdown, and the pandemic is still not over.

Recent evidence has shown that the COVID-19 pandemic and related social and economic measures, such as physical distancing and business closure, have differential impacts on various social groups. In the UK, for example, women and parents are found to have experienced a larger reduction in subjective wellbeing [ 1 , 2 ]. Black, Asian, and minority ethnic (BAME) immigrants were more likely to experience economic hardship immediately after the first national lockdown [ 3 ]. In addition, among those who were known to have COVID-19, people of BAME background in the UK had a death rate that was higher than that of white people [ 4 ]. As Damian Barr said in his poem, “we are in the same storm, but we are not all in the same boat [ 5 ]”.

These earlier findings identified the existence of immediate unequal impacts for different social groups, but our understanding of the longer-term impacts of COVID-19 and related measures remains limited. We know little about how the impacts might have changed since the first lockdown. The COVID-19 pandemic has already lasted for more than one year, and the UK has experienced three national lockdowns. Early research was confined by data that covered only two time points—such as before and shortly after the announcement of the first lockdown. Little is known about to how unequal social impacts reveal themselves at different stages of the COVID-19 pandemic, especially with repeated lockdowns. This omission hinders our understanding of how COVID-19 and COVID-induced social policies, such as physical distancing measures, working from home, and the closure of certain businesses, which have been changing on a weekly or even daily basis, progressively affect people’s lives. Documenting the development of the impacts of COVID-19 and COVID-induced measures is important for us to understand the consequences of this rapidly developing pandemic and help policymakers plan for future waves and future pandemics.

We need more comprehensive and up-to-date research on how inequalities have changed as the COVID-19 pandemic develops with repeated waves and the various measures to contain it were implemented over the past year. We conducted analyses on a nationally representative population data from the latest UK Household Longitudinal Survey (UKHLS), which was conducted before the first lockdown in March 2020, during the first lockdown from April to June 2020, during the ease of the first lockdown (June to September 2020), and during the later two lockdowns (November 2020, and from January 2021 to March 2021). In this paper, we contribute to COVID-19 research by providing a dynamic picture of how people’s labor earnings, time use, and wellbeing changed across different stages of the pandemic. We further investigated whether and the extent to which the inequalities in these outcomes based on gender, ethnicity, and educational level have changed over the past year.

In what follows, we first review the latest works on the impact of COVID-19 and COVID-induced measures on people’s lives, focusing on three dimensions of social inequality: gender, race/ethnicity, and education. We then outline the development of the COVID-19 pandemic and the lockdown measures in the UK from March 2020 to April 2021. Next, we introduce the data and its longitudinal design, which enables us to compare the information of the same individuals before the start of this pandemic and at different time points over the past year. Finally, we will report the results of fixed-effect regression analyses and discuss our conclusions.

The impacts of COVID-19 and its related measures

The COVID-19 pandemic has developed for over one year. In many countries, repeated waves of COVID-19 have been observed. The primary aim of COVID-19 induced measures is to contain the virus by reducing physical contacts between people. Many of these measures immediately affect people’s behaviors, but others could have longer-term impacts. For example, the closure of businesses and work-from-home guidance tremendously altered people’s working patterns. Reductions in paid work time and earnings have been immediately recorded in countries that have introduced lockdown measures such as Australia [ 6 ], the UK [ 3 , 7 ], and the United States (US) [ 8 ]. When more people stayed at home and the option of outsourcing domestic work was reduced due to business closure or the fear of contracting COVID-19, it is not surprising to see that people spent substantially more time on unpaid domestic work than they had in the past [ 6 , 7 , 9 , 10 ].

People’s feelings also changed. The contraction of COVID-19 is associated with a series of symptoms such as a high temperature, continuous cough and a loss or change to the sense of smell or taste. Serious cases will result in hospital admission and death. In the UK, the case-fatality rate is estimated to be 2.1% [ 11 ]. Daily news reporting the surging number of new cases and deaths brings in a high level of worry about health and security [ 2 ]. In addition, loss of employment, financial strain, and social isolation are well-known factors that negatively affect mental health [ 12 – 14 ]. Not surprisingly, soon after the start of the pandemic, worsened subjective wellbeing was observed in Australia [ 6 , 15 ], the UK [ 2 , 16 , 17 ], and the US [ 18 ]. Once daily increase of COVID-19 cases declined and the lockdown restrictions began to be lifted, people’s subjective wellbeing started to recover. As Pierce et al. [ 2 ] noted by using the first five waves of the same UKHLS COVID study data as in this paper, “[b]etween April and October 2020, the mental health of most UK adults remained resilient or returned to pre-pandemic levels.” However, “[a]round one in nine individuals had deteriorating or consistently poor mental health.”

This COVID-19 pandemic and its related measures have raised increasing concerns of exacerbated social inequalities. Since long before the pandemic, gender inequalities have existed in the labor market. In the UK, the labor force participation rate for men is higher than that for women, and men are also much more likely to work full time [ 9 , 19 ]. Women are more likely to be at-home workers. Reasons for this inequality include inflexible workplace expectations, gender norms expecting men to be the primary earners and women the primary caregivers, and discrimination in the labor market. When people are required to work from home, the spatial boundary between market work and family life is blurred. Many studies have investigated whether the changes in time use due to lockdown measures are the same for women and men. Between March and May 2020 (UK 1st lockdown), British men were found to be more likely to be furloughed or dismissed from work than women [ 20 ]. However, studies focusing on the labor market performance of parents reveal a different pattern. In the UK, during the first lockdown period from April to May 2020, among parents with children aged between 4 and 15, mothers were found to be more likely to be laid off, furloughed, or quit their jobs [ 21 ]. Similarly, in Australia [ 6 ], Canada [ 22 ], and the US [ 23 ], mothers with young children experienced a larger change in their paid work time or were more likely to leave their jobs. On the other hand, several studies have reported improvements in the domestic division of labor: the increase in domestic work was larger for men than for women during the lockdown period in Australia [ 6 ], Canada [ 24 ], France [ 25 ], and the US [ 26 ]. However, contrary results were reported in Germany [ 27 ] and Spain [ 28 ]. The decline in subjective wellbeing also differs between women and men. In the UK and Australia, women were found to experience a larger reduction in subjective wellbeing than men [ 1 , 2 , 6 , 9 , 29 ].

In the UK, BAME immigrants were more likely to experience economic hardship just after the first lockdown [ 3 ]. Compared with their white counterparts, BAME immigrants were also found to suffer a larger decline in subjective wellbeing at the beginning of the March 2020 lockdown in the UK [ 3 , 30 ]. In the US state of Indiana, Black Americans were more than three times more likely to lose their jobs than whites [ 31 ]. In contrast, another study highlights that white Britons in middle-income jobs were more likely to experience job loss, primarily driven by the fact that many BAME people are employed in key sectors such as the health and social care services, which were exempt from the lockdown measures and instead had a surge in work demands, during the first UK lockdown [ 20 ]. Notably, in the UK, people of BAME backgrounds had a death rate that was higher than that of white people after they were confirmed to have COVID-19 [ 4 ].

People with less education and lower income suffered substantially during the pandemic. They were particularly hit hard with a higher chance of losing their jobs and earnings in countries such as Canada [ 32 ], the UK [ 20 ], and the US [ 31 ]. Many of the less educated are trapped in lower-skilled occupations with tight financial constraints. Consequently, the less educated group reported a heightened level of distress during the first lockdown in the UK [ 33 ]. However, one US study reports that the decline in subjective wellbeing up to April 2020 was larger among the more educated, possibly because the more educated might have felt a greater loss of control and wealth due to COVID-19-related uncertainties [ 18 ]. Another study conducted in the US between April 2020 and June 2021 pointed out that part of the reason for the deterioration of mental health results should be attributed to the concurrent presidential election and unrest in domestic politics [ 34 ].

Again, the current literature has focused extensively on the impacts of the relatively early stage of this pandemic. In particular, studies that have employed the same British data source as the present study have examined the changes in earnings, time use, and subjective wellbeing during the implementation of the first national lockdown in late March 2020 [ 3 , 7 , 9 , 10 , 20 ]. Pierce et al.’s work [ 2 ] on subjective wellbeing is an exception. Their work examined the recovery of subjective wellbeing when the first lockdown measures were eased from June to October 2020. However, their study did not cover the later lockdowns in November 2020 and January 2021. In this article, we will provide a first-year review of COVID-19 development in the UK and document how people have responded to the first lockdown, the ease of the first lockdown, and the later two lockdowns. This evaluation will reveal whether people responded similarly to repeated lockdowns and whether these changes in earnings, time use, and feelings are temporary or long-lasting.

Timeline of the lockdown measures in the UK

On 31 January 2020, the first two positive cases of COVID-19 were confirmed in the UK. On 5 March 2020, the first patient who tested positive for COVID-19 died. On 23 March 2020, the Prime Minister placed the UK on lockdown to slow down the outbreak of this pandemic. These measures included physical distancing, school closures, working from home, and closure of non-essential businesses, including pubs and cafes. Key sectors, including health and social care, education and childcare, and key public services, were allowed to operate.

To maintain employment and to protect individuals and businesses from economic hardship, a coronavirus job retention scheme was implemented for the period between late March and the end of October 2021 to cover 80 percent of the regular salary of furloughed employees, up to a maximum of £2,500 per month [ 35 ]. In April, the UK had more than 10,000 deaths related to COVID-19. In May, phased reopening of shops and schools was announced, and those who were unable to work from home were expected to return to the workplace.

Beginning on 1 June 2020, schools were open for all Reception, Year 1 and Year 6 pupils, but the summer holiday soon arrived. Nonessential businesses reopened gradually beginning on 15 June. Beginning on 4 July, pubs, cinemas, restaurants reopened. Physical distancing rules were relaxed from a “two-meter” to a “one-meter plus” rule. In August, restrictions were eased further, although the pandemic was far from over.

The UK variant of the coronavirus (scientific name B.1.1.7, WHO name Alpha) was first identified in September 2020 and was considered to be more transmissible and potentially deadlier. In late September, people were required to work from home with a 10 pm curfew for the hospitality sector. In October, England entered a 3-tier system where different regions were classified into different tiers depending on the level of the spread of the virus. Soon after, the second national lockdown came into force on 5 November and lasted until 2 December. People were told to stay at home. Other measures included the closure of the hospitality sector and nonessential shops, but schools were open, and people could leave their home for outdoor exercise. After 2 December, the UK then entered a stricter 3-tier restriction system.

However, this 3-tier system did not last long. After Scotland announced a lockdown, on 4 January 2021, a third national lockdown was announced. Schools were closed again, and people were urged to stay at home. This time, the measures were stricter than those in the second lockdown. They included “Stay at home at all times, wherever possible,” “Not allowed to meet others from outside your household (or support bubble),” “All retail and hospitality venues must close,” and “Personal care services have to close.” Schools were closed to most pupils, except for the children of critical workers and the most vulnerable children. Nurseries were kept open.

Since 8 March, schools in the UK have been completely reopened. Nonessential retail and personal care services have been reopened since 12 April. People have been allowed to meet outdoors, as a number of restrictive measures have been lifted since 17 May. A complete easing will occur on 19 July 2021. The Prime Minister has pledged that all adults in the UK will be offered their first dose of a COVID-19 vaccine by the end of July.

By 16 April 2021, the recorded number of deaths related to COVID-19 had reached over 127,000 in the UK. Fig 1 displays the spread of COVID-19 and related deaths in the UK during the research period. A more detailed timeline of the UK lockdowns can be found at [ https://www.instituteforgovernment.org.uk/sites/default/files/timeline-lockdown-web.pdf ]. Fig 1 shows the development of the COVID-19 pandemic in the UK based on data provided by the UK government.

Note: Data source: https://coronavirus.data.gov.uk/details . Crude death rate is new deaths within 28 days of a positive test per 100,000 population.

https://doi.org/10.1371/journal.pone.0257286.g001

Data and methods

Data and sample.

We use data from the first eight waves of the UKHLS COVID study data and the preceding two waves (2017/18 and 2018/19) of the UKHLS main survey [ 36 ]. The UKHLS is a household panel survey and started its first wave in 2009 with a nationally representative sample of 51,000 adults (aged 16 and above) from approximately 40,000 households. Individuals were followed up annually and were interviewed face-to-face. This research is based completely on the UKHLS data that are publicly available through the UK Data Service (Study numbers: 6614 and 8644) and are completely anonymous.

Regarding the COVID study, households who participated in previous UKHLS surveys were contacted to fill in a monthly online questionnaire beginning in April 2020. The complementary telephone survey started in May 2020. Participation in the survey was voluntary. Approximately 16,000 respondents (aged 16 and above) completed this first wave of the COVID survey with a response rate of 42%. Currently, data from the first eight waves of surveys conducted in the last week in April, May, June, July, September, November in 2020 and the last week in January and March in 2021 are available.

Our analytic sample contains individuals who have participated in the UKHLS main survey and at least one of the eight waves of the COVID study. The respondents all had access to the internet or telephone to participate in the surveys. This requirement might have caused a sample selection bias. In a supplementary analysis, the sample from the COVID study is found to be socioeconomically advantaged in terms of employment, occupation, education, and homeownership compared to the full UKHLS sample. If we assume that one’s socioeconomic status has a protective effect on the negative consequences of the COVID-19 and related lockdown measures, the reported results may underestimate the potential negative impacts of the COVID-19 and the related lockdown. Nonetheless, one paper discusses this issue of nonrandom sample selection and demonstrates that the bias due to sample selection is very limited once weight is considered [ 37 ]. In the following analysis, we apply the individual weights, which were adjusted for “unequal selection probabilities and differential nonresponse” and are supplied in the data [ 38 ]. Based on the User Guide for the data, these weights “scale respondents to the eligible population in the UKHLS wave 9 sample, adjusted for death, incapacity and emigration occurring between wave 9 and the start of the COVID-19 web survey.” [ 38 ] This approach has been used in previous work analyzing the same data [ 2 , 3 , 20 ].

Our sample includes respondents of prime working age (between 20 and 65) in 2020. Two percent of the UKHLS COVID sample has missing values in the predictors to be used in regressions. The numbers of observations with no missing predictors are 10484, 9008, 8478, 8210, 7642, 7083, 7019, and 7525 in the first eight waves of the COVID study. The final sample for each regression is dependent on the outcome variables with nonmissing values (some outcome variables are not asked in certain waves) and the selection of subgroups (for example, people who had a job before the pandemic). Please refer to S1 Table for more details of the sample selection process. The focus on within-individual changes in the outcome variables indicates that the respondents should be followed up for more than one wave. Previous analyses using the same data and selecting the individuals interviewed for more than one wave do not find that this selection would bias the results [ 39 ].

Monthly labor income, weekly paid work hours, subjective wellbeing, weekly housework hours, and weekly childcare hours are the five dependent variables or outcomes of interest.

Monthly labor income.

Respondents’ labor income in January or February 2020 (before the lockdown) was collected retrospectively in the COVID survey. Respondents also provided their current labor income in each month thereafter. We calculate the natural log of the labor income. Those who had a job in January or February 2020 were selected to predict this outcome.

Weekly paid work hours.

Respondents retrospectively reported their current paid work hours per week and their usual working hours in January or February 2020. During the period of the COVID-19 pandemic, the question asked was “How many hours did you work, as an employee or self-employed, last week?” During the prepandemic period, the question was “During January and February 2020, how many hours did you usually work per week?” Those who had a job in January or February 2020 were selected to predict this outcome.

Subjective wellbeing.

Subjective wellbeing is the mental wellbeing reported by the respondents in a General Health Questionnaire (GHQ). The value is the sum of 12 items (GHQ-12) scored on a Likert scale from 0 to 3: “ability to concentrate,” “losing sleep,” “playing a useful role in life,” “capability of making decisions,” “feeling under stress,” “overcoming difficulties,” “ability to enjoy activities,” “ability to face problems,” “feeling unhappy or depressed,” “losing confidence,” “believing in self-worth,” and “feeling generally happy.” The overall scale ranges from 0 (least distressed) to 36 (most distressed). This measurement is a validated and widely used measure of nonspecific mental distress in surveys [ 40 ]. The same information was collected in earlier waves of the main survey of the UKHLS and in each wave of the COVID study. The full sample was used to predict this outcome.

Weekly housework hours.

Respondents’ weekly housework hours were collected by the question “Thinking about last week, how much time did you spend on housework, such as time spent cooking, cleaning and doing the laundry?” Information about housework hours before the COVID survey was derived from the earlier UKHLS waves (the latest one was collected in the years between 2018 and 2019). The full sample was used to predict this outcome.

Weekly childcare hours.

Respondents’ childcare hours were collected by the question “About how many hours did you spend on childcare or home-schooling last week?” This information is only available in the COVID survey. Only those who had a child younger than 16 years old in the household (referred to as parents in later analyses) were asked this question, and these respondents are used for analyses.

Independent variables.

We include the wave dummies, which represent the time point when information was collected to examine the dynamics in those outcome variables.

The key socioeconomic independent variables are constant for the same individual across the waves. These variables are gender (52.7% females), whether an individual is Black, Asian or another minority ethnic (10.1%) or not (reference group: whites), and educational level (university degree holders 32.2%). The underrepresentation of ethnic minority groups is common in a panel survey sample (the 2011 census reported that 85.6% of the working-age people were from white ethnic groups) because of the selection of people with repeated observations to satisfy the requirement of the fixed-effect models. People with disadvantaged backgrounds are known to be more likely to drop out in repeated surveys [ 41 ]. The later regression analysis has considered this sample selection issue using weights, as discussed above. Moreover, attrition in panel surveys is not found to have a significant impact on the estimations in predicting income [ 42 ], time use [ 43 ], or attitudes [ 44 ].

Whether the respondent had a positive COVID-19 test outcome was asked in each wave. We included this variable in the model to control for the impact of contracting COVID-19 so that the period indicators could better represent the spread of COVID-19 and COVID-19-related policy change at the macro-level. This variable has four categories: “having no test” (reference, 89.7%), “tested positive” (0.8%), “tested negative” (9.0%), and “result pending” (0.5%).

All models controlled for respondents’ partnership status (whether they live with a partner) and parenthood status (the presence of a child younger than age 16 in the household) to account for potential changes in the family status that are correlated with the outcomes [ 45 , 46 ].

Analytical strategies

We applied linear fixed-effect regressions to predict the five outcomes. By interacting the month indicator with gender, BAME group, and education levels, we examined how the change in income, time use, and wellbeing differed across individuals in the three different sociodemographic groups in different periods of the pandemic. The reference time point is January and February 2020 for earnings and weekly paid work hours outcomes. The reference time point is the year 2018/2019 for the subjective wellbeing (distress level) and weekly housework hours outcomes. For weekly childcare hours, the reference time point is April 2020, which was during the first national lockdown. The outcome variables compare the information reported by the same individuals at each time point and hence reveal within-person changes. This analytic approach enabled us to investigate trajectories of the outcome variables over the past year conditional on the same individual.

The fixed-effect regression method takes full account of the time-constant individual characteristics that are correlated with both the independent variable and the outcome variables. This is achieved by demeaning the dependent and independent variables using person-specific means [ 47 ].

The samples in the UKHLS main survey and the COVID survey are probability samples of postal addresses. The samples are clustered and stratified. Accordingly, clustered standard errors are used to consider this sampling design [ 48 ].

These analyses were conducted in Stata/SE 16.1. Replication codes are available at https://github.com/jomuzhi/ukcovidunderstandingsociety .

Descriptive results

We first report the weighted mean values of the key outcomes in Table 1 . Please note that the information was collected at the end of each survey month.

https://doi.org/10.1371/journal.pone.0257286.t001

First, among those who worked before this pandemic (between January and February 2020), there was a clear reduction in their average earnings when the pandemic started in the UK. Their income recovered by almost ten percent in May from the April level, which should have been mainly driven by the implementation of the job retention scheme . Some workers who could not work from home, such as those working on construction sites, also returned to the workplace in May. Since then, average monthly net earnings have remained at approximately the level of £1,550. Notably, since the first lockdown, people’s take-home earnings has never returned to their prepandemic level but never fell below 90% of the pre-pandemic level.

Before the pandemic, those who worked in January and February 2020 worked 34.7 hours per week on average. A record low of 21.9 hours per week was observed in April 2020. The persistent decline in paid work time over the past year is evident, although working hours have recovered gradually since May and reached a peak of approximately 30 hours per week in September 2020. The later two national lockdowns (November 2020 and January 2021) did not reduce the working hours as much as the first national lockdown. Weekly paid work hours were maintained at approximately 28 hours.

People felt more distressed beginning in March 2020. The worst number of 13.4 was recorded in the last two rounds of lockdown-November 2020 and January 2021, when new cases and deaths grew sharply at the beginning of these lockdowns.

People’s housework hours increased and reached the highest level of 12.3 hours per week in April and May 2020. Then, housework time declined gradually and was maintained at 10.5 hours per week. Compared with the figure recorded in September 2020 when most lockdown restrictions were eased, the figure in January 2021 did not change significantly, even though a stricter lockdown was in place. This finding concurs with the small reduction in paid work hours from September 2020 to January 2021.

The average childcare hours per week reached 16.7 hours for parents in April, but this figure gradually declined to approximately 13 hours per week before the third national lockdown. In January 2021, childcare hours only increased 0.5 hours per week over the September figure, even though schools were closed to most pupils during the third lockdown. Overall, people’s time use had become less responsive to repeated lockdowns.

Changes in earnings, paid work time, subjective wellbeing, housework and childcare time

Fig 2 reports within-individual changes in earnings, paid work hours, distress level, and housework hours across waves. The red lines indicate the time point when the national lockdowns started to enforce. Please note that the information was collected at the end of each survey month. Detailed coefficients are reported in S2 Table .

https://doi.org/10.1371/journal.pone.0257286.g002

Respondents’ earnings stayed lower than the pre-pandemic level over the entire year, with the largest decline (~9%) recorded in late April, the first month after the announcement of the first national lockdown. Earnings recovered slightly after the gradual relaxation of restrictive measures and the implementation of the job retention scheme. Following the third lockdown, when almost the same strict measures as the first lockdown were imposed, we found a similar level of decline in earnings (~8%) compared with the prepandemic period, as in the first lockdown. One year after the onset of the pandemic in the UK, our sample still experienced a 7.4% decline in earnings compared with the pre-pandemic level.

Paid work hours remained much lower than the prepandemic level over the entire year. The largest drop of nearly 13 hours was observed in the first month after the March 2020 lockdown. Then, paid work hours recovered and have never returned to the same lowest point. People worked the longest hours in September 2020, when restrictive measures were minimal. Interestingly, despite the implementation of the second and the stricter third national lockdowns, paid work hours dropped only slightly compared to the September figure and were even higher than the July 2020 figure, even though all shops were allowed to open back in July 2020. This observation suggests an increased adaptation to the work-from-home practice. After the first lockdown, more firms announced a long-term strategy to allow employees to work from home [ 49 ]. Accordingly, people have increased their paid work time even though they might still work from home.

In this pandemic, people’s subjective well-being has been damaged. The distress level (a higher score indicating more distress) stayed higher than the prepandemic level over the past year. In the three-month period after the first lockdown, a high level of distress was recorded. An improvement in subjective wellbeing was observed from July and before the enforcement of the second lockdown. The November lockdown brought a further decline in subjective wellbeing, which is consistent with the findings in one earlier study [ 2 ]. The distress level in November 2020 and January 2021 was even higher than that in the first lockdown period. It appears that people were much less optimistic and suffered tremendously as the pandemic dragged longer. People became slightly less negatively affected in their subjective wellbeing in March 2021, although the level was only similar to that in April 2020. One year after the onset of the pandemic in the UK, respondents’ subjective wellbeing returned to the level of April 2020, which was one month after the announcement of the first national lockdown.

The increase in housework hours was the highest during the first lockdown. Compared with the housework hours during the easing period in September 2020, the January 2021 lockdown was not associated with an increase in people’s housework time. This change echoes the relatively high level of paid work time in the later two lockdown periods.

Next, we examine childcare time since the first national lockdown. In Fig 3 , we can see that beginning in April 2020 (during the first lockdown period), childcare hours have been dropping. The lowest level was observed in September 2020, when schools completely reopened. Interestingly, childcare hours in January 2021 were similar to those in September 2020, despite the closure of schools to most children in January 2021.

https://doi.org/10.1371/journal.pone.0257286.g003

Differential impacts on women and men

Figs 4 and 5 report whether changes in the five indicators differ between women and men. For monthly net earnings and weekly paid work hours, we analyzed an additional sample that includes only non-key workers. We will examine whether a disproportionate number of female workers in certain key sectors, such as health and social care, drive the results.

https://doi.org/10.1371/journal.pone.0257286.g004

https://doi.org/10.1371/journal.pone.0257286.g005

First, the reduction in earnings for female workers (those who worked in Jan/Feb 2020) was smaller than that for male workers during the first lockdown in April 2020 (p = 0.011). Since then, there has been no difference between women and men in changes in earnings, reflecting the faster recovery of men’s earnings. Differential impacts on women and men were not found among non-keyworkers. Therefore, the higher proportion of women working in key sectors, which were operating much more actively than other sectors during the first lockdown period, should be the main reason for the gender difference in the earning decline during the first lockdown.

During the first lockdown, the decline in paid work hours was smaller for female workers than for male workers, disregarding their keyworker status (p<0.001). The gender difference in the reduction in paid work hours decreased as the first lockdown ended and became statistically insignificant at the 0.05 level from July to September 2020, indicating a faster recovery of paid work time for men than for women. The differential impacts of gender on paid work hours observed in the first lockdown were not observed in later lockdowns among non-keyworkers.

In Fig 5 , the growth in distress level was much higher for women than for men in the first month of the first lockdown (p<0.001). Then, women’s subjective wellbeing recovered, and men’s distress levels began to rise. These findings suggest that men’s response to this pandemic lagged behind that of women in terms of their subjective wellbeing in the first lockdown. The distress level of both women and men was reduced to the lowest level from July to September 2020, when life in general had returned to normal. Once the cases of COVID-19 surged and lockdown restrictions were reimposed in November 2020 (p = 0.056) and January 2021 (p = 0.061), women again suffered from a larger increase in distress levels than men. The distress level of women reached a similar high point across the three lockdowns. For men, their distress level was higher in the later lockdowns than in the first lockdown, when the cases of COVID-19 and its related deaths worsened.

We do not observe a gender-specific impact on housework time. The gender gap in housework time was maintained over the past year.

Differential impacts on BAME people and white people

Figs 6 and 7 report whether changes in the five indicators differ between BAME people and whites. For monthly net earnings and weekly paid work hours, we analyzed an additional sample that includes only non-key workers.

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Compared with whites, the earnings of the BAME group were particularly negatively affected by the pandemic. The differential impacts on earnings persisted across almost all months over the past year, except during the third lockdown. The gap was large even when most lockdown restrictions were eased in September 2020 (p = 0.003). The earning gap between the BAME group and whites was even larger among non-key workers. Over the past year, the decline in market working time was similar for the BAME group and whites in both the full and the non-key worker samples. In March 2021, the reduction in paid work time decreased less for the BAME group than for the whites (p = 0.006).

Regarding the distress level ( Fig 7 ), the increase for the BAME group was larger than that for whites during the first lockdown, but the difference was not statistically significant at the 0.05 level due to the large standard error of the estimates of the BAME group. Beginning in September 2020, the changes in the distress levels were similar for the BAME group and whites. The increase in housework hours seems to be larger for the BAME group, but the large standard errors prevent us from drawing a reliable conclusion.

Differential impacts on degree and non-degree holders

Figs 8 and 9 report whether changes in the five indicators differ between degree and non-degree holders. For monthly net earnings and weekly paid work hours, we analyzed an additional sample that includes only non-key workers.

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As expected, the decline in earnings and paid work hours was particularly acute among non-degree holders. These differential impacts were even larger among non-key workers. When the spread of the virus decreased and most of the restrictive measures eased from July to September 2020, the difference in the impacts on non-degree and degree holders became smaller but was sustained. For paid work hours, the difference was insignificant between July and September 2020 for both the full and the non-keyworker samples. Once restrictive measures were reimposed, the difference became substantial again (p<0.001).

As Fig 9 shows, there was no significant difference in the change in subjective wellbeing between degree and non-degree holders before January 2021. However, degree holders experienced a larger increase in distress level during the third national lockdown that started in January 2021 (p = 0.028), but the differential effect disappeared in March 2021.

We do not observe a statistically significant difference in the changes in housework time between the two groups.

Changes in weekly childcare hours since April 2020

Fig 10 reports whether changes in the weekly childcare hours differ across these groups.

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Our findings show that women and men, BAME people and whites, and degree and non-degree holders did not differ significantly in changes to their childcare time since April 2020. However, there is a tendency that the reduction in childcare time in September, which should be associated with pupils returning to schools after summer vacation, was larger for mothers and the more educated group, suggesting that women and the more educated might have spent more time taking care of children at home.

For more details of the results, please refer to S2 – S5 Tables. The within-individual R-squares are small when predicting earnings, subjective wellbeing, housework time, and childcare time. Small within-individual R-squares are not uncommon in fixed-effect regressions, especially when predicting housework time and subjective wellbeing [ 50 , 51 ]. These results suggest that a limited number of individuals have changed their partnership and parenthood status and COVID-test results, but their outcome variables—earnings, time use, and subjective wellbeing-have changed considerably over the past year. The inclusion of more time-varying variables might be able to improve the explanatory power. Those variables could be whether furloughed, whether participated in the job retention scheme, or whether went back to work/school. However, the purpose of this paper is to provide an overall net impact of COVID-19 and its related measures on an individual instead of focusing on a specific policy or the spread of COVID-19. Given the focus on the trajectories of earnings, time use, and subjective wellbeing at different stages of the pandemic, we do not include those time-varying variables suggested above.

Discussion and conclusion

In this article, we have utilized the latest UK COVID panel data to provide a comprehensive analysis of the dynamics of earnings, time use, and subjective wellbeing at different stages of the pandemic over the past year. Our research, with a much extended time scope, surpasses past UK studies that only followed a short period after the first lockdown imposed in March 2020 [for example, 3, 7, 9, 20]. Our analysis has incorporated multiple domains of outcomes across several social groups. We aim to examine how the spread of COVID-19 and COVID-induced policies have had unequal and dynamic impacts on different social groups in the UK. Our findings offer important insights into whether inequalities in changes in income, time use and wellbeing are likely to be long lasting or temporary.

Overall, the initial outbreak of COVID-19 and the first national lockdown brought the largest change in earnings and time use. The later two lockdowns together with the repeated new highs of the COVID-19 cases and deaths impacted people’s subjective wellbeing the most. Although strict measures that aimed to reduce people’s physical contact were imposed in the later two lockdowns, people’s time use did not respond as strongly as they did during the first lockdown. Among the five indicators, none had returned to their prepandemic level until late March 2021. It remains uncertain when and whether earnings, working patterns, family life, and subjective wellbeing will return to the prepandemic level.

Female workers experienced less reduction in their earnings than male workers, which is largely due to the relatively high proportion of women working in key sectors, especially in the health and social care industry. Women have made an important contribution to the fight against COVID-19 by working in key sectors. However, even among non-key workers, the decline in paid work hours was smaller for women but only during the first lockdown period. These findings concur with earlier research that reported that men in the UK were more likely than women to be laid off or furloughed during the first lockdown [ 20 ]. Once lockdown measures were gradually lifted beginning in June 2020, men’s paid work time recovered faster than that of women. This finding is similar to previous work on the gendered impact of natural disasters on market labor [ 52 ]. In summary, our analysis has shown that in the UK, men’s paid work time was more responsive to the restrictive measures of the first lockdown, but women’s and men’s paid work time responded similarly in the later two lockdowns.

The subjective wellbeing of women was more sensitive to the outbreak of COVID-19 and related lockdown measures than that of men. For example, the increase in women’s distress level was substantial in April, but it then gradually improved until the next lockdown. Men’s responses lagged behind of those of women. Past COVID-19 research has highlighted the gender difference in social networks, where women tend to have more friends [ 29 ]. The larger exposure to news related to COVID-19 for those with more close friends might be the factor that explains the diverging trajectories of women’s and men’s subjective wellbeing [ 53 , 54 ]. Theses differential impacts became smaller in later two lockdowns, as the pandemic had developed for a certain period. At the beginning of the pandemic, women and men seemed to have perceived the danger of this infectious disease differently.

The gender gap in housework time was maintained over the past year. Overall, the gender-specific changes in earnings, paid work time, and subjective wellbeing were mainly observed when strict restrictions were in place, and the gender gap returned to its prepandemic level once those measures were lifted.

People of a BAME background experienced a larger loss in earnings than whites. This finding is consistent with an earlier finding on BAME immigrants in the UK [ 3 ]. We have further shown that the enlarged earning gaps between BAME and white people persisted almost over the entire year.

Persistently enlarged earning gaps were observed between non-degree and degree holders. The gap was even larger among non-key workers. Non-degree holders suffered from a larger reduction in earnings across all months over the past year. This gap was particularly large during the national lockdown periods. A similar observation was found for weekly paid work hours. The spread of COVID-19 and lockdown restrictions are associated with an enlarged gap in paid work time between non-degree and degree holders. This effect on paid work time is likely to be temporary because differential impacts were not observed from July to September 2020, when lockdown measures were mostly lifted.

One limitation of this study is that some changes could be brought by seasonal fluctuations beyond COVID-19 and its related restrictions. For example, people’s paid work time in winter may differ from that in summer. General psychological health was usually worse in winter than in summer [ 55 ]. The ideal solution is to compare information collected in the same month before the pandemic and in 2020. However, this approach is not possible with the current data. If the current survey retains the current monthly or bimonthly data collection frequency, future work can compare the same month in 2020 and the years after to examine pandemic and post-pandemic differences. We have also included the measure of the spread of COVID-19 (daily new cases or daily new death rates, as shown in Fig 1 ) to examine whether the outcomes are affected by the macrolevel development of the COVID-19 pandemic in the UK. We do not find strong evidence showing that those measures are associated with the outcomes. Our results reveal the trajectories of earnings, time use, and subjective wellbeing at different time points over the past year but cannot identify the exact impact of a specific lockdown restrictive policy. There could be other non-COVID-19-related policy updates that occurred in parallel over the past year that may have had an impact on the same outcomes. Nonetheless, the trends of the observed changes in income, time use, and subjective wellbeing corresponded closely to the different waves of the pandemic and the lockdown timeline. Therefore, the major sources of those changes should be related to the spread of COVID-19 and its related lockdown measures.

In conclusion, our findings suggest that the long-lasting pandemic and the related restrictions to contain the virus over the past year have produced persistent negative consequences for earnings, work patterns, and subjective wellbeing. The spread of COVID-19 and the national lockdowns at different stages had distinct patterns and measures, and their impacts on labor earnings, time use and subjective well-being varied. Time use patterns became less sensitive to the later lockdowns, but the distress levels reached a new high with repeated lockdowns in multiple waves of the pandemic. The differential impacts of the lockdown measures based on gender became insignificant once lockdown measures were lifted. However, some social groups, including BAME and white people and non-degree holders and degree holders, experienced persistently enlarged gaps in earnings. The negative impacts of the spread of COVID-19 and its related measures vary not only in their extent but also in their speed among different social groups. Further research should be conducted to understand factors that have driven these social inequalities and to monitor how inequalities based on gender, educational level, and ethnic minority status might be persistent or even exacerbated in the long term.

Supporting information

S1 table. samples and sample selection..

https://doi.org/10.1371/journal.pone.0257286.s001

S2 Table. Baseline model: Changes in the five indicators across waves.

https://doi.org/10.1371/journal.pone.0257286.s002

S3 Table. Gender and period interaction model results.

https://doi.org/10.1371/journal.pone.0257286.s003

S4 Table. Ethnicity and period interaction models.

https://doi.org/10.1371/journal.pone.0257286.s004

S5 Table. Education and period interaction model results.

https://doi.org/10.1371/journal.pone.0257286.s005

Welcome to Rapid Reviews: Infectious Diseases !

RR\ID is an open-access overlay journal that accelerates peer review of important infectious disease-related research preprints. We are an evolution of Rapid Reviews: COVID-19 (RR\C19) , which won the 2022 PROSE Award for Innovation in Journal Publishing for introducing a new peer review model facilitating rapid publishing of COVID-19-related research preprints. RR\ID expands beyond COVID-19 to advance our understanding of infectious diseases, leaning on a similar “curate, review, publish” model. RR\ID aims to prevent the dissemination of false/misleading scientific information and accelerate the validation and diffusion of robust findings. RR\ID aims to increase the application of science for the common good, responding to infectious disease challenges throughout the world.

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Opinion article, does peer reviewing for covid-19-related papers still work.

Introduction

In this article, we aimed to analyze whether coronavirus disease 2019 (COVID-19)-associated articles were being subjected to the same standards of peer-review as non-COVID-19 articles. In order to do this, we taught eight PhD students manuscript reviewing skills and analyzed eight papers published in valued journals, five of them on COVID-19. Each selected publication was reviewed by at least two graduate students from a Scientific English class and two scientists in charge of the course at the Faculty of Medicine, Universidad de Chile. Several shortcomings were identified in the revised studies, particularly on those related to COVID-19, which led us to conclude that the emergency imposed by the COVID-19 has endangered the quality of the accepted studies.

The COVID-19 pandemic caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread throughout the world. This virus is killing many people, and taking a massive physical, as well as mental toll, on the lives of all those that have been infected ( Yi et al., 2020 ). The COVID-19 pandemic has severely depressed every country’s economy because governments have been obliged to apply quarantine measures to control the disease. Thus, the impact has not only been on people’s health, but also on their lifestyle and economic situation ( Nicola et al., 2020 ). For these same reasons, many people, including politicians and leaders from different countries, have turned to the scientific community for answers regarding actions that need to be taken to control and treat the disease. Nevertheless, many scientific studies published these days concerning the COVID-19 virus, even those reported by important journals, fall short on experimental evidence to support their conclusions. It is clear that great pressure exists to rapidly know more about this virus and how to stop the pandemic. Notwithstanding, we believe that this is leading editors and reviewers to accept manuscripts that would have never been considered for publication under different circumstances. It is not bad science, it is just not the complete story; the story that the good, high impact journals would normally ask for, when peer reviewing manuscripts for publication.

Article Analysis

In a Scientific English course for postgraduate students, we taught them how to review a manuscript and gave them several scientific papers on COVID-19 published in prestigious journals. Their task was to elaborate a critique, according to the instructions given by the professors. Additionally, non-COVID-19 articles (but of related subtopics) from similar journals were reviewed by the students as controls. All papers were evaluated by at least two students and reviewed by the two scientists in charge of the course.

Considering that: i) journals have received a wealth of manuscripts on COVID-19 and therefore, accelerated the publication reviewing process to allow faster publication, and dissemination of information (pandemic publishing) ( Kwon, 2020 ); ii) the worldwide daily confirmed peak of COVID-19 deaths was at the middle of April 2020 ( https://ourworldindata.org/grapher/daily-covid-deaths-region ), and started rising in December 2019; and iii) data published in a middle-to-high impact journal (IF > 9; 2019) can cause more damage to the public because it is easier to consider the information as reliable and valid ( Kwon, 2020 ), the papers were selected based on the following criteria: i) paper main topic was on COVID-19; ii) publication date was between January–June 2020; iii) articles were mainly brief reports; only one research article was selected; iv) papers were published by journals with an IF > 9.4 (2019). Additionally, as controls, we selected three papers meeting the same criteria, except they did not cover COVID-19.

To perform the critique, we used the criteria described under the sub-item “Critique” in Table 1 . These criteria included: structure of the paper, data collection, appropriate methods and controls to gather the evidence, analysis and interpretation of evidence lead the reader to similar conclusions than the authors.

TABLE 1 . Criteria used to evaluate the work performed by the students. Peer reviewing was performed as suggested by published literature ( Benos et al., 2003 ; McPeek et al., 2009 ; Lippi, 2018 ).

A paper written by Emmie de Wit and colleagues and published by PNAS in February 2020 ( de Wit et al., 2020 ) was reviewed by all students. In this paper, the authors reported that prophylactic treatment with the antiviral drug Remdesivir prevented clinical manifestations in the lungs of Rhesus macaques infected with MERS-CoV, and provided a clear clinical benefit when the drug was administered post infection. They suggest that Remdesivir could be useful in the treatment of other coronaviruses such as SARS-CoV-2, the virus responsible for COVID-19. Although the article is well backed up, there were a number of important concerns. First, the number of animals in each group of the study was very small (six), which led to a considerable variation in the results observed, making the reported therapeutic effect of Remdesivir questionable. In addition, although the authors had two different types of vehicle control groups (three animals/group), the results were treated as if these two groups were the same. Furthermore, the study lacked a control group without viral inoculation. Additionally, only male animals were used, despite the evidence indicating that adverse effects may vary between male and female animals ( Klein, 2012 ). Finally, important clinical details about the macaques, such as their age—which is known to influence MERS-CoV infection outcome ( Garbati et al., 2016 )—weight, physical activity, presence of chronic diseases, etc., should have been indicated. Yet another limitation of this publication was the absence of toxicity assays. Measurements of renal clearance, liver or renal damage, and determinations of Remdesivir side effects, such as nausea, should have been included. Similar concerns were raised in the rest of the articles analyzed.

In another report by Giamarellos-Bourboulis and colleagues published in Cell Host & Microbe in April 2020 ( Giamarellos-Bourboulis et al., 2020 ), the authors describe a unique signature of the immune response, different from that induced by bacterial community-acquired pneumonia sepsis or H1N1 influenza, which precedes severe respiratory failure in COVID-19 patients. The common critique to this study was the lack of healthy controls in some of the experiments, which appeared in only a few of the comparisons performed throughout the article. Healthy controls should have been included in all the analyses, and their clinical data, provided. Additionally, some of the conclusions were drawn using data that appears as non-significant in the corresponding graphs. For example, Figure 2E shows no statistically significant differences between B lymphocyte counts, when comparing immune dysregulated patient samples and intermediate state patients or healthy controls. Therefore, the authors should not have drawn the conclusion of lymphopenia as a characteristic of COVID-19 patients with immune dysregulation. Moreover, many of the figures have high data dispersion, and some of them even show outliers. Statistical outcomes obtained using data sets that include outliers can often be misleading and compromise the generalizability of the research findings ( Salgado et al., 2016 ).

Another example is the study by Hoffmann and colleagues published in Molecular Cell in April 2020 ( Hoffmann et al., 2020 ). The authors demonstrate the importance of a multibasic site in the SARS-CoV-2 spike protein, for proteolytic cleavage. They identify the endo protease Furin as a potential target for therapeutic intervention, since this protease cleaves the S protein, which is a key step for viral entry into lung cells. However, the quantitative densitometric analyses of the immunoblots showing cleavage comparison were not provided, making it hard to extrapolate their conclusions to the actual COVID-19 condition. Furthermore, this study lacked physiologically relevant models, such as a primary lung cell line or an in vivo system, in order to test the different mutations of the multibasic site. The use of a more appropriate model would have permitted a rigorous evaluation of how these mutations affect viral infection. In addition, an in vivo approach would also have been useful to test the effects of the Furin inhibitor, a shortcoming the authors themselves acknowledge in the discussion section, considering that the drug may exert toxic effects. Thus, suggesting Furin as a COVID-19 therapeutic target seemed rather premature.

The brief communication published in Nature Medicine in April 2020 by Leung et al. (2020) discusses the efficacy of face masks in preventing transmission of three different viruses, including COVID-19. A major concern here was that the authors did not clearly describe the masks used in the experiments, particularly, in terms of the submicron-sized filter or the mask certification. This information is highly relevant because differences have been reported between different face masks and their ability to filter aerosols ( Oberg and Brosseau, 2008 ). Moreover, differences between aerosols and droplet transmission were not discussed and although the authors concluded that for all studied viruses shedding is higher in nasal swabs than in throat swabs, they did not provide a statistical analysis of these results. Another important problem was the small size of the population analyzed for coronavirus (only 17 patients) and the fact that this sample included patients with chronic medical conditions (five patients) and one smoker. These conditions may cause changes in the respiratory rate and other symptoms that might not be a direct consequence of viral infection but of the underlying condition ( Martin et al., 2016 ; Britto et al., 2017 ). As an outcome, the number of viral copies in exhaled breath could be altered and might not reflect the real values of most patients. Furthermore, swab samples were taken from all 17 patients; however, in the droplet vs. aerosol experiments, the number of patients was reduced to 10 (without mask) and 9 (with mask), respectively. This limited sample size is insufficient to draw significant conclusions.

The last report on COVID-19 reviewed was the one published in Science by Rockx et al. (2020) in April 2020. Here, the authors studied the pathogenesis of infection produced by SARS-CoV-2 and compared it with that of SARS-CoV and MERS-CoV, using a non-human primate infection model. A common critique here was that the information provided about the studied subjects was incomplete, considering that the main goal of the article was to describe an appropriate animal model for COVID-19 trials. No information concerning the macaques, such as physical status or health condition was provided, although these are parameters that can affect the severity of respiratory diseases. Moreover, the authors did not provide information concerning the exact age of macaques in both MERS and SARS groups, nor did they mention specific details about the inoculation doses used in the experiments. Moreover, the data showed high variability and no statistical analyses were provided.

The first article reviewed as a control was published in Cell Host & Microbe by Di Luccia and colleagues, on June 2020 ( Di Luccia et al., 2020 ). The authors studied the effect that undernutrition and microbiota can have on the immune response to oral vaccination in a gnotobiotic mouse model. In this article, we observed that sample collection and clinical data from the donors, the methodology regarding the mouse model, controls and statistics were well detailed. Nevertheless, a common critique found was that the study included only fecal samples from one child donor for each group of study (supplement-responsive and supplement-hyporesponsive).

Another article we used as a control was published in PNAS by Wang and colleagues, on June 2020 ( Wang et al., 2020 ). Here, the authors compared the capacity to lower the viral load of wild-type anti–HIV-1 immunoglobulin G1, using an Fc Null variant of the same antibody in both a humanized mouse model and in R. macaques . The article was well written, the methodology was explained with enough detail, and the controls and sample sizes used were appropriate. The only critique made was that the confirmation of the results using different antibodies were performed using distinct virus strains.

Our last article reviewed as a control was published in Nature Medicine , in March 2020. Here, Colby and colleagues provide evidence for the safety, immunogenicity, and viral rebound dynamics of a heterologous Ad26, MVA vaccine regimen in antiretroviral therapy-suppressed HIV patients ( Colby et al., 2020 ). The major criticism found here was that the authors did not perform power calculations to obtain an optimal sample size, which they mention was due to the number of available subjects. Furthermore, they used the Wilcoxon test (which compares two related samples) for statistical significance even though their graphs show the comparison of multiple populations. Additionally, they only studied Asian males, which confers, gender and ethnic biases to their results.

Therefore, major criticisms that were common to most of the reports on COVID-19 were: i) high variability of the results, or no statistical analysis provided, or results with non-significant differences; ii) lack of appropriate controls; iii) models were not described in detail, incomplete patient information, insufficient information about the experimental design, exact age or lack of information related to doses inoculated, etc.; iv) small sample size; v) no indication of toxicity assays; vi) insufficient evidence provided to claim clinical relevance; vii) a more physiologically relevant model would have been necessary to draw the conclusions. All of the aforementioned elements are crucial requisites that should not be bypassed in the peer reviewing process. Nevertheless, we did notice a greater number of these issues in COVID-19 related papers than in control papers. Additionally, the students also noted that non-COVID-19 articles were more prone to discussing the limitations of the studies and less prone to overselling their results in comparison to COVID-19 related ones. Importantly, a more extended reviewing process was evident for the control articles (>50 days) when compared to the COVID-19 papers (mean = 31 days) ( Table 2 ).

TABLE 2 . Summary of selected articles, indicating the journal and its impact factor, along with the number of days to final acceptance, and the article type, i.e., short (green) vs. long (orange) articles.

Scientific rigor is a must no matter the circumstances. Thus, researchers and scientific journals should not take advantage of the pandemic contingency to publish papers that do not present enough evidence to support the conclusions claimed by the authors. Particularly, in these times when people turn to scientists in search of answers to calm their fears and concerns. The peer review process should assist the scientific community in “assuring the quality of research before it is published and before it can be examined and used by a wider audience” ( Cargill and O’Connor, 2013 ). Therefore, peer reviewing should be carried out thoroughly and meticulously to guarantee that carefully conducted scientific studies are being published in these emergency times.

In addition, because the situation is critical and it will inevitably affect the economy worldwide, significant financial cuts are foreseen in every field and discipline, and science is not an exception. However, this is a typical catch-twenty-two situation. Science, research, and experimentation is needed to learn about all these microbes, viruses, and other microorganisms that can cause severe damage to human health, yet funds are being cut in order to provide money for other more immediate needs. What all the relevant players need to learn from this experience, is that science should always be an action rather than a reaction, which is what we are now learning from these articles that have been peer reviewed using less than rigorous criteria. Likewise, while fear of running out of funds and the urgent need for a treatment for this deadly disease may be pressuring from all angles to publish at any cost, ethics and rigor are core scientific values that need to be met to draw meaningful conclusions.

Interestingly, when the students were asked to review additional non-COVID-19 articles as controls for this Opinion, they rapidly noticed the apparent differences in the details between COVID-19 and non-COVID-19 articles with respect to the methodology used and other significant issues analyzed during this study (see Table 1 ). Thus, the question as to whether COVID-19 articles, given the current pandemic, are being reviewed with a less critical eye is supported here by the comparison performed with the non-COVID-19 article reviews. Until now, other authors have also remarked on this issue ( Bagdasarian et al., 2020 ; Kwon, 2020 ). However, it is noteworthy to point out that peer reviewing is a complex task that involves human judgment and interpretation of someone else’s experimental design and findings, and as such, it is not free of error (e.g, see http://retractionwatch.com/ ).

Author Contributions

The authors confirm to have contributed to this work and have all approved it for publication.

LL was funded by the Agencia Nacional de Investigación y Desarrollo (ANID) grant (FCYT1200836), OO was funded by the Millennium Nucleus of Ion Channel-Associated Diseases (MiNICAD), and the PhD students by ANID fellowships [Beca Doctorado Nacional 21190214 (DJ); 21170292 (MFG); 21201941 (DM); 21191668 (DH); 21190725 (JR); 21191341 (MD), and tuition fee/stipend grant for Doctoral Studies 2020, from the Faculty of Medicine, Universidad de Chile (MG, SS)].

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.

Acknowledgments

The authors would like to thank Andrew Quest for insightful discussions regarding the topic and Ana María Avalos for her editing/proofreading contribution.

Bagdasarian, N., Cross, G. B., and Fisher, D. (2020). Rapid publications risk the integrity of science in the era of COVID-19. BMC Med. 18, 192. doi:10.1186/s12916-020-01650-6

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Keywords: coronavirus, severe acute respiratory syndrome, SARS-CoV-2, infectious disease, pandemic, scientific rigor, research funds

Citation: Orellana-Serradell O, Díaz MC, González MF, Gutiérrez M, Herrera D, Jara D, Maureira D, Ruiz-Fuentes JL, Sanhueza S and Leyton L (2020) Does Peer Reviewing for COVID-19-Related Papers Still Work?. Front. Res. Metr. Anal. 5:571886. doi: 10.3389/frma.2020.571886

Received: 16 June 2020; Accepted: 22 September 2020; Published: 08 October 2020.

Reviewed by:

Copyright © 2020 Orellana Serradell, Díaz, González, Gutiérrez, Herrera, Jara, Maureira, Ruiz-Fuentes, Sanhueza and Leyton. 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: Lisette Leyton, [email protected]

This article is part of the Research Topic

Coronavirus Research Landscape: Resources, Utilities, and Analytic Studies

Peer-Reviewed Report on Moderna COVID-19 Vaccine Publishes

Data from phase 3 clinical trial confirm vaccine is effective.

December 30, 2020

Dr. Fauci is sitting while a healthcare worker administers a vaccine in his upper arm.

Dr. Anthony Fauci, Director of the National Institute of Allergy and Infectious Diseases, receives the Moderna COVID-19 vaccine at the HHS/NIH COVID-19 Vaccine Kick-Off event at NIH on 12/22/20.

The investigational vaccine known as mRNA-1273 was 94.1% efficacious in preventing symptomatic coronavirus disease 2019 (COVID-19), according to preliminary results from a Phase 3 clinical trial reported in the New England Journal of Medicine . The vaccine also demonstrated efficacy in preventing severe COVID-19. Investigators identified no safety concerns and no evidence of vaccine-associated enhanced respiratory disease (VAERD).

There were no concerning safety issues with vaccination, according to the authors. Local reactions to the vaccine were generally mild. About 50% of participants receiving mRNA-1273 experienced moderate-to-severe side effects—such as fatigue, muscle aches, joint pain and headache—after the second dose, which resolved in most volunteers within two days.

The authors concluded by discussing the unprecedented efficiency of the candidate vaccine’s development, noting, “this process demonstrates what is possible in the context of motivated collaboration among key sectors of society including academia, government, industry, regulators and the larger community.”

ARTICLE: LR Baden, et al. Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine. New England Journal of Medicine DOI: 10.1056/NEJMoa2035389 (2020).

WHO: NIAID Director Anthony S. Fauci, M.D. is available to comment on this study. John R. Mascola, M.D., director of NIAID’s Vaccine Research Center, is also available to comment.

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Explore the latest COVID-19 related research, reviews, commentary, news and analysis across epidemiology, treatments, vaccines and much more from across the Lancet family of journals. Search all COVID-19 content published by the Lancet journals here.

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Clinical case definition of post-covid-19 condition in children: a good start, but improvements are needed, incidence of new-onset in-hospital and persistent diabetes in covid-19 patients: comparison with influenza, latest covid-19 research.

Please select at least one article in order to proceed.

Protective immunity of SARS-CoV-2 infection and vaccines against medically attended symptomatic omicron BA.4, BA.5, and XBB reinfections in Singapore: a national cohort study

Latest reviews and reports, confronting the evolution and expansion of anti-vaccine activism in the usa in the covid-19 era, synthesising evidence of the effects of covid-19 regulatory changes on methadone treatment for opioid use disorder: implications for policy, learning from the covid-19 pandemic response to strengthen undocumented migrant-sensitive health systems: case studies from four countries, leveraging research, community and collaboration towards robust covid-19 mental health response in the caribbean, featured multimedia, infographics.

Explore all COVID-19 infographics

Listen to the latest episode of The Lancet Voice

Past SARS-CoV-2 infection protection against re-infection: a systematic review and meta-analysis

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The Lancet journals endorse the Wellcome Trust Statement on sharing research data and findings relevant to the COVID-19 pandemic.

Peer-reviewed data show high protection for leading COVID vaccines

Province of British Columbia / Flickr cc

The peer-reviewed data on both the Moderna and Pfizer-BioNTech COVID vaccines are in, demonstrating 94% to 95% protection from the disease.

The phase 3 clinical trial results for the Moderna COVID-19 vaccine, mRNA-1273, and the Pfizer-BioNTech COVID-19 vaccine, BNT162b2 or Comirnaty, were published late last week in the New England Journal of Medicine ( NEJM ). When compared with placebos, Moderna's vaccine showed 94.1% efficacy (95% confidence interval [CI], 89.3% to 96.8%), and Pfizer's had 95.0% efficacy (95% CI, 90.3% to 97.6%).

Both rates are for patients who received the two intended doses. Adverse events were uncommon in both studies.

"That the mRNA-1273 Covid-19 and the BNT162b2 Covid-19 vaccines protect with near-identical 94 to 95% vaccine efficacies—and that both vaccines were developed and tested in less than year—are extraordinary scientific and medical triumphs," writes Barton Haynes, MD, in a NEJM editorial on the Moderna study.

Eric Rubin, MD, PhD, and Dan Longo, MD, also use the word triumph to describe the Pfizer vaccine in their editorial , adding that, despite the further areas of research needed, the data are "impressive enough to hold up in any conceivable analysis."

The US Food and Drug Administration authorized the Pfizer vaccine on Dec 11 and the Moderna vaccine on Dec 18 and. Both make use of messenger RNA in lipid nanoparticles.

The authors of both papers aim to assess similar outcomes in future studies: long-term efficacy, uncommon or slow-to-surface side effects, and effects on asymptomatic infections and transmission rates.

Moderna trial aimed for representative demographics

In Moderna's phase 3 trial , also called COVE (Coronavirus Efficacy and Safety Study), 14,134 adults across 99 sites in the United States received two 100-microgram (mcg) dosages of the vaccine 28 days apart, with follow-ups continuing for a median of 64 days after the second dose.

When compared with 14,073 patients who received placebo, the researchers found 11 (0.1%) patients in the intervention group developed symptomatic COVID-19, compared with 185 (1.3%) in the placebo group. The only cases of severe COVID-19 infections occurred in the placebo group (0.2%). Data stratification by patients' age, sex, and race still showed consistent vaccine efficacy. The lowest rate was for those 65 and above (86.4%; 95% CI, 61.4% to 95.2%).

"The data suggest protection from severe illness, indicating that the vaccine could have an impact on preventing hospitalizations and deaths, at least in the first several months post-vaccination," says Lindsey Baden, MD, co-principal investigator for the study and lead author, says in a Brigham and Women's Hospital press release . Baden is an infectious disease specialist at Brigham, which was one of the study's locations.

The study's design tried to create a sample pool that reflected US racial demographics, including 20.5% of Hispanic or Latino background, 10.2% of black or African American background, and 4.6% of Asian descent, according to the press release. Additionally, 24.8% of patients were 65 and older, and 16.7% of those younger had comorbidities such as chronic lung disease, diabetes, or obesity.

Common side effects for the vaccine treatment were mild-to-moderate injection-site pain, headache, and fatigue. Overall, severe adverse events occurred in 0.5% of the intervention group and 0.2% of the placebo group, and none were classified as immediately life-threatening or as a cause of death.

People as young as 16 received Pfizer vaccine

In the Pfizer-BioNTech study , 18,508 people 16 years and older received the vaccine in two 30-mcg dosages 21 days apart.

During a follow-up that had a median duration of two months, 9 cases of symptomatic COVID-19 were found in the intervention group (0.05%), whereas 162 of the 18,435 people in the placebo group (0.9%) reported COVID-19 infections. Of the 10 severe COVID-19 cases, 9 occurred in the placebo group. Vaccine efficacy appeared to be consistent across racial backgrounds, age, and comorbidities.

"The study was not designed to assess the efficacy of a single-dose regimen," the researchers wrote. "Nevertheless, in the interval between the first and second doses, the observed vaccine efficacy against Covid-19 was 52%, and in the first 7 days after dose 2, it was 91%, reaching full efficacy against disease with onset at least 7 days after dose 2."

The study cohort was 82.9% white, 9.2% black, 27.9% Hispanic or Latino, and 4.2% Asian. Twenty-one percent had at least one pre-existing comorbidity, and 42.3% were older than 55 years. Almost 77% of the study cohort was located in the United States, but 15.3% were located in Argentina, 6.1% were in Brazil and 2.0% were in South Africa.

"The frequency of any severe systemic event after the first dose was 0.9% or less. Severe systemic events were reported in less than 2% of vaccine recipients after either dose, except for fatigue (in 3.8%) and headache (in 2.0%) after the second dose," the researchers write. No vaccine-related deaths occurred in any study participant.

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Study of H5N1 avian flu seal deaths reveals multiple lineages

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Paxlovid's estimated effectiveness in preventing hospitalization or death was 53.6%, rising to 89.6% when it was taken on the day of the COVID-19 test.

Global research on coronavirus disease (COVID-19)

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WHO is bringing the world’s scientists and global health professionals together to accelerate the research and development process, and develop new norms and standards to contain the spread of the coronavirus pandemic and help care for those affected.

The R&D Blueprint has been activated to accelerate diagnostics, vaccines and therapeutics for this novel coronavirus.

The solidarity of all countries will be essential to ensure equitable access to COVID-19 health products.

WHO COVID-19 Research Database

WHO is gathering the latest international multilingual scientific findings and knowledge on COVID-19. The global literature cited in the WHO COVID-19 Research Database is updated daily (Monday through Friday) from searches of bibliographic databases, hand searching, and the addition of other expert-referred scientific articles. This database represents a comprehensive multilingual source of current literature on the topic. While it may not be exhaustive, new research is added regularly.

The WHO evidence retrieval sub-group has begun collaboration with key partners to enrich the citations and build a more comprehensive database with inclusion of other content. The database is built by BIREME, the Specialized Center of PAHO/AMRO and part of the Regional Office’s Department of Evidence and Intelligence for Action in Health.

For further information or questions, please contact the WHO Library via email .

Disclaimer: the designations employed and the presentation of the material in publications listed in this database does not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement.

The mention of specific companies or of certain manufacturers’ products in publications listed in the database does not imply that they are endorsed or recommended by WHO in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.

By listing publications in this database and providing links to external sites does not mean that WHO endorses or recommends those publications or sites, or has verified the content contained within them. The database has been compiled without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of publications included in this database lies with the reader. In no event shall WHO be liable for damages arising from its use.

Find the latest Covid-19 research papers.

Hi there, I'm not sure this service is of so much use now, so I've not been keeping it up to date and I will probably shut it down soon. If you still need it, just let me know .

We've got some...

@covidpapers for alerts About this service

Many thanks to Digital Ocean for providing us some free credits to help pay for our cloud server infrastructure! And also to Open Access Button and Cottage Labs for making time to enable this work, and to Wikidata for their work and the images used here.

Searching Covid papers

Search for anything that could be related to covid-19 in all the latest research. For example find papers about underlying health conditions by typing the name of that condition. Or find papers about a certain location, treatment, etc.

There are dropdown filters for authors , journals , and keywords . The search/settings icon next to the search bar shows any currently available advanced options (these are in development). The search box accepts complex queries using elasticsearch query string syntax. For example all closed access papers that mention fibrosis would be NOT url:* AND fibrosis . Use double quotes to combine multi-word strings, e.g. "reactive oxygen species" .

How this works

We query academic search APIs for covid , then combine all the results to make it easy to search, and display it in order of most recent publication. We check every couple of hours for new publications. We know some sources include other ones, and can include other institutional repositories, so we merge and deduplicate everything to get the best possible coverage and metadata. The sources we currently use are:

Suggest a new source

We also check to see if the paper is open access. We use the Wellcome Trust Compliance checker to find the licence, and we use Unpaywall to check if a DOI has an open access link. If there is no DOI we search Open Access Button by title. We display open paper links with each open article. For closed ones we show a go to paper link , along with options to request paper or share your paper via Open Access Button. There's also a link to show more details and options for each article.

We're currently working on retrieving the full text and analysing them for more useful search terms, MeSH headings, molecules, proteins, processes, etc. Read further down for progress info.

Are these sources and articles reliable?

The sources we search are well respected. Some of the materials will be preprints rather than published, peer-reviewed articles though. Some sources indicate that the articles are peer-reviewed, while others state that they are not. There isn't a way yet to filter to show only peer-reviewed papers, but you can look at the sources to find out. If you want to tell us an article has been peer reviewed, or provide peer review for an article we have listed, please do! We would like to make this more useful in any way possible.

Let us know if you can help with peer review .

It's possible we've made a mistake or that one of our sources has some wrong data in it. If you spot an incorrect article, of if you can tell us more about an article, please do! Each article will have a Report problem button in the Analysis section.

Report a problem / update

Maybe your article has been published and is relevant to Covid, but we don't yet have it listed here. If so, it will likely show up soon. Or if your article is published but is not freely available anywhere, please use shareyourpaper.org from Open Access Button to get help with permissions and sharing an acceptable copy. Each article that we don't yet have an open link for will show a button to take you straight there.

Who made this?

Hi, I'm Mark. I was a PhD student at the University of Edinburgh , then I was a founding partner of Cottage Labs , where I continue to consult on various projects with great organisations like the Directory of Open Access Journals and the National Institute of Materials Science . I am also the lead developer of Open Access Button . This service is running on various bits of software, some of which I wrote, many that others wrote, and all of which is open source. It runs on Cottage Labs infrastructure, and makes use of the other sources and services mentioned above.

Why did you make it? Don't you know about X, Y, Z already doing similar things?

I did it to feel pretty. Sorry, I mean, to feel useful.

Yes, I'm aware of some great tools that already exist - all the sources already listed above, of course, and search tools like Google Scholar and Microsoft Academic Search . Here's a list of some of my favourites:

Some other resources provide access to many more papers about coronavirus in general - publishers have made a collection of over 40,000 related papers available in the Semantic Scholar dataset, for example; but I wanted to search for new covid-specific papers. However it could be useful to do something with related papers as well - what do you think? I may index all the papers that the main ones refer to, or all papers with related terms in them apart from just covid .

New features coming soon

(maybe, as soon as I can - there is a lot going on)

There is an API, but it is not documented yet, coming soon. If anyone wants to, I could give out an API key that would allow you to submit articles or analysis of articles. So if you have some kind of analysis you can run yourself, I think that is easier than contributing to a codebase right now - just do your own thing, contact me for a key, and then you can send your analysis over to my API to get added to the index.

I don't have enough time to add more here right now, get in touch if you want to know more.

Attribution

Any articles you use should be attributed directly in the usual way.

If this site has been useful you can attribute us too, using the covidpapers.com domain name. The author name is Mark MacGillivray

Get in touch if you want more details about the analysis work going on here, or about using and attributing our data directly. Offers to support this service to keep it running are also welcome!

I'm making some graphs and visualisations in case they are useful, let me know if you have any suggestions. They'll be listed here once available.

Network graph : a useful way to explore the data, to see relations between different entities in the research papers. For example, remdesivir has been in the news lately, try selecting it from the drugs filter to view the relation between it and other drugs and diseases, and the papers that mention them.

Time series graphs coming soon...

IMAGES

I do not know if this article is scholarly or peer-reviewed.
Greenwich Academic Literature Archive
I do not know what "peer-reviewed" means.
Accuracy of rapid Covid-19 antibody test ‘may be lower than previously suggested’
Covid-19 & Community Voice
Supporting COVID-19 Research

COMMENTS

Peer-Reviewed Publications about COVID-19 (Coronavirus) by Yale Authors
Peer-Reviewed Publications about COVID-19 (Coronavirus) by Yale Authors Sharing knowledge about COVID-19 (coronavirus) is vital to our efforts as we fight the pandemic. Yale researchers are publishing their discoveries about COVID-19 (coronavirus) in peer-reviewed publications. Check back frequently to access the latest findings.
Coronavirus (Covid-19)
Original Article Covid-19 Vaccine in Children and Adolescents in Qatar H. Chemaitelly and Others The 10-μg dose of BNT162b2 led to modest, rapidly waning protection against Covid-19 in children...
Coronavirus disease 2019 (COVID-19): A literature review
Coronavirus disease 2019 (COVID-19): A literature review J Infect Public Health. 2020 May;13 (5):667-673. doi: 10.1016/j.jiph.2020.03.019. Epub 2020 Apr 8. Authors Harapan Harapan 1 , Naoya Itoh 2 , Amanda Yufika 3 , Wira Winardi 4 , Synat Keam 5 , Haypheng Te 6 , Dewi Megawati 7 , Zinatul Hayati 8 , Abram L Wagner 9 , Mudatsir Mudatsir 10
COVID-19 Research < Yale School of Medicine
Peer-Reviewed Publications about COVID-19 (Coronavirus) by Yale Authors Sharing knowledge about COVID-19 (coronavirus) is vital to our efforts as we fight the pandemic. Yale researchers are publishing their discoveries about COVID-19 (coronavirus) in peer-reviewed publications.
The proximal origin of SARS-CoV-2
SARS-CoV-2 is the seventh coronavirus known to infect humans; SARS-CoV, MERS-CoV and SARS-CoV-2 can cause severe disease, whereas HKU1, NL63, OC43 and 229E are associated with mild symptoms 6....
Laboratory Tests for COVID-19: A Review of Peer-Reviewed ...
Serology is gaining attention, mainly because of convalescent plasma gaining importance as treatment for clinically worsening COVID-19 patients. We provide a narrative review of peer-reviewed research studies on RT-PCR, serology and antigen immune-assays for COVID-19, briefly describe their lab methods and discuss their limitations for clinical ...
Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine
A two-dose regimen of BNT162b2 (30 μg per dose, given 21 days apart) was found to be safe and 95% effective against Covid-19. The vaccine met both primary efficacy end points, with more than a 99 ...
Peer-reviewed report on Moderna COVID-19 vaccine publishes
The investigational vaccine known as mRNA-1273 was 94.1% efficacious in preventing symptomatic coronavirus disease 2019 (COVID-19), according to preliminary results from a Phase 3 clinical trial reported in the New England Journal of Medicine. The vaccine also demonstrated efficacy in preventing severe COVID-19.
Do three new studies add up to proof of COVID-19's origin ...
Hewing closely to government assertions on COVID-19's origin, the preprint by Gao and colleagues notes studies that have reported evidence of SARS-CoV-2 in other countries before it surfaced in Wuhan, making no mention of critiques that attribute that evidence to contamination.
How a torrent of COVID science changed research publishing
More than 30,000 of the COVID-19 articles published in 2020 were preprints — between 17% and 30% of total COVID-19 research papers (depending on database searched). And, according to...
The varying impacts of COVID-19 and its related measures in the ...
Recent evidence has shown that the COVID-19 pandemic and related social and economic measures, such as physical distancing and business closure, have differential impacts on various social groups. In the UK, for example, women and parents are found to have experienced a larger reduction in subjective wellbeing [ 1, 2 ].
Rapid Reviews Infectious Diseases
Welcome to Rapid Reviews: Infectious Diseases!. RR\ID is an open-access overlay journal that accelerates peer review of important infectious disease-related research preprints. We are an evolution of Rapid Reviews: COVID-19 (RR\C19), which won the 2022 PROSE Award for Innovation in Journal Publishing for introducing a new peer review model facilitating rapid publishing of COVID-19-related ...
Does Peer Reviewing for COVID-19-Related Papers Still Work?
The last report on COVID-19 reviewed was the one published in Science by Rockx et al. (2020) in April 2020. Here, the authors studied the pathogenesis of infection produced by SARS-CoV-2 and compared it with that of SARS-CoV and MERS-CoV, using a non-human primate infection model.
Peer-Reviewed Report on Moderna COVID-19 Vaccine Publishes
Peer-Reviewed Report on Moderna COVID-19 Vaccine Publishes Data from Phase 3 Clinical Trial Confirm Vaccine is Effective December 30, 2020 Dr. Anthony Fauci, Director of the National Institute of Allergy and Infectious Diseases, receives the Moderna COVID-19 vaccine at the HHS/NIH COVID-19 Vaccine Kick-Off event at NIH on 12/22/20. Credit: NIH
PDF The Impact of Covid-19 on Student Experiences and Expectations
of COVID-19 can explain 40% of the delayed graduation gap (as well as a substantial part of the gap for other outcomes) between lower- and higher-income students. To our knowledge, this is the rst paper to shed light on the e ects of COVID-19 on college students' experiences. The treatment e ects that we nd are large in economic terms.
ERIC
Purpose: The global spread of the COVID-19 pandemic resulted in the complete lockdown of almost every part of the world, including all educational institutions, resulting in the prompt implementation of online education to facilitate the students to carry on their learning. These conditions made the researchers study the experiences of online education among students and teachers.
COVID-19 Resource Centre
Explore the latest COVID-19 related research, reviews, commentary, news and analysis across epidemiology, treatments, vaccines and much more from across the Lancet family of journals. Search all COVID-19 content published by the Lancet journals here. Editor's pick Correspondence International cooperation to end the COVID-19 pandemic Qu The Lancet
Peer-reviewed data show high protection for leading COVID vaccines
The peer-reviewed data on both the Moderna and Pfizer-BioNTech COVID vaccines are in, demonstrating 94% to 95% protection from the disease. The phase 3 clinical trial results for the Moderna COVID-19 vaccine, mRNA-1273, and the Pfizer-BioNTech COVID-19 vaccine, BNT162b2 or Comirnaty, were published late last week in the New England Journal of Medicine (NEJM).
Global research on coronavirus disease (COVID-19)
The global literature cited in the WHO COVID-19 Research Database is updated daily (Monday through Friday) from searches of bibliographic databases, hand searching, and the addition of other expert-referred scientific articles. This database represents a comprehensive multilingual source of current literature on the topic.
Covid papers
The pneumonia caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)abbreviated as Coronavirus Disease 2019 (COVID-19)has been classified as a Class B infectious disease under the management of Class A infectious diseases in China. COVID-19 caused by hospital-related transmission is not uncommon and poses a serious threat to ...