Search

Massive scientific productivity accompanied the COVID-19 pandemic. We evaluated the citation impact of COVID-19 publications relative to all scientific work published in 2020 to 2021 and assessed the impact on scientist citation profiles. Using Scopus data until August 1, 2021, COVID-19 items accounted for 4% of papers published, 20% of citations received to papers published in 2020 to 2021, and >30% of citations received in 36 of the 174 disciplines of science (up to 79.3% in general and internal medicine). Across science, 98 of the 100 most-cited papers published in 2020 to 2021 were related to COVID-19; 110 scientists received ≥10,000 citations for COVID-19 work, but none received ≥10,000 citations for non–COVID-19 work published in 2020 to 2021. For many scientists, citations to their COVID-19 work already accounted for more than half of their total career citation count. Overall, these data show a strong covidization of research citations across science, with major impact on shaping the citation elite. [ABSTRACT FROM AUTHOR]

Closing the achievement gap for minority students in higher education requires addressing the lack of belonging these students experience. This paper introduces a psychological intervention that strategically targets key elements within the learning environment to foster the success of minority students. The intervention sought to enhance Palestinian minority student's sense of belonging by increasing the presence of their native language. We tested the effectiveness of the intervention in two field experiments in Israel (n > 20,000), at the height of the COVID-19 pandemic when all classes were held via Zoom. Lecturers in the experimental condition added a transcript of their names in Arabic to their default display (English/Hebrew only). Our findings revealed a substantial and positive impact on Palestinian student's sense of belonging, class participation, and overall grades. In experiment 1, Palestinian student's average grade increased by 10 points. In experiment 2, there was an average increase of 4 points among Palestinian students' semester grade. Our intervention demonstrates that small institutional changes when carefully crafted can have a significant impact on minority populations. These results have significant implications for addressing educational disparities and fostering inclusive learning environment., Competing Interests: Competing interests statement:The authors declare no competing interest.

The transition to remote learning in the context of COVID-19 led to dramatic setbacks in education. Is the return to in-person classes sufficient to eliminate these losses eventually? We study this question using data from the universe of secondary students in São Paulo State, Brazil. We estimate the causal medium-run impacts of the length of exposure to remote learning during the pandemic through a triple-differences strategy, which contrasts changes in educational outcomes across municipalities and grades that resumed in-person classes earlier (already by Q4/2020) or only in 2021. We find that relative learning losses from longer exposure to remote learning did not fade out over time-attesting that school reopening was at the same time key but not enough to mitigate accumulated learning losses in face of persistence. Using observational and experimental variation in local responses across 645 municipalities, we further document that remedial educational policies in the aftermath of the pandemic boosted learning recovery., Competing Interests: Competing interests statement:G.L. is a cofounder at Movva, the implementing partner of the SMS intervention featured among the remedial policies evaluated in the paper.

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers., Competing Interests: Competing interests statement:The authors declare no competing interest.

This paper presents quasiexperimental evidence of Covid-19 transmission through casual contact between customers in retail stores. For a large sample of individuals in Denmark, we match card payment data, indicating exactly where and when each individual made purchases, with Covid-19 test data, indicating when each individual was tested and whether the test was positive. The resulting dataset identifies more than 100,000 instances where an infected individual made a purchase in a store and, in each instance, allows us to track the infection dynamics of other individuals who made purchases in the same store around the same time. We estimate transmissions by comparing the infection rate of exposed customers, who made a purchase within 5 min of an infected individual, and nonexposed customers, who made a purchase in the same store 16 to 30 min before. We find that exposure to an infected individual in a store increases the infection rate by around 0.12 percentage points ( P < 0.001) between day 3 and day 7 after exposure. The estimates imply that transmissions in stores contributed around 0.04 to the reproduction number for the average infected individual and significantly more in the period where Omicron was the dominant variant., Competing Interests: Competing interests statement:The authors declare no competing interest.

The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as “long COVID” and studies of inequality. [ABSTRACT FROM AUTHOR]

Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and caused multiple waves of infection globally. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern. They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta. Phylogenetic methods provide the "gold standard" for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges provide a motivation to develop complementary methods that can incorporate all of the genetic data available without down-sampling to extract meaningful information rapidly and with minimal curation. In this paper, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies. While not serving as a substitute for current phylogenetic analyses, the proposed methods can be used as a complementary, and fully automatable, approach to identify and confirm new emerging variants., Competing Interests: Competing interests statement:The authors declare no competing interest.

Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2), resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T's "arms." Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4 to 6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across these human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9 to 8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4 to 9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities and notable differences, with implications for potential protein-binding modes and therapeutic targets., Competing Interests: Competing interests statement:Stanford University is filing patent applications based on concepts described in this paper.

This paper examines whether school COVID-19 policies influenced enrollment differently by student age and race/ethnicity. Unlike much prior research, we i) analyze enrollments for virtually the entire U.S. public school population for both the 2020-2021 and 2021-2022 school years, ii) compare enrollment trends within districts in order to isolate subgroup heterogeneity from district characteristics, and iii) account for district selection into preferred learning modes. Analyzing data on over 9,000 districts that serve more than 90% of public school students in the United States, we find enrollment responses to COVID policies differed notably. We find that White enrollments declined more than Black, Hispanic, and Asian enrollments in districts that started the 2020-2021 school year virtually, but in districts that started in-person the reverse was true: Non-White enrollments declined more than White enrollments. Moreover, Black, Hispanic, and Asian families responded more than White families to higher COVID-19 death rates in the months preceding the start of the 2021 school year. In 2021-2022, enrollment differences by the previous year's learning mode persisted. Racial/ethnic differences did not vary by whether the district required masking in classrooms. These findings are consistent with the greater risk faced by communities of color during the pandemic and demonstrate an additional source of disparate impact from COVID policies., Competing Interests: Competing interests statement:The authors declare no competing interest.

Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures., Competing Interests: Competing interests statement:The authors declare no competing interest.

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic struck societies directly and indirectly, not just challenging population health but disrupting many aspects of life. Different effects of the spreading virus-and the measures to fight it-are reported and discussed in different scientific fora, with hard-to-compare methods and metrics from different traditions. While the pandemic struck some groups more than others, it is difficult to assess the comprehensive impact on social inequalities. This paper gauges social inequalities using individual-level administrative data for Sweden's entire population. We describe and analyze the relative risks for different social groups in four dimensions-gender, education, income, and world region of birth-to experience three types of COVID-19 incidence, as well as six additional negative life outcomes that reflect general health, access to medical care, and economic strain. During the pandemic, the overall population faced severe morbidity and mortality from COVID-19 and saw higher all-cause mortality, income losses and unemployment risks, as well as reduced access to medical care. These burdens fell more heavily on individuals with low income or education and on immigrants. Although these vulnerable groups experienced larger absolute risks of suffering the direct and indirect consequences of the pandemic, the relative risks in pandemic years (2020 and 2021) were conspicuously similar to those in prepandemic years (2016 to 2019).

Governments worldwide have announced stimulus packages to remobilize the labor force after COVID-19 and therefore to cope with the COVID-19-related recession. However, it is still unclear how to facilitate large-scale work resumption. This paper aims to clarify the issue by analyzing the large-scale prefecture-level dataset of human mobility trajectory information for 320 million workers and about 500,000 policy documents in China. We model work resumption as a collective behavioral change due to configurations of capacity, motivation, and policy instruments by using qualitative comparative analysis. We find that the effectiveness of post-COVID-19 recovery stimulus varied across China depending on the fiscal and administrative capacity and the policy motivation of the prefecture. Subnational fiscal and procurement policies were more effective for the wholesale and retail sector and the hotel and catering sector, whereas the manufacturing and business services sectors required more effort regarding employment policies. Due to limited prefectural capacity and wavering policy motivation, the simultaneous adoption of fiscal, employment, and procurement policy interventions endangered post-COVID-19 work resumption. We highlight the necessity of tailored postcrisis recovery strategies based on local fiscal and administrative capacity and the sectoral structure.

Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures. [ABSTRACT FROM AUTHOR]

Governments worldwide have announced stimulus packages to remobilize the labor force after COVID-19 and therefore to cope with the COVID-19-related recession. However, it is still unclear how to facilitate large-scale work resumption. This paper aims to clarify the issue by analyzing the large-scale prefecture-level dataset of human mobility trajectory information for 320 million workers and about 500,000 policy documents in China. We model work resumption as a collective behavioral change due to configurations of capacity, motivation, and policy instruments by using qualitative comparative analysis. We find that the effectiveness of post-COVID-19 recovery stimulus varied across China depending on the fiscal and administrative capacity and the policy motivation of the prefecture. Subnational fiscal and procurement policies were more effective for the wholesale and retail sector and the hotel and catering sector, whereas the manufacturing and business services sectors required more effort regarding employment policies. Due to limited prefectural capacity and wavering policy motivation, the simultaneous adoption of fiscal, employment, and procurement policy interventions endangered post-COVID-19 work resumption. We highlight the necessity of tailored postcrisis recovery strategies based on local fiscal and administrative capacity and the sectoral structure. [ABSTRACT FROM AUTHOR]

Public health organizations increasingly use social media advertising campaigns in pursuit of public health goals. In this paper, we evaluate the impact of about $40 million of social media advertisements that were run and experimentally tested on Facebook and Instagram, aimed at increasing COVID-19 vaccination rates in the first year of the vaccine roll-out. The 819 randomized experiments in our sample were run by 174 different public health organizations and collectively reached 2.1 billion individuals in 15 languages. We find that these campaigns are, on average, effective at influencing self-reported beliefs-shifting opinions close to 1% at baseline with a cost per influenced person of about $3.41. Combining this result with an estimate of the relationship between survey outcomes and vaccination rates derived from observational data yields an estimated cost per additional vaccination of about $5.68. There is further evidence that campaigns are especially effective at influencing users' knowledge of how to get vaccines. Our results represent, to the best of our knowledge, the largest set of online public health interventions analyzed to date.

Pooled testing increases efficiency by grouping individual samples and testing the combined sample, such that many individuals can be cleared with one negative test. This short paper demonstrates that pooled testing is particularly advantageous in the setting of pandemics, given repeated testing, rapid spread, and uncertain risk. Repeated testing mechanically lowers the infection probability at the time of the next test by removing positives from the population. This effect alone means that increasing frequency by x times only increases expected tests by around √ x. However, this calculation omits a further benefit of frequent testing: Removing infections from the population lowers intragroup transmission, which lowers infection probability and generates further efficiency. For this reason, increasing testing frequency can paradoxically reduce total testing cost. Our calculations are based on the assumption that infection rates are known, but predicting these rates is challenging in a fast-moving pandemic. However, given that frequent testing naturally suppresses the mean and variance of infection rates, we show that our results are very robust to uncertainty and misprediction. Finally, we note that efficiency further increases given natural sampling pools (e.g., workplaces, classrooms) that induce correlated risk via local transmission. We conclude that frequent pooled testing using natural groupings is a cost-effective way to provide consistent testing of a population to suppress infection risk in a pandemic. [ABSTRACT FROM AUTHOR]

The main mathematical result in this paper is that change of variables in the ordinary differential equation (ODE) for the competition of two infections in a Susceptible-Infected-Removed (SIR) model shows that the fraction of cases due to the new variant satisfies the logistic differential equation, which models selective sweeps. Fitting the logistic to data from the Global Initiative on Sharing All Influenza Data (GISAID) shows that this correctly predicts the rapid turnover from one dominant variant to another. In addition, our fitting gives sensible estimates of the increase in infectivity. These arguments are applicable to any epidemic modeled by SIR equations.

This paper argues that changes in human activity during the COVID-19 pandemic led to an unusual divergence between crime rates and victimization risk in US cities. Most violent crimes declined during the pandemic. However, analysis using data on activity shows that the risk of street crime victimization was elevated throughout 2020. People in public spaces were 15 to 30% more likely to be robbed or assaulted. This increase is unlikely to be explained by changes in crime reporting or selection into outdoor activities by potential victims. Traditional crime rates may present a misleading view of the recent changes in public safety.

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators--derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity--from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends. [ABSTRACT FROM AUTHOR]

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey--over 20 million responses in its first year of operation--allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making. [ABSTRACT FROM AUTHOR]

High COVID-19 mortality among Black communities heightened the pandemic's devastation. In the state of Louisiana, the racial disparity associated with COVID-19 mortality was significant; Black Americans accounted for 50% of known COVID-19-related deaths while representing only 32% of the state's population. In this paper, we argue that structural racism resulted in a synergistic framework of cumulatively negative determinants of health that ultimately affected COVID-19 deaths in Louisiana Black communities. We identify the spatial distribution of social, environmental, and economic stressors across Louisiana parishes using hot spot analysis to develop aggregate stressors. Further, we examine the correlation between stressors, cumulative health risks, COVID-19 mortality, and the size of Black populations throughout Louisiana. We hypothesized that parishes with larger Black populations (percentages) would have larger stressor values and higher cumulative health risks as well as increased COVID-19 mortality rates. Our results suggest two categories of parishes. The first group has moderate levels of aggregate stress, high population densities, predominately Black populations, and high COVID-19 mortality. The second group of parishes has high aggregate stress, lower population densities, predominantly Black populations, and initially low COVID-19 mortality that increased over time. Our results suggest that structural racism and inequities led to severe disparities in initial COVID-19 effects among highly populated Black Louisiana communities and that as the virus moved into less densely populated Black communities, similar trends emerged.

This paper provides a picture of how societies in the G7 countries have responded to the COVID-19 pandemic. Our point of departure is to examine the effects of the pandemic in terms of four fundamental normative sources for well-being: Solidarity (S; willingness for social cooperation), Agency (A; empowerment to shape one's prospects through one's own efforts), GDP (G), and Environmental Performance (E)-SAGE for short. The normative foundations of SAGE are communitarianism, classical liberalism, materialistic utilitarianism, and ecoethics. We find that although G and E responded predictably and uniformly to the pandemic (such as G declining and carbon emissions improving), the societal responses were strikingly different. Societies that are cohesive and empowered (high S and A) may be expected to cope with the pandemic better than those that are fragmented and disempowered (low S and A). Furthermore, the pandemic has had diverse effects on S and A; while some societies became cohering and empowering (rising S and A), others became fragmenting and disempowering (falling S and A), and yet others became fragmenting and empowering. We also show that most G7 countries experienced greater tribalization (measured as the difference between inward S and outward S) during the pandemic. These trends are a matter of concern since they suggest that the willingness and perceived ability to address collective challenges collectively have waned. The analysis also suggests that governments' social policies may have an important role to play alongside economic and health policies in coping with the pandemic.

High COVID-19 mortality among Black communities heightened the pandemic's devastation. In the state of Louisiana, the racial disparity associated with COVID-19 mortality was significant; Black Americans accounted for 50% of known COVID-19-related deaths while representing only 32% of the state's population. In this paper, we argue that structural racism resulted in a synergistic framework of cumulatively negative determinants of health that ultimately affected COVID-19 deaths in Louisiana Black communities. We identify the spatial distribution of social, environmental, and economic stressors across Louisiana parishes using hot spot analysis to develop aggregate stressors. Further, we examine the correlation between stressors, cumulative health risks, COVID-19 mortality, and the size of Black populations throughout Louisiana. We hypothesized that parishes with larger Black populations (percentages) would have larger stressor values and higher cumulative health risks as well as increased COVID-19 mortality rates. Our results suggest two categories of parishes. The first group has moderate levels of aggregate stress, high population densities, predominately Black populations, and high COVID-19 mortality. The second group of parishes has high aggregate stress, lower population densities, predominantly Black populations, and initially low COVID-19 mortality that increased over time. Our results suggest that structural racism and inequities led to severe disparities in initial COVID-19 effects among highly populated Black Louisiana communities and that as the virus moved into less densely populated Black communities, similar trends emerged. [ABSTRACT FROM AUTHOR]

This paper empirically examines how the opening of K-12 schools is associated with the spread of COVID-19 using county-level panel data in the United States. As preliminary evidence, our event-study analysis indicates that cases and deaths in counties with in-person or hybrid opening relative to those with remote opening substantially increased after the school opening date, especially for counties without any mask mandate for staff. Our main analysis uses a dynamic panel data model for case and death growth rates, where we control for dynamically evolving mitigation policies, past infection levels, and additive county-level and state-week "fixed" effects. This analysis shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case and death growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These findings support policies that promote masking and other precautionary measures at schools and giving vaccine priority to education workers., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

It is a fundamental question in disease modeling how the initial seeding of an epidemic, spreading over a network, determines its final outcome. One important goal has been to find the seed configuration, which infects the most individuals. Although the identified optimal configurations give insight into how the initial state affects the outcome of an epidemic, they are unlikely to occur in real life. In this paper we identify two important seeding scenarios, both motivated by historical data, that reveal a complex phenomenon. In one scenario, the seeds are concentrated on the central nodes of a network, while in the second one, they are spread uniformly in the population. Comparing the final size of the epidemic started from these two initial conditions through data-driven and synthetic simulations on real and modeled geometric metapopulation networks, we find evidence for a switchover phenomenon: When the basic reproduction number [Formula: see text] is close to its critical value, more individuals become infected in the first seeding scenario, but for larger values of [Formula: see text], the second scenario is more dangerous. We find that the switchover phenomenon is amplified by the geometric nature of the underlying network and confirm our results via mathematically rigorous proofs, by mapping the network epidemic processes to bond percolation. Our results expand on the previous finding that, in the case of a single seed, the first scenario is always more dangerous and further our understanding of why the sizes of consecutive waves of a pandemic can differ even if their epidemic characters are similar., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

This paper uses real-time transaction data from a large bank in Scandinavia to estimate the effect of social distancing laws on consumer spending in the coronavirus 2019 (COVID-19) pandemic. The analysis exploits a natural experiment to disentangle the effects of the virus and the laws aiming to contain it: Denmark and Sweden were similarly exposed to the pandemic but only Denmark imposed significant restrictions on social and economic activities. We estimate that aggregate spending dropped by around 25% (95% CI: 24 to 26%) in Sweden and, as a result of the shutdown, by 4 additional percentage points (95% CI: 3 to 5 percentage points [p.p.]) in Denmark. This suggests that most of the economic contraction is caused by the virus itself and occurs regardless of social distancing laws. The age gradient in the estimates suggests that social distancing reinforces the virus-induced drop in spending for low-health-risk individuals but attenuates it for high-risk individuals by lowering the overall prevalence of the virus in the society. [ABSTRACT FROM AUTHOR]

In response to coronavirus disease 2019 (COVID-19), many scholars and policy makers are urging the United States to expand voting-by-mail programs to safeguard the electoral process. What are the effects of vote-by-mail? In this paper, we provide a comprehensive design-based analysis of the effect of universal vote-by-mail--a policy under which every voter is mailed a ballot in advance of the election--on electoral outcomes. We collect data from 1996 to 2018 on all three US states that implemented universal vote-by-mail in a staggered fashion across counties, allowing us to use a difference-in-differences design at the county level to estimate causal effects. We find that 1) universal vote-by-mail does not appear to affect either party's share of turnout, 2) universal vote-by-mail does not appear to increase either party's vote share, and 3) universal vote-by-mail modestly increases overall average turnout rates, in line with previous estimates. All three conclusions support the conventional wisdom of election administration experts and contradict many popular claims in the media. [ABSTRACT FROM AUTHOR]

This paper provides a picture of how societies in the G7 countries have responded to the COVID-19 pandemic. Our point of departure is to examine the effects of the pandemic in terms of four fundamental normative sources for well-being: Solidarity (S; willingness for social cooperation), Agency (A; empowerment to shape one's prospects through one's own efforts), GDP (G), and Environmental Performance (E)--SAGE for short. The normative foundations of SAGE are communitarianism, classical liberalism, materialistic utilitarianism, and ecoethics. We find that although G and E responded predictably and uniformly to the pandemic (such as G declining and carbon emissions improving), the societal responses were strikingly different. Societies that are cohesive and empowered (high S and A) may be expected to cope with the pandemic better than those that are fragmented and disempowered (low S and A). Furthermore, the pandemic has had diverse effects on S and A; while some societies became cohering and empowering (rising S and A), others became fragmenting and disempowering (falling S and A), and yet others became fragmenting and empowering. We also show that most G7 countries experienced greater tribalization (measured as the difference between inward S and outward S) during the pandemic. These trends are a matter of concern since they suggest that the willingness and perceived ability to address collective challenges collectively have waned. The analysis also suggests that governments' social policies may have an important role to play alongside economic and health policies in coping with the pandemic. [ABSTRACT FROM AUTHOR]

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups., Competing Interests: Competing interest statement: X.N., H.J., J.Y.L., K.L., A.J., S.X., and J.A.R. report inventorships and potential royalties in patents assigned to Northwestern University. M.K. and J.Y.L. are employees of a small private company with a commercial interest in the technology. A.R.B., S.X., and J.A.R. report equity ownership in a small private company with a commercial interest in the technology., (Copyright © 2021 the Author(s). Published by PNAS.)

With more time being spent on caregiving responsibilities during the COVID-19 pandemic, female scientists' productivity dropped. When female scientists conduct research, identity factors are better incorporated in research content. In order to mitigate damage to the research enterprise, funding agencies can play a role by putting in place gender equity policies that support all applicants and ensure research quality. A national health research funder implemented gender policy changes that included extending deadlines and factoring sex and gender into COVID-19 grant requirements. Following these changes, the funder received more applications from female scientists, awarded a greater proportion of grants to female compared to male scientists, and received and funded more grant applications that considered sex and gender in the content of COVID-19 research. Further work is urgently required to address inequities associated with identity characteristics beyond gender., Competing Interests: Competing interest statement: H.O.W. holds a funded grant as principal investigator from the second competition described in the paper. J.H. is employed by the Canadian Institutes of Health Research Institute of Gender and Health. C.T. is a Scientific Institute director at Canadian Institutes of Health Research and is therefore partially employed by the Canadian Institutes of Health Research., (Copyright © 2021 the Author(s). Published by PNAS.)

From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

We use natality microdata covering the universe of US. births for 2015 to 2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in US fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all US births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall "baby bump" among US-born mothers, which marked the first reversal in declining fertility rates since the Great Recession. Births to US-born mothers fell by 31,000 in 2020 relative to a prepandemic trend but increased by 71,000 in 2021. The data for California suggest that US births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women aged 30 to 34 and women with a college education. The 2021 to 2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models. [ABSTRACT FROM AUTHOR]

Sustaining economic activities while curbing the number of new coronavirus disease 2019 (COVID-19) cases until effective vaccines or treatments become available is a major public health and policy challenge. In this paper, we use agent-based simulations of a network-based susceptible-exposed-infectious-recovered (SEIR) model to investigate two network intervention strategies for mitigating the spread of transmission while maintaining economic activities. In the simulations, we assume that people engage in group activities in multiple sectors (e.g., going to work, going to a local grocery store), where they interact with others in the same group and potentially become infected. In the first strategy, each group is divided into two subgroups (e.g., a group of customers can only go to the grocery store in the morning, while another separate group of customers can only go in the afternoon). In the second strategy, we balance the number of group members across different groups within the same sector (e.g., every grocery store has the same number of customers). The simulation results show that the dividing groups strategy substantially reduces transmission, and the joint implementation of the two strategies could effectively bring the spread of transmission under control (i.e., effective reproduction number ≈ 1.0)., Competing Interests: Competing interest statement: A.N. is a consultant to Urbanic & Associates. A.E. received a research grant from Taisho Pharmaceutical Co., Ltd., (Copyright © 2020 the Author(s). Published by PNAS.)

Effective therapies are urgently needed for the SARS-CoV-2/COVID-19 pandemic. We identified panels of fully human monoclonal antibodies (mAbs) from large phage-displayed Fab, scFv, and VH libraries by panning against the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) glycoprotein. A high-affinity Fab was selected from one of the libraries and converted to a full-size antibody, IgG1 ab1, which competed with human ACE2 for binding to RBD. It potently neutralized replication-competent SARS-CoV-2 but not SARS-CoV, as measured by two different tissue culture assays, as well as a replication-competent mouse ACE2-adapted SARS-CoV-2 in BALB/c mice and native virus in hACE2-expressing transgenic mice showing activity at the lowest tested dose of 2 mg/kg. IgG1 ab1 also exhibited high prophylactic and therapeutic efficacy in a hamster model of SARS-CoV-2 infection. The mechanism of neutralization is by competition with ACE2 but could involve antibody-dependent cellular cytotoxicity (ADCC) as IgG1 ab1 had ADCC activity in vitro. The ab1 sequence has a relatively low number of somatic mutations, indicating that ab1-like antibodies could be quickly elicited during natural SARS-CoV-2 infection or by RBD-based vaccines. IgG1 ab1 did not aggregate, did not exhibit other developability liabilities, and did not bind to any of the 5,300 human membrane-associated proteins tested. These results suggest that IgG1 ab1 has potential for therapy and prophylaxis of SARS-CoV-2 infections. The rapid identification (within 6 d of availability of antigen for panning) of potent mAbs shows the value of large antibody libraries for response to public health threats from emerging microbes., Competing Interests: Competing interest statement: W.L., C.C., Z.S., D.V.Z., J.W.M., and D.S.D. are coinventors of a patent, filed by the University of Pittsburgh on March 12, 2020, related to antibodies described in this paper. E.C.P., A.C., J.W.M., and D.S.D. are employed by Abound Bio, a company which is developing some of the antibodies for human use., (Copyright © 2020 the Author(s). Published by PNAS.)

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state's social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state's own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the "loss from anarchy" in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders., Competing Interests: Competing interest statement: D.H., J.A., D.G., and D.E. were previously employees, interns, or contractors of Facebook, and J.A. has and, until recently, D.E. had a significant financial interest in Facebook. While this paper was under editorial review, S.A. and D.E. received a grant from Facebook for other research.

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups. [ABSTRACT FROM AUTHOR]

From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study. [ABSTRACT FROM AUTHOR]

The prevalence of "long COVID" is just one of the conundrums highlighting how little we know about the lung's response to viral infection, particularly to syndromecoronavirus-2 (SARS-CoV-2), for which the lung is the point of entry. We used an in vitro human lung system to enable a prospective, unbiased, sequential single-cell level analysis of pulmonary cell responses to infection by multiple SARS-CoV-2 strains. Starting with human induced pluripotent stem cells and emulating lung organogenesis, we generated and infected three-dimensional, multi-cell-type-containing lung organoids (LOs) and gained several unexpected insights. First, SARS-CoV-2 tropism is much broader than previously believed: Many lung cell types are infectable, if not through a canonical receptor-mediated route (e.g., via Angiotensin-converting encyme 2(ACE2)) then via a noncanonical "backdoor" route (via macropinocytosis, a form of endocytosis). Food and Drug Administration (FDA)-approved endocytosis blockers can abrogate such entry, suggesting adjunctive therapies. Regardless of the route of entry, the virus triggers a lung-autonomous, pulmonary epithelial cell--intrinsic, innate immune response involving interferons and cytokine/chemokine production in the absence of hematopoietic derivatives. The virus can spread rapidly throughout human LOs resulting in mitochondrial apoptosis mediated by the prosurvival protein Bcl-xL. This host cytopathic response to the virus may help explain persistent inflammatory signatures in a dysfunctional pulmonary environment of long COVID. The host response to the virus is, in significant part, dependent on pulmonary Surfactant Protein-B, which plays an unanticipated role in signal transduction, viral resistance, dampening of systemic inflammatory cytokine production, and minimizing apoptosis. Exogenous surfactant, in fact, can be broadly therapeutic. [ABSTRACT FROM AUTHOR]

LETTER REPLY TO WEISEL: Frompolarizationtovaccinationandback Cornelia Betsch a,b,1 , Lars Korn a,b , and Robert Böhm c,d,e  In his comment (1), Ori Weisel conducted a preregistered experiment that closely followed the methodology and analysis in our paper (2). Considerably more Democrats were willing to become vaccinated compared to Republicans [experiment: 83% of Democrats vs. 44% of Republicans; recent US polls (3): 86% of Democrats vs. 56% of Republicans]. He examined whether vaccination against COVID-19 can be understoodasasocialcontractevengivenstronganimosity between existing natural groups (i.e., the opposing political preferences that separate US citizens into Democrats vs. [Extracted from the article]

REPLY TO KU'SMIERZ AND TOYOIZUMI: A network-based explanation of whymost COVID-19 infection curves are linear. As seen in figure 2A in ref. 2, the D c values for ER networks for different settings are clearly below the corresponding D c for SW networks. LETTER REPLY TO KU SMIERZ AND TOYOIZUMI: Anetwork-basedexplanationofwhymost COVID-19infectioncurvesarelinear Stefan Thurner a,b,c,1, Peter Klimek a,b , and Rudolf Hanel a,b  In a comment on our recent paper (1), the commentators point out that infection curves on small-world (SW) networks are linear only in the vicinity of the critical point (2) and that infection dynamics on complex or SW networks have already been studied, for instance,inref.3. [Extracted from the article]

Directed by a consolidated government budget constraint, we compare US monetary– fiscal responses to World Wars I and II and the War on COVID-19. [ABSTRACT FROM AUTHOR]

Characterizing the relationship between disease testing behaviors and infectious disease dynamics is of great importance for public health. Tests for both current and past infection can influence disease-related behaviors at the individual level, while population-level knowledge of an epidemic's course may feed back to affect one's likelihood of taking a test. The COVID-19 pandemic has generated testing data on an unprecedented scale for tests detecting both current infection (PCR, antigen) and past infection (serology); this opens the way to characterizing the complex relationship between testing behavior and infection dynamics. Leveraging a rich database of individualized COVID-19 testing histories in New Jersey, we analyze the behavioral relationships between PCR and serology tests, infection, and vaccination. We quantify interactions between individuals' test-taking tendencies and their past testing and infection histories, finding that PCR tests were disproportionately taken by people currently infected, and serology tests were disproportionately taken by people with past infection or vaccination. The effects of previous positive test results on testing behavior are less consistent, as individuals with past PCR positives were more likely to take subsequent PCR and serology tests at some periods of the epidemic time course and less likely at others. Lastly, we fit a model to the titer values collected from serology tests to infer vaccination trends, finding a marked decrease in vaccination rates among individuals who had previously received a positive PCR test. These results exemplify the utility of individualized testing histories in uncovering hidden behavioral variables affecting testing and vaccination. [ABSTRACT FROM AUTHOR]

Since its introduction in the human population, SARS-CoV-2 has evolved into multi-ple clades, but the events in its intrahost diversification are not well understood. Here, we compare three-dimensional (3D) self-organized neural haplotype maps (SOMs) of SARS-CoV-2 from thirty individual nasopharyngeal diagnostic samples obtained within a 19-day interval in Madrid (Spain), at the time of transition between clades 19 and 20. SOMs have been trained with the haplotype repertoire present in the mutant spectra of the nsp12- and spike (S)-coding regions. Each SOM consisted of a dominant neuron (displaying the maximum frequency), surrounded by a low-frequency neuron cloud. The sequence of the master (dominant) neuron was either identical to that of the reference Wuhan-Hu-1 genome or differed from it at one nucleotide position. Six different deviant haplotype sequences were identified among the master neurons. Some of the substitutions in the neural clouds affected critical sites of the nsp12-nsp8-nsp7 polymerase complex and resulted in altered kinetics of RNA synthesis in an in vitro primer extension assay. Thus, the analysis has identified mutations that are relevant to modification of viral RNA synthesis, present in the mutant clouds of SARS-CoV-2 quasispecies. These mutations most likely occurred during intrahost diversification in several COVID-19 patients, during an initial stage of the pandemic, and within a brief time period. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) infection has been detected in almost all organs of coronavirus disease-19 patients, although some organs do not express angiotensin-converting enzyme-2 (ACE2), a known receptor of SARS-CoV-2, implying the presence of alternative receptors and/or co-receptors. Here, we show that the ubiquitously distributed human transferrin receptor (TfR), which binds to diferric transferrin to traffic between membrane and endosome for the iron delivery cycle, can ACE2-independently mediate SARS-CoV-2 infection. Human, not mouse TfR, interacts with Spike protein with a high affinity (KD ~2.95 nM) to mediate SARS-CoV-2 endocytosis. TfR knock-down (TfR-deficiency is lethal) and overexpression inhibit and promote SARS-CoV-2 infection, respectively. Humanized TfR expression enables SARS-CoV-2 infection in baby hamster kidney cells and C57 mice, which are known to be insusceptible to the virus infection. Soluble TfR, Tf, designed peptides blocking TfR-Spike interaction and anti-TfR antibody show significant anti-COVID-19 effects in cell and monkey models. Collectively, this report indicates that TfR is a receptor/co-receptor of SARS-CoV-2 mediating SARS-CoV-2 entry and infectivity by likely using the TfR trafficking pathway. [ABSTRACT FROM AUTHOR]