Containment measures implemented by some countries to suppress the spread of COVID-19 have resulted in a slowdown of the epidemic characterized by time series of daily infections plateauing over extended periods of time. We prove that such a dynamical pattern is compatible with critical Susceptible-Infected-Removed (SIR) dynamics. In traditional analyses of the critical SIR model, the critical dynamical regime is started from a single infected node. The application of containment measures to an ongoing epidemic, however, has the effect to make the system enter in its critical regime with a number of infected individuals potentially large. We describe how such non-trivial starting conditions affect the critical behavior of the SIR model. We perform a theoretical and large-scale numerical investigation of the model. We show that the expected outbreak size is an increasing function of the initial number of infected individuals, while the expected duration of the outbreak is a non-monotonic function of the initial number of infected individuals. Also, we precisely characterize the magnitude of the fluctuations associated with the size and duration of the outbreak in critical SIR dynamics with non-trivial initial conditions. Far from heard immunity, fluctuations are much larger than average values, thus indicating that predictions of plateauing time series may be particularly challenging.

What’s the Covid State of Play? Join Dr. Margaret Bourdeaux and Professor Jonathan Zittrain, co-chairs of the Berkman Klein Center’s Digital Pandemic Response Working Group, as they try to untangle the challenges in the fight against COVID-19 in a chat with former NSC pandemic policy staffer Beth Cameron and Chief of Strategy and Policy for Partners in Health's MA COVID-19 Response KJ Seung.

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Have you ever thought that our whole political system needs radical change? That in the face of complex global challenges - a pandemic, for example - the British state is a lumbering beast still using the technology of the 20th century, if not the 19th? That our politicians aren’t as well equipped for their jobs as they could be? In this three-part series, author and broadcaster Timandra Harkness examines whether there's truth in these ideas and how our political system can change for the better. In this first episode, Timandra looks at the role forecasting, statistics and probability play in government. It’s ironic that nobody saw the coronavirus crisis coming. Because plenty of people saw it coming. A pandemic was top of the UK government risk register, and still it seemed to take everyone by surprise. Suddenly we’re all living in a world of mathematical models, projected curves and logarithmic scales. Are politicians equipped to use the data they have? Or is it time to send them back to the classroom for extra Maths?

The COVID-19 pandemic has taken a significant toll on people worldwide, and there are currently no specific antivirus drugs or vaccines. We report herein a therapeutic based on catalase, an antioxidant enzyme that can effectively breakdown hydrogen peroxide and minimize the downstream reactive oxygen species, which are excessively produced resulting from the infection and inflammatory process. Catalase assists to regulate production of cytokines, protect oxidative injury, and repress replication of SARS-CoV-2, as demonstrated in human leukocytes and alveolar epithelial cells, and rhesus macaques, without noticeable toxicity. Such a therapeutic can be readily manufactured at low cost as a potential treatment for COVID-19.

A dysregulated immune response against the SARS-CoV-2 virus plays a critical role in severe COVID-19. However, the molecular and cellular mechanisms by which the virus causes lethal immunopathology are poorly understood. Here, we utilize multi-omics single-cell analysis to probe dynamic immune responses in patients with stable or progressive manifestations of COVID-19, and assess the effects of tocilizumab, an anti-IL-6 receptor monoclonal antibody. Coordinated profiling of gene expression and cell lineage protein markers reveals a prominent type-1 interferon response across all immune cells, especially in progressive patients. An anti-inflammatory innate immune response and a pre-exhaustion phenotype in activated T cells are hallmarks of progressive disease. Skewed T cell receptor repertoires in CD8 T cells and uniquely enriched V(D)J sequences are also identified in COVID-19 patients. B cell repertoire and somatic hypermutation analysis are consistent with a primary immune response, with possible contribution from memory B cells. Our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19, which may contribute to delayed virus clearance and has implications for therapeutic intervention.

BackgroundCOVID-19 has the potential to cause substantial disruptions to health services, due to cases overburdening the health system or response measures limiting usual programmatic activities. We aimed to quantify the extent to which disruptions to services for HIV, tuberculosis, and malaria in low-income and middle-income countries with high burdens of these diseases could lead to additional loss of life over the next 5 years.MethodsAssuming a basic reproduction number of 3·0, we constructed four scenarios for possible responses to the COVID-19 pandemic: no action, mitigation for 6 months, suppression for 2 months, or suppression for 1 year. We used established transmission models of HIV, tuberculosis, and malaria to estimate the additional impact on health that could be caused in selected settings, either due to COVID-19 interventions limiting activities, or due to the high demand on the health system due to the COVID-19 pandemic.FindingsIn high-burden settings, deaths due to HIV, tuberculosis, and malaria over 5 years could increase by up to 10%, 20%, and 36%, respectively, compared with if there was no COVID-19 pandemic. The greatest impact on HIV was estimated to be from interruption to antiretroviral therapy, which could occur during a period of high health system demand. For tuberculosis, the greatest impact would be from reductions in timely diagnosis and treatment of new cases, which could result from any prolonged period of COVID-19 suppression interventions. The greatest impact on malaria burden could be as a result of interruption of planned net campaigns. These disruptions could lead to a loss of life-years over 5 years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV and tuberculosis epidemics.InterpretationMaintaining the most critical prevention activities and health-care services for HIV, tuberculosis, and malaria could substantially reduce the overall impact of the COVID-19 pandemic.

We show that SARS-CoV-2 spike protein interacts with cell surface heparan sulfate and angiotensin converting enzyme 2 (ACE2) through its Receptor Binding Domain. Docking studies suggest a putative heparin/heparan sulfate-binding site adjacent to the domain that binds to ACE2. In vitro, binding of ACE2 and heparin to spike protein ectodomains occurs independently and a ternary complex can be generated using heparin as a template. Contrary to studies with purified components, spike protein binding to heparan sulfate and ACE2 on cells occurs codependently. Unfractionated heparin, non-anticoagulant heparin, treatment with heparin lyases, and purified lung heparan sulfate potently block spike protein binding and infection by spike protein-pseudotyped virus and SARS-CoV-2 virus. These findings support a model for SARS-CoV-2 infection in which viral attachment and infection involves formation of a complex between heparan sulfate and ACE2. Manipulation of heparan sulfate or inhibition of viral adhesion by exogenous heparin may represent new therapeutic opportunities.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely affected health care workers (HCWs).1 As a result, hospital systems began testing HCWs2 and implementing infection control measures to mitigate workforce depletion and prevent disease spread.3 Mass General Brigham (MGB) is the largest health care system in Massachusetts, with 12 hospitals and more than 75 000 employees. In March 2020, MGB implemented a multipronged infection reduction strategy involving systematic testing of symptomatic HCWs and universal masking of all HCWs and patients with surgical masks.4 This study assessed the association of hospital masking policies with the SARS-CoV-2 infection rate among HCWs.

Background: Hydroxychloroquine and chloroquine have been proposed as treatments for coronavirus disease 2019 (COVID-19) on the basis of in vitro activity, uncontrolled data, and small randomized studies. Methods: The Randomised Evaluation of COVID-19 therapy (RECOVERY) trial is a randomized, controlled, open-label, platform trial comparing a range of possible treatments with usual care in patients hospitalized with COVID-19. We report the preliminary results for the comparison of hydroxychloroquine vs. usual care alone. The primary outcome was 28-day mortality. Results: 1561 patients randomly allocated to receive hydroxychloroquine were compared with 3155 patients concurrently allocated to usual care. Overall, 418 (26.8%) patients allocated hydroxychloroquine and 788 (25.0%) patients allocated usual care died within 28 days (rate ratio 1.09; 95% confidence interval [CI] 0.96 to 1.23; P=0.18). Consistent results were seen in all pre-specified subgroups of patients. Patients allocated to hydroxychloroquine were less likely to be discharged from hospital alive within 28 days (60.3% vs. 62.8%; rate ratio 0.92; 95% CI 0.85-0.99) and those not on invasive mechanical ventilation at baseline were more likely to reach the composite endpoint of invasive mechanical ventilation or death (29.8% vs. 26.5%; risk ratio 1.12; 95% CI 1.01-1.25). There was no excess of new major cardiac arrhythmia. Conclusions: In patients hospitalized with COVID-19, hydroxychloroquine was not associated with reductions in 28-day mortality but was associated with an increased length of hospital stay and increased risk of progressing to invasive mechanical ventilation or death

SARS-CoV-2 outbreak is the first pandemic of the century. SARS-CoV-2 infection is transmitted through droplets; other transmission routes are hypothesized but not confirmed. So far, it is unclear whether and how SARS-CoV-2 can be transmitted from the mother to the fetus. We demonstrate the transplacental transmission of SARS-CoV-2 in a neonate born to a mother infected in the last trimester and presenting with neurological compromise. The transmission is confirmed by comprehensive virological and pathological investigations. In detail, SARS-CoV-2 causes: (1) maternal viremia, (2) placental infection demonstrated by immunohistochemistry and very high viral load; placental inflammation, as shown by histological examination and immunohistochemistry, and (3) neonatal viremia following placental infection. The neonate is studied clinically, through imaging, and followed up. The neonate presented with neurological manifestations, similar to those described in adult patients.

An effective vaccine is needed to halt the spread of the SARS-CoV-2 pandemic. Recently, we reported safety, tolerability and antibody response data from an ongoing placebo-controlled, observer-blinded phase 1/2 COVID-19 vaccine trial with BNT162b1, a lipid nanoparticle (LNP) formulated nucleoside-modified messenger RNA encoding the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Here we present antibody and T cell responses after BNT162b1 vaccination from a second, non-randomized open-label phase 1/2 trial in healthy adults, 18-55 years of age. Two doses of 1 to 50 μg of BNT162b1 elicited robust CD4+ and CD8+ T cell responses and strong antibody responses, with RBD-binding IgG concentrations clearly above those in a COVID-19 convalescent human serum panel (HCS). Day 43 SARS-CoV-2 serum neutralising geometric mean titers were 0.7-fold (1 μg) to 3.5-fold (50 μg) those of HCS. Immune sera broadly neutralised pseudoviruses with diverse SARS-CoV-2 spike variants. Most participants had TH1 skewed T cell immune responses with RBD-specific CD8+ and CD4+ T cell expansion. Interferon (IFN)γ was produced by a high fraction of RBD-specific CD8+ and CD4+ T cells. The robust RBD-specific antibody, T-cell and favourable cytokine responses induced by the BNT162b1 mRNA vaccine suggest multiple beneficial mechanisms with potential to protect against COVID-19.

As school districts around the country reveal reopening guidelines for when students return to classrooms in the fall, many parents are concerned about how the coronavirus could affect their children.The White House has provided confusing statements. Press secretary Kayleigh McEnany said Thursday "science should not stand in the way" of schools reopening, though "science is on our side."A compilation of studies and articles published in JAMA Pediatrics found most patients under the age of 18 with COVID-19, the illness caused by SARS-CoV-2, have mild symptoms and fully recover within one to two weeks – quicker than most adults. Though this may alleviate some concerns, medical experts said parents shouldn’t let their guard down.

Results from trials involving three different coronavirus vaccines released Monday, all showing positive results, with evidence the vaccines can produce immune responses that would be expected to protect people against infection.They all also appeared to be safe, although it will take studies with more people to show how safe they really are and whether they can prevent infection.

President Donald Trump’s extraordinary interview with “Fox News Sunday” host Chris Wallace, who frequently fact-checked the president in real time, contained a long list of false, misleading and unsubstantiated claims: The president claimed other countries, later mentioning Europe, are only testing for COVID-19 if someone is “really sick” and that the “massive” testing in the U.S. “skews the numbers.” But testing data show many countries, including in Europe, have conducted more tests per confirmed case than the U.S. Trump falsely claimed that there’s “no reason” — “[e]xcept for Nov. 3rd” — for California to pull back on reopening its economy. Multiple states, including California, have concerning trends that public health experts — including those on the White House’s coronavirus task force — say warrant business closures and other precautions. Trump claimed he “forced” Seattle officials to finally end protests near a police precinct because “they heard” that “we were going in that following day.” Seattle’s mayor said her decision had nothing to do with Trump, who she said never notified her of a plan to send in federal forces. Trump falsely claimed credit for “the biggest pay raises in the history of our military.” The largest increase in basic military pay under Trump was 3.1%. It was higher than that seven times in the last 19 years — including a high of 5% in 2002.

Following announcement of the first local area lockdown – where it has been widely quoted that Leicester accounted for 10 per cent of all positive cases in the country over the last week – there has been an even greater interest in the local area data on COVID-19 cases and tests.To meet this immediate need for data in the public domain Public Health England (PHE) has published the data underlying the graphic in its weekly surveillance report. Figure 9 in the accompanying dataset provides the weekly rate of COVID-19 cases of people tested under Pillars 1 and 2 per 100,000 population by upper-tier local authority in England. The rate of cases in Leicester shown in the table (140.2 cases per 100,000 population) is more than double the rate in any other local authority area.It is likely demand for local area data on COVID-19 will continue to increase and we understand health bodies are expecting to publish further helpful data over the coming days.OSR will continue to work with producers of statistics on COVID-19 to make the case for improved data in the public domain. You can see more of our work in this area on our dedicated COVID-19 webpage.

Boris Johnson’s warning comes after Spain is critical of UK for extending non-essential travel advice to Canary and Balearic islands

Around the world, governments have been asking their citizens to substantially change their behaviour for a prolonged period of time, by practising physical distancing and staying at home, to contain the spread of Covid-19. Are happier people more willing to comply with these measures? Using three independent surveys covering about 119,000 adult respondents across 35 countries, including longitudinal data from the UK, we found that past and present happiness predicts compliance during lockdown. The relationship is stronger for those with higher levels of happiness. A negative mood, or loss in happiness, predicts lower compliance. We explored risk-avoidance and pro-social motivations for this relationship, and found that motivations for compliance are not uniformly distributed but dependent on personal characteristics and context: people who are older or have certain medical preconditions seem to be predominantly motivated by risk-avoidance, whereas motivations of people who are less at risk of Covid-19 seem more mixed. Our findings have implications for policy design, targeting, and communication.

Following testing in clinical trials, the use of remdesivir for treatment of COVID-19 has been authorized for use in parts of the world, including the USA and Europe. These early authorizations were largely based on results from two clinical trials. A third study published by Wang et al. was deemed inconclusive. We demonstrate the utility of Bayesian reanalyses in the context of non-significant results like the Wang et al. trial. Results of a reanalysis of the three trials show ambiguous evidence for the primary outcome of clinical improvement and moderate evidence against efficacy of remdesivir for the secondary outcome of mortality rate. We recommend that regulatory bodies take all available evidence into account for endorsement decisions.

The most basic level of human interaction is that which happens at close proximity: conversations, physical commerce, education, sports, entertainment and transportation, are all activities that are possible through close proximity without the use of technology. The way in which people physically interact is a major component in the complexity found in human societies. Mexico City, the largest city in Mexico, is as well one of the largest cities in the world, with over 9 million inhabitants and concentrating the vast majority of government and business centers. Reconstructing the set of physical interactions between its inhabitants could lead to a better understanding of the city's complexity. Such contact network can be used for applications that range from urban planning to the spread of infectious agents. In this work, we used anonymized location data from mobile devices (cellphones) collected in the month of February 2020 to reconstruct contact networks for several days, based on physical proximity of these devices. We found that contact networks in Mexico City are very sparse, with a heavy tailed degree distribution, characterized by a largest connected component that contains between 1010 and 20%20\% of devices. We found that, while network topologies are different each day, there is no consistent difference in these between weekdays and weekends. We present this results along with the release of an anonymized network reconstructed with data from Februrary 18th, 2020, as the first description of a contact network of Mexico City

Nudging and behavioural insights need to be taken into account in strategic action planning for suppression and reduction of pandemic effects. This paper focuses on a few selected areas of significant importance to getting SAR-CoV2 under control in both the non-pharmaceutical and the pharmaceutical phase, i.e. after the development of an effective

The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) emerged in the city of Wuhan, Hubei Province, China, in late 2019. Since then, the virus has spread globally and caused a pandemic. Assays that can measure the antiviral activity of antibodies or antiviral compounds are needed for SARS‐CoV‐2 vaccine and drug development. Here, we describe in detail a microneutralization assay, which can be used to assess in a quantitative manner if antibodies or drugs can block entry and/or replication of SARS‐CoV‐2 in vitro.

SARS-CoV-2 has caused a global pandemic with millions infected and numerous fatalities. Questions regarding the robustness, functionality and longevity of the antibody response to the virus remain unanswered. Here we report that the vast majority of infected individuals with mild-to-moderate COVID-19 experience robust IgG antibody responses against the viral spike protein, based on a dataset of 19,860 individuals screened at Mount Sinai Health System in New York City. We also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2. Our data suggests that more than 90% of seroconverters make detectible neutralizing antibody responses and that these titers are stable for at least the near-term future.

SARS-CoV-2 has caused a global pandemic with millions infected and numerous fatalities. Questions regarding the robustness, functionality and longevity of the antibody response to the virus remain unanswered. Here we report that the vast majority of infected individuals with mild-to-moderate COVID-19 experience robust IgG antibody responses against the viral spike protein, based on a dataset of 19,860 individuals screened at Mount Sinai Health System in New York City. We also show that titers are stable for at least a period approximating three months, and that anti-spike binding titers significantly correlate with neutralization of authentic SARS-CoV-2. Our data suggests that more than 90% of seroconverters make detectible neutralizing antibody responses and that these titers are stable for at least the near-term future.

21% of reported cases affect Black people, who make up only 9% of the city's overall population

In July 2020, two of the largest funders of mental health research worldwide — the National Institute of Mental Health (NIMH) and the Wellcome Trust — announced plans to standardize mental health measurement. Specifically, obtaining funding for research related to depression and anxiety will be conditional on using four specific measures.While we agree that there are obvious benefits to standardizing mental health measurement, some of which are discussed in the announcement by NIMH and Wellcome, in this paper we focus on potential unintended negative consequences of this initiative, and lay out recommendations for how some of these might be mitigated.

The sensible and conflicting scenario of the pandemic postulated many challenges to societies around the world in 2020. Part of this problem refers to how the differences between politics and science are not comprehended in their particularities. The recognition of limits and power of science and politics can not only contribute to reaching the actions and strategies facing novel coronavirus but also optimized many domains of societies post-pandemic.

By many accounts politics is becoming more polarized, yielding dire consequences for democracy and trust in government. Yet a growing body of research on “false polarization” finds that perceptions of “what the other side believes” are inaccurate–specifically, overly pessimistic–and that these inaccuracies exacerbate intergroup conflict. Through a review of existing work and a reanalysis of published data, we (i) develop a typology of the disparate phenomena that are labeled “polarization,” (ii) use that typology to understand when polarization is “false,” and (iii) identify when false polarization gives rise to true polarization (e.g., extreme issue attitudes and prejudice). We further suggest that a specific psychological domain is ideal for developing corrective interventions: meta-perception, one’s judgment of how they are perceived by others. We review evidence indicating that correcting meta-perception inaccuracies is effective at reducing intergroup conflict, and discuss methods for precisely measuring meta-perception accuracy. We argue that the reputational nature of meta-perception provides a motivational mechanism by which individuals are sensitive to the truth, even when those truths pertain to the “other side.” We conclude by discussing how these insights can be integrated into existing research across multiple disciplines seeking to understand polarization and its negative consequences.

In their Comment, the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) clinical characterisation group outline how harmonisation of clinical characterisation studies is achieved through their collaborative resource-sharing and data-sharing platform.1ISARIC clinical characterisation groupGlobal outbreak research: harmony not hegemony.Lancet Infect Dis. 2020; 20: 770-772Summary Full Text Full Text PDF PubMed Scopus (0) Google Scholar We fully agree with both the importance of international harmonisation and the authors' approach. Yet, in our opinion, they could have expressed more clearly how important harmonisation is to use resources in research responsibly and efficiently.

Background Conspiracy theories about the origins of COVID-19 are wide-spread and have even been propagated by highly ranked state officials and politicians in the U.S. Health authorities have cautioned that such theories, although not questioning the existence of the pandemic, may increase the spread of the virus by reducing people’s efforts to socially distance. Methods We test this proposition empirically using longitudinal survey data collected at five time points during the early outbreak of the virus in the U.S. (N = 403). Results Multivariate growth curve analyses showed that, although conspiracy beliefs decreased and social distancing increased over time, people holding more conspiracy beliefs at the beginning of the pandemic showed the lowest increase in social distancing. Moreover, cross-lagged analyses demonstrated that people who reported more conspiracy beliefs at any wave tended to report less social distancing at the following wave. Conclusions Our findings show that COVID-19 conspiracy theories pose a significant threat to public health as they may reduce adherence to social distancing measures. Keywords Conspiracy theories, COVID-19, social distancing, longitudinal

Transparency is indispensable for accuracy and correction in science, and is discussed frequently in the credibility revolution. A less often mentioned aspect of credibility is the need for intellectual humility: When scientific communication is overconfident or contains too many exaggerations, the field stands to lose its credibility, even if the methods and statistics underlying the research are sound. We argue that intellectual humility is given a great deal of lip service, but is too rarely valued - we may say that we as scientists ought to be intellectually humble, but our actions as a field suggest that this is not a priority. Although we acknowledge that intellectual humility is presented as a widely accepted scientific norm, we argue that current research practice does not actually incentivize intellectual humility. A promising solution could be to use our roles as reviewers to incentivize authors putting the flaws and uncertainty in their work front and center, thus giving their critics ammunition to find their errors. We describe several ways reviewers (and authors) can contribute to increasing humility in practice, instead of passively waiting for the system to change

The ongoing COVID-19 pandemic has created a global crisis of massive scale. Prior research indicates that human mobility is one of the key factors involved in viral spreading. Indeed, in a connected planet, rapid world-wide spread is enabled by long-distance air-, land- and sea-transportation among countries and continents, and subsequently fostered by commuting trips within densely populated cities. While early travel restrictions contribute to delayed disease spread, their utility is much reduced if the disease has a long incubation period or if there is asymptomatic transmission. Given the lack of vaccines, public health officials have mainly relied on non-pharmaceutical interventions, including social distancing measures, curfews, and stay-at-home orders. Here we study the impact of city organization on its susceptibility to disease spread, and amenability to interventions. Cities can be classified according to their mobility in a spectrum between compact-hierarchical and decentralized-sprawled. Our results show that even though hierarchical cities are more susceptible to the rapid spread of epidemics, their organization makes mobility restrictions quite effective. Conversely, sprawled cities are characterized by a much slower initial spread, but are less responsive to mobility restrictions. These findings hold globally across cities in diverse geographical locations and a broad range of sizes. Our empirical measurements are confirmed by a simulation of COVID-19 spread in urban areas through a compartmental model. These results suggest that investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove most helpful in containing and reducing the impact of present and future pandemics.

Lockdown measures and school closures affect girls and women differently across the world and may have long-term negative consequences. Talha Burki reports.Soon after the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) last winter, it became apparent that mortality rates were higher for men than for women. This remains the case but it is women who are more likely to bear the brunt of the social and economic consequences of the pandemic. Earlier this year, UN Secretary-General António Guterres noted that “COVID-19 could reverse the limited progress that has been made on gender equality and women's rights”. With the pandemic set to stretch into 2021, his words are looking less like a warning than a prediction

The COVID-19 pandemic is undermining nutrition across the world, particularly in low-income and middle-income countries (LMICs).1United NationsThe impact of COVID-19 on food security and nutrition.http://www.fao.org/policy-support/tools-and-publications/resources-details/es/c/1287907/Date: 2020Date accessed: July 21, 2020Google Scholar The worst consequences are borne by young children. Some of the strategies to respond to COVID-19—including physical distancing, school closures, trade restrictions, and country lockdowns—are impacting food systems by disrupting the production, transportation, and sale of nutritious, fresh, and affordable foods, forcing millions of families to rely on nutrient-poor alternatives. Strained health systems and interruptions in humanitarian response are eroding access to essential and often life-saving nutrition services.2Roberton T Carter ED Chou VB et al.Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study.Lancet Glob Health. 2020; 8: e901-e908Summary Full Text Full Text PDF PubMed Scopus (27) Google Scholar Social protection systems in many LMICs are overloaded as vulnerable families struggle to access the food and services they need in the context of an economic downturn.Malnutrition could exacerbate the effects of COVID-19 in mothers and children. At the same time, more children are becoming malnourished due to the deteriorating quality of their diets, interruptions in nutrition and other essential services, and the socioeconomic shocks created by the pandemic in LMICs. New estimates by Derek Headey and colleagues3Headey D Heidkamp R Osendarp S et al.Impacts of COVID-19 on childhood malnutrition and nutrition-related mortality.Lancet. 2020; (published online July 27.)https://doi.org/10.1016/S0140-6736(20)31647-0Google Scholar in an accompanying Comment in The Lancet suggest that without timely action, the global prevalence of child wasting could rise by a shocking 14·3%. With an estimated 47 million children younger than 5 years affected by wasting globally before the COVID-19 pandemic,4UNICEFWHOWorld Bank GroupJoint malnutrition estimates, 2020 edition.https://www.who.int/publications/i/item/jme-2020-editionDate: 2020Date accessed: July 21, 2020Google Scholar this would translate to an estimated additional 6·7 million children with wasting during the first 12 months of the pandemic—80% of them in sub-Saharan Africa and south Asia—and more than 10 000 additional child deaths per month during this same period.3

Social influence is an important determinant of adolescent risk-taking, but little is known about how social norms contribute to adolescents’ tendency to recommend and engage in risky behaviours. Using the Adolescent Risk-taking Questionnaire, we assessed subjects’ (n=198, age=10-26) propensity to engage in and recommend risk-taking as well as their perception of risk-norms. Adolescents recommended risk-taking more often compared to children and young adults. Perceived social norms were the most important factor predicting engagement in risky behaviours, and adolescents perceived risk-taking to be more normative than children or adults. Our findings highlight two mechanisms that contribute to adolescent risk-taking; active recommendation and perceived social norms. On this basis, we discuss potential means to attenuate excessive adolescent risk-taking.

To slow COVID-19, many countries have shut down part of the economy. Older individuals have the most to gain from slowing virus diffusion. Younger workers in sectors that are shuttered have most to lose. In this paper, we build a model in which economic activity and disease progression are jointly determined. Individuals differ by age (young, retired), by sector (basic, luxury), and health status. Disease transmission occurs in the workplace, through consumption, at home, and in hospitals. We study the optimal economic mitigation policy for a government that can redistribute across individuals, but where redistribution is costly. Optimal redistribution and mitigation policies interact, and more modest shutdowns are optimal when redistribution is more costly. We find that the shutdowns that were implemented in mid-April were too extensive, but a partial shutdown should remain in place through the fall. A deeper and longer shutdown is preferred if a vaccine is imminent.

We develop a heterogeneous-agents network-based model to analyze alternative policies during a pandemic outbreak, accounting for health and economic trade-offs within the same empirical framework. We leverage a variety of data sources, including data on individuals' mobility and encounters across metropolitan areas, health records, and measures of the possibility to be productively working from home. This combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions. We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas. Our results highlight how outcomes vary across areas in relation to the underlying heterogeneity in population density, social network structures, population health, and employment characteristics. We find that policies by which individuals who can work from home continue to do so, or in which schools and firms alternate schedules across different groups of students and employees, can be effective in limiting the health and healthcare costs of the pandemic outbreak while also reducing employment losses.

This short note constructs Mobility Zones to facilitate the discussion on the geographic extent of individual mobility restrictions to control the spread of Covid-19. Mobility Zones are disjoint sets of counties where a given level of individual mobility directly or indirectly connects all counties within each set. I compute Mobility Zones for the United States and each state using smartphone-based mobility data between counties. The average area and population of Mobility Zones have slightly grown at the onset of the epidemic and have sharply shrunk afterward. Pre-Covid-19 Mobility Zones may be useful in calibrating quantitative studies of targeted restriction policies, or for policymakers deciding on the adoption of specific mobility measures. Two examples suggest the use of Mobility Zones to inform within-state differences and cross-state coordination in mobility restriction policies.

We present a theory of Keynesian supply shocks: supply shocks that trigger changes in aggregate demand larger than the shocks themselves. We argue that the economic shocks associated to the COVID-19 epidemic—shutdowns, layoffs, and firm exits—may have this feature. In one-sector economies supply shocks are never Keynesian. We show that this is a general result that extend to economies with incomplete markets and liquidity constrained consumers. In economies with multiple sectors Keynesian supply shocks are possible, under some conditions. A 50% shock that hits all sectors is not the same as a 100% shock that hits half the economy. Incomplete markets make the conditions for Keynesian supply shocks more likely to be met. Firm exit and job destruction can amplify the initial effect, aggravating the recession. We discuss the effects of various policies. Standard fiscal stimulus can be less effective than usual because the fact that some sectors are shut down mutes the Keynesian multiplier feedback. Monetary policy, as long as it is unimpeded by the zero lower bound, can have magnified effects, by preventing firm exits. Turning to optimal policy, closing down contact-intensive sectors and providing full insurance payments to affected workers can achieve the first-best allocation, despite the lower per-dollar potency of fiscal policy.

How should un­em­ploy­ment benefits vary in response to the economic crisis induced by the COVID-19 pandemic? We answer this question by computing the optimal un­em­ploy­ment insurance response to the COVID-​induced recession.We compare the optimal policy to the pro­vi­sions under the CARES Act—which sub­stan­tially expanded un­em­ploy­ment insurance and sparked an ongoing debate over further increases—and several al­ter­na­tive scenarios. We find that it is optimal first to raise un­em­ploy­ment benefits but then to begin lowering them as the economy starts to reopen — despite un­em­ploy­ment remaining high. We also find that the $600 UI sup­ple­ment payment im­ple­mented under CARES was close to the optimal policy. Extending this UI sup­ple­ment for another six months would hamper the recovery and reduce welfare. On the other hand, a UI extension combined with a re-​employment bonus would further increase welfare compared to CARES alone, with only minimal effects on un­em­ploy­ment.

Barely having had the time to digest the economic and social af­ter­shocks of the Great Recession, European welfare states are con­fronted with the even more dis­rup­tive coro­n­avirus pandemic as probably, threat­en­ing the life of the more vul­ner­a­ble, while incurring job losses for many as the con­se­quence of the temporal “freezing of the economy” by lockdown measures. Before the Covid-19 virus struck, the new face of the digital trans­for­ma­tion and the rise of the ‘platform’ economy already raised ex­is­ten­tial questions for future welfare provision. The Great Lockdown – if anything – is bound to ac­cel­er­ate these trends. Greater au­toma­tion will reinforce working from home to reduce Covid-19 virus trans­mis­sion risks. At the same time, the Great Lockdown will reinforce in­equal­ity, as the poor find it more difficult to work from home, while low-paid workers in essential service in health care, su­per­mar­ket retail, postal services, security and waste disposal, continue to face contagion risks. And although popular con­jec­tures of ‘jobless growth’ and ‘routine-​biased’ job po­lar­iza­tion, driven by dig­i­ti­za­tion and ar­ti­fi­cial in­tel­li­gence, may still be overblown, intrusive change in the nature of work and em­ploy­ment relations require fun­da­men­tal re­think­ing of extant labour market reg­u­la­tion and social pro­tec­tion. Inspired more by adverse family de­mog­ra­phy than tech­no­log­i­cal change, social in­vest­ment reform has been the fil rouge of welfare re­cal­i­bra­tion since the turn of the century. Is social in­vest­ment reform still valid in the new era of ‘dis­rup­tive’ tech­no­log­i­cal trans­for­ma­tion in aftermath of Coro­n­avirus pandemic that is likely to turn into the worst recession since the second world war? Em­pir­i­cally, this chapter explores how Germany, Italy and the Nether­lands, in terms of the strengths and vul­ner­a­bil­i­ties of their labour market to dig­i­ti­za­tion, together with their re­spec­tive social in­vest­ment aptitude, are currently preparing their welfare states for the in­ten­si­fi­ca­tion of tech­no­log­i­cal change in the decade ahead.

We measure the effect of lockdown policies on em­ploy­ment and GDP across countries using individual-​ and sector-​level data. Em­ploy­ment effects depend on the ability to work from home, which ranges from about half of total em­ploy­ment in rich countries to around 35% in poor countries. This gap reflects dif­fer­ences in oc­cu­pa­tional com­po­si­tion, self-​employment levels, and in­di­vid­ual char­ac­ter­is­tics across countries. GDP effects of lockdown policies also depend on countries’ sectoral structure. Losses in poor countries are at­ten­u­ated by their higher value-​added share in essential sectors, notably agri­cul­ture. Overall, a realistic lockdown policy implies GDP losses of 20-25% on an an­nu­al­ized basis.

We present findings from a novel survey of Italian, British, and American families in lockdown during the COVID-19 pandemic of spring 2020. A high per­cent­age report dis­rup­tions in the patterns of family life, man­i­fest­ing in new work patterns, chore al­lo­ca­tions and household tensions. Though men have taken a greater share of childcare and grocery shopping duties, re­al­lo­ca­tions are not nearly as stark as dis­rup­tions to work patterns might suggest, and families having to re­al­lo­cate duties report greater tensions. Our results paint a picture of tightened con­straints budging up against stable and gendered patterns of intra-​household co­op­er­a­tion. While the long-run con­se­quences of the COVID-19 lockdown on family life cannot be assessed at this stage, we point towards the likely op­por­tu­ni­ties and chal­lenges.

As the World Health Organization (WHO) baldly reminds us, “there are no specific vaccines or treatments for COVID-19”. However, trials of treatments are taking place. Some have shown promise in helping those infected by calming an overreacting immune system or targeting the coronavirus – either by destroying it or stopping it from replicating.

Provisional counts of the number of deaths registered in England and Wales, including deaths involving the coronavirus (COVID-19) pandemic, by age, sex and region, in the latest weeks for which data are available.

The Susceptible-Infectious-Recovered (SIR) model is the canonical model of epidemics of infections that make people immune upon recovery. Many of the open questions in computational epidemiology concern the underlying contact structure's impact on models like the SIR model. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this article, we discuss the detailed assumptions behind such simulations -- how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also present a highly optimized, open-source code for this purpose and discuss all steps needed to make the program as fast as possible.

Sparked by the killing of George Floyd in police custody, the 2020 Black Lives Matter protests have brought a new wave of attention to the issue of in­equal­ity within criminal justice. However, many public health officials have warned that mass protests could lead to a reduction in social dis­tanc­ing behavior, spurring a resur­gence of COVID-19. This study uses newly collected data on protests in 315 of the largest U.S. cities to estimate the impacts of mass protests on social dis­tanc­ing and COVID-19 case growth. Event-​study analyses provide strong evidence that net stay-​at-home behavior increased following protest onset, con­sis­tent with the hy­poth­e­sis that non-​protesters’ behavior was sub­stan­tially affected by urban protests. This effect was not fully explained by the im­po­si­tion of city curfews. Estimated effects were generally larger for per­sis­tent protests and those ac­com­pa­nied by media reports of violence. Fur­ther­more, we find no evidence that urban protests reignited COVID-19 case growth during the more than three weeks following protest onset. We conclude that pre­dic­tions of broad negative public health con­se­quences of Black Lives Matter protests were far too narrowly conceived.

On June 20, 2020, President Donald J. Trump held his first mass campaign rally following the outbreak of COVID-19. Held in Tulsa, Oklahoma, the political gathering attracted 6,000 to 12,000 individuals to the indoor Bank of Oklahoma (BOK) arena. This study is the first to explore the impact of this event on the spread of COVID-19. First, using data from Safegraph Inc, we show that while non-resident visits to census block groups hosting the Trump event grew by approximately 25 percent, there was no decline in net stay-at-home behavior in Tulsa county, reflecting important offsetting behavioral effects. Then, using data on coronavirus cases from the Centers for Disease Control and Prevention (CDC) and a synthetic control design, we find little evidence that COVID-19 case growth grew more rapidly in Tulsa County, its border counties, or in the state of Oklahoma than each’s estimated counterfactual in the three weeks following the campaign rally. Difference-in-differences estimates further provide no evidence that COVID-19 case rates grew faster in counties that drew relatively larger shares of residents to the event. We conclude that offsetting behavioral responses to the rally — including voluntary closures of restaurants and bars in downtown Tulsa, increases in stay-at-home behavior, displacement of usual activities of weekend inflows, and smaller-than-expected crowd attendance — may be important mechanisms.

On March 19, 2020, California Governor Gavin Newsom issued Executive Order N-33-20 2020, which required all residents of the state of California to shelter in place for all but essential activities such as grocery shopping, retrieving prescriptions from a pharmacy, or caring for relatives. This shelter-in-place order (SIPO), the first such statewide order issued in the United States, was designed to reduce COVID-19 cases and mortality. While the White House Task Force on the Coronavirus has credited the State of California for taking early action to prevent a statewide COVID-19 outbreak, no study has examined its impact. This study is the first to estimate the effect of SIPO adoption on health. Using daily state-level coronavirus data and a synthetic control research design, we find that California’s statewide SIPO reduced COVID-19 cases by 125.5 to 219.7 per 100,000 population by April 20, one month following the order. We further find that California’s SIPO led to as many as 1,661 fewer COVID-19 deaths during this period. Back-of-the-envelope calculations suggest that there were about 400 job losses per life saved during this short-run post-treatment period.

On April 7, 2020, Wisconsin held a major election for state positions and presidential preferences for both major parties. News reports showed pictures of long lines of voters due to fewer polling locations and suggested that the election may further the spread of the SARS-CoV-2 virus. A contact-tracing analysis by the Wisconsin Department of Health Services identified 71 confirmed cases of COVID-19 to in-person voting, but no research has conducted a broader analysis of the extent to which in-person voting increased the number of COVID-19 cases. We use county level data on voting and COVID-19 tests to connect the election to the spread of the SARS-CoV-2 virus. We find a statistically and economically significant association between in-person voting and the spread of COVID-19 two to three weeks after the election. Results indicate that on average a 10% difference in in-person voters per polling location between counties is associated with approximately a 17.7% increase in the positive test rate. Further, extrapolation of estimates from the average county suggests that in-person voting was related to approximately 700 more COVID-19 cases in Wisconsin during the weeks following the election, or about 7.7% of the total number of confirmed cases during the five week post-treatment time period studied.

In order to understand the impact of the COVID-19 pandemic on higher education, we surveyed approximately 1,500 students at one of the largest public institutions in the United States using an instrument designed to recover the causal impact of the pandemic on students' current and expected outcomes. Results show large negative effects across many dimensions. Due to COVID-19: 13% of students have delayed graduation, 40% lost a job, internship, or a job offer, and 29% expect to earn less at age 35. Moreover, these effects have been highly heterogeneous. One quarter of students increased their study time by more than 4 hours per week due to COVID-19, while another quarter decreased their study time by more than 5 hours per week. This heterogeneity often followed existing socioeconomic divides; lower-income students are 55% more likely to have delayed graduation due to COVID-19 than their higher-income peers. Finally, we show that the economic and health related shocks induced by COVID-19 vary systematically by socioeconomic factors and constitute key mediators in explaining the large (and heterogeneous) effects of the pandemic.

We investigate how the coronavirus pandemic affected the demand for online food shopping services using data from the largest agri-food e-commerce platform in Taiwan. We find that an additional confirmed case of COVID-19 increased sales by 5.7% and the number of customers by 4.9%. The demand for grains, fresh fruit and vegetables, and frozen foods increased the most, which benefited small farms over agribusinesses. Online food shopping was highly responsive to COVID-19 media coverage and online content. Because Taiwan did not impose a stay-at-home order, the demand for online food shopping may be similar in other countries after they lift mobility restrictions.

This case study compares the importance of prevailing market factors against that of COVID-19 dynamics and policy responses in explaining the evolution of Eurozone (EZ) sovereign spreads during the first half of 2020. Focusing on daily Eurozone CDS spreads, we adopt a multi-stage econometric approach. First, we estimate a multi-factor model for changes in EZ CDS spreads over the pre-COVID-19 period of January 2014 through June 2019. Then, we apply a synthetic control-type procedure to extrapolate model-implied changes in the CDS from July 2019 through June 2020. We find that the factor model does very well in tracing the realized sovereign spreads over the rest of 2019, but breaks down during the pandemic – diverging substantially in March 2020. In the second stage, focusing specifically on the 2020 period, we find that the March 2020 divergence is well accounted for by COVID-specific risks and associated policies. In particular, mortality outcomes and policy announcements, rather than traditional determinants like fiscal space and systematic risk, drove CDS adjustment over this period. Daily CDS spread widening ceased almost immediately after the ECB announced the PEPP, but the divergence between actual and model-implied changes persisted. This divergence can be traced back to the fact that fiscally secure EZ Core countries saw spreads widen further than implied – comparable to the widening of more fragile countries - as several of the Core countries were hit hard by COVID-19. Taken all together, this points to COVID-19 Dominance: The widening spreads during the pandemic induced by COVID-specific risks and fiscal responses has led to unconventional monetary policies that primarily aim to mitigate the short-run fear of the worst economic outcomes, temporarily pushing away concerns over fiscal risk.

The relationship between population health and measures of economic well-being and economic activity is a long standing topic in health economics (Preston, 1975; Cutler, Deaton, and Lleras-Muney, 2006; Ruhm, 2000). The conceptual issues in analyzing the complicated link between health and economic well-being are central to understanding the implications of the COVID-19 epidemic in the United States The public health shock of the epidemic has direct economic impacts, but the mitigation policies governments are using to control the spread of the virus may also damage economic activity. We estimate how state job market conditions respond to state COVID-19 infections and school closures, which are the earliest of the major mitigation policies. Mitigation policies and local epidemiological conditions explain some of the variation in unemployment patterns. However, the historically unprecedented increase in new UI claims during the weeks of March 15-21 and March 22-28 was largely across-the-board and occurred in all states. This suggests most of the economic disruption was driven by the health shock itself. Put differently, it appears that the labor market slowdown was due primarily to a nationwide response to evolving epidemiological conditions and that individual state policies and own epidemiologic situations have had a comparatively modest effect.

Shelter in place orders (SIPOs) require residents to remain home for all but essential activities such as purchasing food or medicine, caring for others, exercise, or traveling for employment deemed essential. Between March 19 and April 20, 2020, 40 states and the District of Columbia adopted SIPOs. This study explores the impact of SIPOs on health, with particular attention to heterogeneity in their impacts. First, using daily state-level social distancing data from SafeGraph and a difference-in-differences approach, we document that adoption of a SIPO was associated with a 5 to 10 percent increase in the rate at which state residents remained in their homes full-time. Then, using daily state-level coronavirus case data collected by the Centers for Disease Control and Prevention, we find that approximately three weeks following the adoption of a SIPO, cumulative COVID-19 cases fell by 44 percent. Event-study analyses confirm common COVID-19 case trends in the week prior to SIPO adoption and show that SIPO-induced case reductions grew larger over time. However, this average effect masks important heterogeneity across states — early adopters and high population density states appear to reap larger benefits from their SIPOs. Finally, we find that statewide SIPOs were associated with a reduction in coronavirus-related deaths, but estimated mortality effects were imprecisely estimated.

We analyze the supply-side disruptions associated with Covid-19 across firms and workers. To do so, we exploit differences in the ability of workers across industries to work remotely using data from the American Time Use Survey (ATUS). We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced significantly greater declines in employment, significantly more reductions in expected revenue growth, worse stock market performance, and higher expected likelihood of default. In terms of individual employment outcomes, lower-paid workers, especially female workers with young children, were significantly more affected by these disruptions. Last, we combine these ex-ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that most closely replicate the supply-side disruptions resulting from the pandemic.

Motivated by reports in the media suggesting unequal access to Covid-19 testing across incomes, we analyze zip-code level data on the number of Covid-19 tests, test results, and income per capita in New York City. We find that the number of tests administered is evenly distributed across income levels. In particular, the test distribution across income levels is significantly more egalitarian than the distribution of income itself: The ten percent of the city's population living in the richest zip codes received 11 percent of the Covid-19 tests and 29 percent of the city's income. The ten percent of the city's population living in the poorest zip codes received 10 percent of the tests but only 4 percent of the city's income. At the same time, we find significant disparity in the fraction of tests that come back negative for the Covid-19 disease across income levels: moving from the poorest zip codes to the richest zip codes is associated with an increase in the fraction of negative Covid-19 test results from 38 to 65 percent.

The Covid-19 Pandemic has led to changes in consumer expenditure patterns that can introduce significant bias in the measurement of inflation. I use data collected from credit and debit transactions in the US to update the official basket weights and estimate the impact on the Consumer Price Index (CPI). I find that the Covid inflation rate is higher than the official CPI in the US, for both headline and core indices. I also find similar results with Covid baskets in 10 out of 16 additional countries. The difference is significant and growing over time, as social-distancing rules and behaviors are making consumers spend relatively more on food and other categories with rising inflation, and relatively less on transportation and other categories experiencing significant deflation.

What will be political legacy of the Coronavirus pandemic? We find that epidemic exposure in an individual’s “impressionable years” (ages 18 to 25) has a persistent negative effect on confidence in political institutions and leaders. We find similar negative effects on confidence in public health systems, suggesting that the loss of confidence in political leadership and institutions is associated with healthcare-related policies at the time of the epidemic. In line with this argument, our results are mostly driven by individuals who experienced epidemics under weak governments with less capacity to act against the epidemic, disappointing their citizens. We provide evidence of this mechanism by showing that weak governments took longer to introduce policy interventions in response to the COVID-19 outbreak. These results imply that the Coronavirus may leave behind a long-lasting political scar on the current young generation (“Generation Z”).

We provide a model of endogenous asset price spirals and severe aggregate demand contractions following a large real (non-financial) shock. The key mechanism stems from the drop in the wealth share of the economy's risk-tolerant agents: as a recessionary shock hits the economy, their wealth declines and their leverage rises endogenously, causing them to o­ oad some risky assets. When monetary policy is unconstrained, it can offset the decline in risk tolerance with an interest rate cut that boosts the market's Sharpe ratio. However, if the interest rate policy is constrained, new contractionary feedbacks arise: recessionary shocks not only lead to reduced risk tolerance but also to further asset price and output drops, which feed the risk-off episode and trigger a downward loop. When pre-shock leverage ratios are high, multiple equilibria are possible, including one where risk-tolerant agents go bankrupt. A large-scale asset purchases (LSAPs) policy can be highly effective in this environment, as it reverses the downward asset price spiral. In an extension, we show how corporate debt overhang problems exacerbate our mechanism. The Covid-19 shock and the large response by all the major central banks provide a vivid illustration of the environment we seek to capture.

This note develops a framework for thinking about the following question: What is the maximum amount of consumption that a utilitarian welfare function would be willing to trade off to avoid the deaths associated with the pandemic? The answer depends crucially on the mortality rate associated with the coronavirus. If the mortality rate averages 0.81%, taken from the Imperial College London study, our answer is 41% of one year's consumption. If the mortality rate instead averages 0.44% across age groups, our answer is 28%.

We study disruptions in debt markets during the COVID-19 crisis. The safer end of the credit spectrum experienced significant losses that are hard to fully reconcile with standard default or risk premium channels. Corporate bonds traded at a large discount to their corresponding CDS, and this basis widened most for safer bonds. Liquid bond ETFs traded at a large discount to their NAV, more so for Treasuries, municipal bonds, and investment-grade corporate than high-yield corporate. These facts suggest investors tried to sell safer, more liquid securities to raise cash. These disruptions disappeared nearly as fast as they appeared. We trace this recovery back to the unprecedented actions the Fed took to purchase corporate bonds rather than its interventions in extending credit. The March 23rd announcement to buy investment-grade debt boosted prices and lowered bond spreads (particularly at shorter maturities and the safer end of investment-grade) while having virtually no effect on high-yield debt. April 9th, in contrast, had a large effect on both investment-grade and high-yield, even for the riskier end of high yield which would only indirectly benefit from the policy. These facts highlight the importance of financial frictions early on in the crisis, but also challenge existing theories of these frictions.

We correlate county-level COVID-19 death rates with key variables using both linear regression and negative binomial mixed models, although we focus on linear regression models. We include four sets of variables: socio-economic variables, county-level health variables, modes of commuting, and climate and pollution patterns. Our analysis studies daily death rates from April 4, 2020 to May 27, 2020. We estimate correlation patterns both across states, as well as within states. For both models, we find higher shares of African American residents in the county are correlated with higher death rates. However, when we restrict ourselves to correlation patterns within a given state, the statistical significance of the correlation of death rates with the share of African Americans, while remaining positive, wanes. We find similar results for the share of elderly in the county. We find that higher amounts of commuting via public transportation, relative to telecommuting, is correlated with higher death rates. The correlation between driving into work, relative to telecommuting, and death rates is also positive across both models, but statistically significant only when we look across states and counties. We also find that a higher share of people not working, and thus not commuting either because they are elderly, children or unemployed, is correlated with higher death rates. Counties with higher home values, higher summer temperatures, and lower winter temperatures have higher death rates. Contrary to past work, we do not find a correlation between pollution and death rates. Also importantly, we do not find that death rates are correlated with obesity rates, ICU beds per capita, or poverty rates. Finally, our model that looks within states yields estimates of how a given state's death rate compares to other states after controlling for the variables included in our model; this may be interpreted as a measure of how states are doing relative to others. We find that death rates in the Northeast are substantially higher compared to other states, even when we control for the four sets of variables above. Death rates are also statistically significantly higher in Michigan, Louisiana, Iowa, Indiana, and Colorado. California's death rate is the lowest across all states.

Emerging economies are characterized by an extremely high prevalence of informality, small-firm employment and jobs not fit for working from home. These features factor into how the COVID-19 crisis has affected the economy. We develop a framework that, based on accounting identities and actual data, quantifies potential job and income losses during the crisis and recovery for economies with different economic organization structures. Our analysis incorporates differential exposure of jobs across categories of firm-size and formality status, as well as sectors and occupations. We account for the direct supply shock caused by lockdowns, the idiosyncratic demand shock suffered by sectors that rely on high contact with their costumers, the transmission of both shocks through IO linkages, and the overall aggregate demand effect derived from these shocks. Applying our framework to data for Colombia, which exhibits an employment distribution similar to that of other emerging market countries, in particular Latin America, we find that well over 50% of jobs are at risk in the initial stages of the crisis. Because informal jobs and those not fit for telework are at higher risk, this number goes down to 33% if the US employment distribution is imposed on the Colombian data. As the crisis deepens, the risk of unemployment grows. However, informality rebounds quickly in the recovery, an employment at risk is quickly reduced to 20% of the baseline, all concentrated in formal jobs. Our findings point to the importance of action to maintain formal matches from dissolving, given their scarcity and rebuilding difficulty, while protecting the poor and the informal via income transfers

We study the effects of negative supply shocks and shocks to the composition of final demand on aggregate output in a disaggregated neoclassical model with multiple sectors, factors, and input-output linkages. We show how nonlinearities associated with complementarities in consumption and production amplify the effect of negative supply shocks by creating supply bottlenecks and disrupting supply chain networks. These nonlinearities are particularly potent when the shocks are more heterogeneous as the worst-affected sectors drag down the other sectors. Nonlinearities are strengthened when changes in preferences lead households to tilt the composition of their demand towards the crippled sectors directly and indirectly through their supply chains. And nonlinearities are further intensified when factors cannot easily be reallocated across sectors to reinforce weak links. A quantitative investigation suggests that nonlinearities may amplify the impact of the Covid-19 shock by between 10\%-100\%, depending on the horizon of analysis and the exact size of the shocks.

This paper examines the impact of the social distancing policies states adopted between March and April of 2020 in response to the COVID-19 epidemic. These actions, together with voluntary social distancing, appear to have reduced the rate of new COVID-19 cases and deaths, but raised concerns about the costs experienced by workers and businesses. Estimates from difference-in-difference models that leverage cross-state variation in the timing of business closures and stay-at-home mandates suggest that the employment rate fell by about 1.7 percentage points for every extra 10 days that a state experienced a stay-at-home mandate during the period March 12-April 12, 2020; select business closure laws were associated with similar employment effects.

"95% of all decision making is unconscious." It's such a common refrain, but is it true? What does it even mean for a decision to be unconscious? What about an unconscious influence? Professor David Shanks sheds light on these topics and discusses money priming, whether sex really sells, and why grocery stores put the bakery by the entrance.

Marcus Rashford has called on the government to reverse a decision not to provide free school meal vouchers during the summer, saying that "the system isn't built for families like mine to succeed".

First up this week, Staff Writer Meredith Wadman talks with host Sarah Crespi about how male sex hormones may play a role in higher levels of severe coronavirus infections in men. New support for this idea comes from a study showing high levels of male pattern baldness in hospitalized COVID-19 patients. Read all our coronavirus coverage. Next, Jason Qian, a Ph.D. student in the systems biology department at Harvard Medical School, joins Sarah to talk about an object-tracking system that uses bacterial spores engineered with unique DNA barcodes. The inactivated spores can be sprayed on anything from lettuce, to wood, to sand and later be scraped off and read out using a CRISPR-based detection system. Spraying these DNA-based identifiers on such things as vegetables could help trace foodborne illnesses back to their source. Read a related commentary piece. 

The impact of COVID-19 on the American Media How do we as citizens and news consumers navigate the complexities of our current media landscape? And what has the coronavirus meant for journalists, media companies and where we get our news and information from? Our expert panel will make sense of the present, and look ahead to the future.

The United Nations Sustainable Development Goals (SDGs) are a global consensus on the world’s most pressing challenges. They come with a set of 232 indicators against which countries should regularly monitor their progress, ensuring that everyone is represented in up-to-date data that can be used to make decisions to improve people’s lives. However, existing data sources to measure progress on the SDGs are often outdated or lacking appropriate disaggregation. We evaluate the value that anonymous, publicly accessible advertising data from Facebook can provide in mapping socio-economic development in two low and middle income countries, the Philippines and India. Concretely, we show that audience estimates of how many Facebook users in a given location use particular device types, such as Android vs. iOS devices, or particular connection types, such as 2G vs. 4G, provide strong signals for modeling regional variation in the Wealth Index (WI), derived from the Demographic and Health Survey (DHS). We further show that, surprisingly, the predictive power of these digital connectivity features is roughly equal at both the high and low ends of the WI spectrum. Finally we show how such data can be used to create gender-disaggregated predictions, but that these predictions only appear plausible in contexts with gender equal Facebook usage, such as the Philippines, but not in contexts with large gender Facebook gaps, such as India.

After an epic EU summit, the €750bn facility to aid members worst hit by the pandemic is almost in hand, lacking only formal ratification. Its fraught negotiations centred on issues such as the split between loans and grants, approval procedures, and the rule of law. Lost in the fray, though, was a basic question: what kind of spending specifically promotes long-term recovery best?

Research shows entrenched partisanship between Leavers and Remainers in response to the British government’s efforts to control the pandemic

There were two narratives that emerged in the week before we locked down on 23rd March that could go some way to explaining why the UK was relatively slow to lockdown. One was the idea of “herd immunity” - that the virus was always going to spread throughout the population to some extent, and that should be allowed to happen to build up immunity. That theory may have been based on a misunderstanding of how this particular virus behaved. The second narrative was based on the idea of “behavioural fatigue”. This centred around the notion that the public will only tolerate a lockdown for so long so it was crucial to wait for the right moment to initiate it. Go too soon, and you might find that people would not comply later on. It turns out that this theory was also wrong. And based on a fundamental misunderstanding of human behaviour. Despite photos of packed parks, crammed beaches and VE day conga lines, on the whole the British public complied beyond most people’s expectations. So what informed the government’s decision making?In this programme we ask, what is “behavioural fatigue”, where did it come from, how much influence did it have on the UK’s late lockdown, and where does Nudge theory fit into the narrative?

Most of the data available through tidycensus and the Census API is aggregated to certain geographic levels (tract, county, state, etc.). In other words, the data we get by executing get_acs() has been summarized by the Census Bureau so that we are able to learn how many people live in a particular county or what the median household income of a state is. There are thousands of individual variables that the Census aggregates and publishes in tabular form. For many purposes, these pre-aggregated tables have enough information to work with. But, the Census Bureau also releases microdata from the American Community Survey. Microdata is the individual-level responses to the ACS that is used to create the summary and detail tables the Census publishes. Instead of a getting one row per state from a table, we can get one row per respondent. For the American Community Survey, this data is called the Public Use Microdata Sample (PUMS). Using PUMS data instead of the published tables can be a very powerful tool. It can, for instance, allow you to create custom estimates that aren’t available in pre-aggregated tables. You can also use microdata to fit models on individual-level data instead of modeling block groups or census tracts. Until recently, PUMS data was only available from the Census Bureau FTP site and was somewhat cumbersome to use. Now, PUMS data is available via web API, which means you can easily access it in R by using tidycensus.

We present a scalable methodology to identify Twitter handles of politicians in a given region and test our framework in the context of Indian and US politics. The main contribution of our work is the list of the curated Twitter handles of 18500 Indian and 8000 US politicians. Our work leveraged machine learning-based classification and human verification to build a data set of Indian politicians on Twitter. We built NivaDuck, a highly precise, two-staged classification pipeline that leverages Twitter description text and tweet content to identify politicians. For India, we tested NivaDuck’s recall using Twitter handles of the members of the Indian parliament while for the US we used state and local level politicians in California state and San Diego county respectively. We found that while NivaDuck has lower recall scores, it produces large, diverse sets of politicians with precision exceeding 90 percent for the US dataset. We discuss the need for an ML-based, scalable method to compile such a dataset and its myriad use cases for the research community and its wide-ranging utilities for research in political communication on social media.

This chart shows the countries with the worst coronavirus outbreaks in the world right now. With its cases surging since mid-June, the United States is squarely in the top 10.

Scientists who study complex systems offer solutions to the pandemic.

Scottish numbers: 30 July 2020 17 new confirmed cases of COVID-19; this is 0.5% of newly tested individuals. 0 new reported death(s) of people who have tested positive. 2 people were in intensive care last night with confirmed COVID-19. 260 people were in hospital with confirmed COVID-19. 13,208 new tests for COVID-19 that reported results.

This paper in­ves­ti­gates the incidence, trend and de­ter­mi­nants of remote work in Greece. A crisis-​stricken country in the years preceding the Covid-19 crisis, Greece entered the first wave of the public health shock as a laggard in dig­i­tal­i­sa­tion and remote work arrange­ments among European countries. While Covid-19 induced a spike in the use of remote work arrange­ments in many countries, this paper presents evidence that working from home (WfH) in Greece was subdued in the past decade. By analysing the profile of the job tasks and skill needs of Greek home­work­ers, the paper also shows marked de­vi­a­tions in home­work­ing patterns and de­ter­mi­nants in Greece, relative to other EU countries. This includes a higher preva­lence of WfH among Greek females and non-​nationals, limited use by young workers and families with children and a stronger relation with atypical work hours. While remote workers in Greece receive a 7% monthly wage premium, their jobs are found to involve stan­dard­ised and moderate ICT tasks and to rely more on social serving tasks. The paper high­lights that there is sig­nif­i­cant scope to enhance remote work in Greece, which can amount to up to 37% of all salaried jobs, subject to changing work or­gan­i­sa­tion, norms and policies. In the coro­n­avirus era, over­com­ing barriers to remote work will be key for the Greek labour market to adapt to social distances practices and dig­i­tal­i­sa­tion.

Evidence from past economic crises indicates that re­ces­sions often affect men’s and women’s em­ploy­ment dif­fer­ently, with a greater impact on male-​dominated sectors. The current COVID-19 crisis presents novel char­ac­ter­is­tics that have affected economic, health and social phenomena over wide swaths of the economy. Social dis­tanc­ing measures to combat the spread of the virus, such as working from home and school closures, have placed an ad­di­tional tremen­dous burden on families. Using new survey data collected in April 2020 from a rep­re­sen­ta­tive sample of Italian women, we analyse jointly the effect of COVID-19 on the working arrange­ments, housework and childcare of couples where both partners work. Our results show that most of the ad­di­tional workload as­so­ci­ated to COVID-19 falls on women while childcare ac­tiv­i­ties are more equally shared within the couple than housework ac­tiv­i­ties. According to our empirical estimates, changes to the amount of housework done by women during the emergency do not seem to depend on their partners’ working arrange­ments. With the exception of those con­tin­u­ing to work at their usual place of work, all of the women surveyed spend more time on housework than before. In contrast, the amount of time men devote to housework does depend on their partners’ working arrange­ments: men whose partners continue to work at their usual workplace spend more time on housework than before. The link between time devoted to childcare and working arrange­ments is more symmetric, with both women and men spending less time with their children if they continue to work away from home. For home schooling, too, parents who continue to go to their usual workplace after the lockdown are less likely to spend greater amounts of time with their children than before. Similar results emerge for our sample of women not working before the emergency. Finally, analysis of work-life balance sat­is­fac­tion shows that working women with children aged 0-5 are those who say they find balancing work and family more difficult during COVID-19. The work-life balance is es­pe­cially difficult to achieve for those with partners who continue to work outside the home during the emergency.

The goal of this piece is to survey the emerging and rapidly growing lit­er­a­ture on the economic con­se­quences of COVID-19 and gov­ern­ment response, and to syn­thetize the insights emerging from a very large number of studies. This survey (i) provides an overview of the data sets used to measure social dis­tanc­ing and COVID-19 cases and deaths; (ii) reviews the lit­er­a­ture on the de­ter­mi­nants of com­pli­ance and ef­fec­tive­ness of social dis­tanc­ing; (iii) sum­ma­rizes the lit­er­a­ture on the socio-​economic con­se­quences of COVID-19 and gov­ern­ment in­ter­ven­tions, focusing on labor, health, gender, dis­crim­i­na­tion and en­vi­ron­men­tal aspects; and (iv) discusses policy proposals.

We study the impact of short-​term exposure to ambient air pollution on the spread and severity of COVID-19 in Germany. We combine data on county-​by-day level on confirmed cases and deaths with in­for­ma­tion on local air quality and weather con­di­tions and exploit short-​term variation in the con­cen­tra­tion of par­tic­u­late matter (PM10) and ozone (O3). We apply fixed effects re­gres­sions con­trol­ling for global time-​varying con­found­ing factors and regional time-​invariant con­found­ing factors on the county level, as well as po­ten­tially con­found­ing weather con­di­tions and the regional stage of the pandemic. We find sig­nif­i­cant positive effects of PM10 con­cen­tra­tion after the onset of the illness on COVID-19 deaths specif­i­cally for elderly patients (80+ years): higher levels of air pollution by one standard deviation 3 to 12 days after de­vel­op­ing symptoms increase deaths by 30 percent (males) and 35 percent (females) of the mean. In addition, air pollution raises the number of confirmed cases of COVID-19. The timing of results supports mech­a­nisms of air pollution affecting the severity of already realized in­fec­tions. Air pollution appears not to affect the prob­a­bil­ity of infection itself.

Early non-​pharmaceutical in­ter­ven­tions (NPI) sig­nif­i­cantly reduced the death toll of the COVID-19 pandemic. Yet, there are vast dif­fer­ences in how quickly gov­ern­ments im­ple­mented NPIs. In this paper, we analyze the role of public attention, measured as the share of daily Google searches in a country related to COVID-19, in the timing of the NPI responses. We first show that public attention depends strongly on whether there are cases in own country. We then show that countries with high levels of public attention are more likely to implement NPIs, even after con­trol­ling for the number of cases and deaths. Finally, we show that the extent to which a gov­ern­ment responds to public attention is highly dependent on the country’s in­sti­tu­tional quality. The positive effect of public attention on policy im­ple­men­ta­tion is driven entirely by countries with good in­sti­tu­tions.

The virality of the video underscores the difficulty in moderating coronavirus misinformation as treatments and public health responses have become more political.

Scholars traditionally receive career credit for a paper based on where in the author list they appear, but position in an author list often carries little information about what the contribution of each researcher was. “Contributorship” refers to a movement to formally document the nature of each researcher’s contribution to a project. We discuss the emerging CRediT standard for documenting contributions and describe a web-based app and R package, tenzing, that is designed to facilitate its use. tenzing can make it easier for researchers on a project to plan and record their planned contributions and to document those contributions in a journal article.

A framework for tax compliance is suggested in which both the power of tax authorities and trust in the tax authorities are relevant dimensions for understanding enforced and voluntary compliance. Dynamic interactions between power and trust are considered. Using the framework as a conceptual tool, factors studied in previous research, such as fines, audit probabilities, tax rate, knowledge, attitudes, norms and fairness are reviewed and discussed with reference to the power and trust dimensions. Using the framework as an operational tool, approaches of responsive regulation to increase tax compliance are discussed.

Policymakers are increasingly turning to insights gained from the experimental method as a means to inform large-scale public policies. Critics view this increased usage as premature, pointing to the fact that many experimentally tested programs fail to deliver their promise at scale. Under this view, the experimental approach drives too much public policy. Yet, if policymakers could be more confident that the original research findings would be delivered at scale, even the staunchest critics would carve out a larger role for experiments to inform policy. Leveraging the economic framework of Al-Ubaydli et al. (2019), we put forward 12 simple proposals, spanning researchers, policymakers, funders and stakeholders, which together tackle the most vexing scalability threats. The framework highlights that only after we deepen our understanding of the scale-up problem will we be on solid ground to argue that scientific experiments should hold a more prominent place in the policymaker's quiver.

COVID-19 has created a dual set of stresses on health care systems worldwide: a rise in expensive intensive care services and a dramatic decline in elective services. The U.S. government has responded with both grant and loan programs to help health care providers weather the storm. But the optimal size and nature of such programs are hard to evaluate without an understanding of the ability of providers to make up their lost elective service revenues over time. In this paper, we study the closest relevant parallel to the reduction in elective services seen under COVID-19: hurricanes. We match information on hurricanes to data on Medicare hospital elective visits and charges from 1997-2013, comparing counties impacted by hurricanes to nearby unaffected counties. We find that the average hurricane reduces elective services by about 7% in the month it makes landfall. For the most severe hurricanes, we estimate a reduction of more than 20%. Services return to baseline fairly rapidly, but for severe hurricanes it takes a year or more to make up lost revenues. Projections based on variation in hurricane severity suggest that it will take over 3 years for providers to make up the lost revenue from COVID-19.

This paper considers a modification of the standard Susceptible-Infected-Recovered (SIR) model of epidemic that allows for different degrees of compulsory as well as voluntary social distancing. It is shown that the fraction of population that self-isolates varies with the perceived probability of contracting the disease. Implications of social distancing both on the epidemic and recession curves are investigated and their trade off is simulated under a number of different social distancing and economic participation scenarios. We show that mandating social distancing is very effective at flattening the epidemic curve, but is costly in terms of employment loss. However, if targeted towards individuals most likely to spread the infection, the employment loss can be somewhat reduced. We also show that voluntary self-isolation driven by individual’s perceived risk of becoming infected kicks in only towards the peak of the epidemic and has little or no impact on flattening the epidemic curve. Using available statistics and correcting for measurement errors, we estimate the rate of exposure to COVID-19 for 21 Chinese provinces and a selected number of countries. The exposure rates are generally small, but vary considerably between Hubei and other Chinese provinces as well as across countries. Strikingly, the exposure rate in Hubei province is around 40 times larger than the rates for other Chinese provinces, with the exposure rates for some European countries being 3-5 times larger than Hubei (the epicenter of the epidemic). The paper also provides country-specific estimates of the recovery rate, showing it to be about 21 days (a week longer than the 14 days typically assumed), and relatively homogeneous across Chinese provinces and for a selected number of countries.

We study liquidity conditions in the corporate bond market since the onset of the COVID-19 pandemic. We find that in mid-March 2020, as selling pressure surged, dealers were wary of accumulating inventory on their balance sheets, perhaps out of concern for violating regulatory requirements. As a result, the cost to investors of trading immediately with a dealer surged. A portion of transactions migrated to a slower, less costly process wherein dealers arranged for trades directly between customers without using their own balance sheet space. Interventions by the Federal Reserve appear to have relaxed balance sheet constraints: soon after they were announced, dealers began absorbing inventory, bid-ask spreads declined, and market liquidity started to improve. Interestingly, liquidity conditions improved for bonds that were eligible for the Fed’s lending/purchase programs and for bonds that were ineligible. Hence, by allowing dealers to unload certain assets from their balance sheet, the Fed’s interventions may have helped dealers to better intermediate a wide variety of assets, including those not directly targeted.

We develop an analytically tractable method to estimate the fraction of unreported infections in epidemics with a known epicenter and estimate the number of unreported COVID-19 infections in the US during the first half of March 2020. Our method utilizes the covariation in initial reported infections across US regions and the number of travelers to these regions from the epicenter, along with the results of an early randomized testing study in Iceland. Using our estimates of the number of unreported infections, which are substantially larger than the number of reported infections, we also provide estimates for the infection fatality rate using data on reported COVID-19 fatalities from U.S. counties.

This paper presents a procedure for estimating and forecasting disease scenarios for COVID-19 using a structural SIR model of the pandemic. Our procedure combines the flexibility of noteworthy reduced-form approaches for estimating the progression of the COVID-19 pandemic to date with the benefits of a simple SIR structural model for interpreting these estimates and constructing forecast and counterfactual scenarios. We present forecast scenarios for a devastating second wave of the pandemic as well as for a long and slow continuation of current levels of infections and daily deaths. In our counterfactual scenarios, we find that there is no clear answer to the question of whether earlier mitigation measures would have reduced the long run cumulative death toll from this disease. In some cases, we find that it would have, but in other cases, we find the opposite — earlier mitigation would have led to a higher long-run death toll.

What are epidemiologists, virologists, and doctors putting on their (and their families’) faces in day-to-day life?

Disease spread is in part a function of individual behavior. We examine the factors predicting individual behavior during the Covid-19 pandemic in the United States using novel data collected by Belot et al. (2020). Among other factors, we show that people with lower income, less flexible work arrangements (e.g., an inability to tele-work) and lack of outside space at home are less likely to engage in behaviors, such as social distancing, that limit the spread of disease. We also find evidence that region, gender and beliefs predict behavior. Broadly, our findings align with typical relationships between health and socio-economic status. Moreover, they suggest that the burden of measures designed to stem the pandemic are unevenly distributed across socio-demographic groups in ways that affect behavior and thus potentially the spread of illness. Policies that assume otherwise are unlikely to be effective or sustainable.

We develop a multi-​sectoral matching model to predict the impact of the lockdown on the US un­em­ploy­ment, con­sid­er­ing the het­ero­gene­ity of workers to account for the con­trasted impacts across various types of jobs. We show that sep­a­ra­tions and business closures that hit the workers with the first level of education explains the abrupt­ness of the un­em­ploy­ment rise. The existence of sig­nif­i­cant con­ges­tion ex­ter­nal­i­ties in the hiring process suggests that a comeback to the pre-​crisis un­em­ploy­ment level could be reached in 2024 in a scenario with a double wave. In the same scenario, a cal­i­bra­tion on French data leads to more pes­simistic forecasts with a comeback to the pre-​crisis un­em­ploy­ment level expected until 2027.

This paper estimates the link between pop­u­la­tion density and COVID-19 spread and severity in the con­tigu­ous United States. To overcome con­found­ing factors, we use two In­stru­men­tal Variable (IV) strate­gies that exploit ge­o­log­i­cal features and his­tor­i­cal pop­u­la­tions to induce exogenous variation in pop­u­la­tion density without affecting COVID-19 related deaths directly. We find that density has affected the timing of the outbreak in each county, with denser locations more likely to have an early outbreak. However, we find no evidence that pop­u­la­tion density is linked with COVID-19 cases and deaths. Using data from Google, Facebook and the US Census, we also in­ves­ti­gate several possible mech­a­nisms for our findings. We show that pop­u­la­tion density can affect the timing of outbreaks through higher con­nect­ed­ness of denser location. Fur­ther­more, we find that pop­u­la­tion density is pos­i­tively as­so­ci­ated with proxies of social dis­tanc­ing and neg­a­tively as­so­ci­ated with the age of the pop­u­la­tion, high­light­ing the im­por­tance of these mediating factors.

I use simple cor­re­la­tions and re­gres­sion analysis to study how the number of confirmed Covid-19 cases and the number of deaths with Covid-19 per 100,000 people is related with the so­cioe­co­nomic char­ac­ter­is­tics of local areas in England and Wales. I find that local areas that have larger house­holds, worse levels of self-​reported health and a larger fraction of people using public transport have more Covid-19 in­fec­tions per 100,000 people. For mortality, household size and use of public transport are less important, but there is a clear relation with age, ethnicity and self-​reported health. Local areas with an older pop­u­la­tion, a larger share of black or Asian pop­u­la­tion and worse levels of self-​reported health have more Covid-19 deaths per 100,000 people. To prevent the spread of infection and reduce mortality, pol­i­cy­mak­ers should introduce measures to improve housing con­di­tions and improve the health of the pop­u­la­tion. Also, as many countries now begin to relax lockdown measures, they should pay par­tic­u­lar attention to reducing the risk of infection in public transport.

Although late-stage venture capital (VC) activity did not change dramatically in the first two months after the COVID-19 pandemic reached the U.S., early-stage VC activity declined by 38%. The particular sensitivity of early-stage VC investment to market conditions—which we show to be common across recessions spanning four decades from 1976 to 2017—raises questions about the pro-cyclicality of VC and its implications for innovation, especially in light of the common narrative that VC is relatively insulated from public markets. We find that the implications for innovation are not benign: innovation conducted by VC-backed firms in recessions is less highly cited, less original, less general, and less closely related to fundamental science. These effects are more pronounced for startups financed by early-stage venture funds. Given the important role that VC plays in financing breakthrough innovations in the economy, our findings have implications for the broader discussion on the nature of innovation across business cycles

This paper in­ves­ti­gates the decline in traffic accidents, fa­tal­i­ties and injuries during the months that COVID-19 stay-​at-home orders im­ple­mented in Turkey. Taking into account the decline in accidents in March and April together, these rates roughly translate to 200 traffic related deaths and 17,600 injuries avoided during the months that stay-​at-home orders were in place. The Dif­fer­ence in Dif­fer­ence estimates that exploit variation in quar­an­tine orders among small cities, I also show that stricter rules in April are re­spon­si­ble for the decline of accidents with death or injury by 35 percent, death by 72 percent and injuries by 19 percent.

Entrepreneurship, as reflected in the start-up of new firms, the growth and market exit of existing firms, and the ow of venture capital, has been severely curtailed by the lockdown and social distancing measures taken by governments around the world in the fight against COVID-19. This paper, after documenting preliminary evidence on these declines, argues that there is a strong possibility that the unintended damage to entrepreneurship, innovation and growth could be persistent. This requires that short- term economic and business rescue packages be complimented by measures aimed at the longer-term, and that these be based on at least five principles. These 5 principles (5Ds) refer to decentralization, democratization, demand, distribution and demography.

This paper employs a skills-​based approach to identify in­di­vid­ual and job factors most likely to be impacted by social dis­tanc­ing measures and practices due to the Covid-19 pandemic. Using data from the Cedefop European skills and jobs survey, a Covid-19 social dis­tanc­ing risk index (COV19R) is created based on skills de­scrip­tors that cat­e­gorise jobs by their level of physical proximity to others and their digital intensity. It is con­ser­v­a­tively estimated that about 45 million jobs in the EU-27 labour market (23% of total EU-27 em­ploy­ment) are faced with a very high risk of Covid-19 dis­rup­tion and another 22% of the EU workforce – mostly medium- to lower-​skilled service provision – is exposed to some sig­nif­i­cant risk. The burden of the Covid-19 social dis­tanc­ing risk falls dis­pro­por­tion­ately on vul­ner­a­ble workforce groups, such as women, older employees, non-​natives, the lower-​educated, those working longer hours and employed in micro-​sized work­places. The findings call for immediate and targeted policy responses to prevent ongoing job losses and widening of labour market and social in­equal­i­ties due to the pandemic.

In this paper, we examine the short-​term con­se­quences of COVID-19 and evaluate the impacts of stay-​at-home orders on em­ploy­ment and wages in the United States. Guided by a pre-​analysis plan, we document that COVID-19 increased the un­em­ploy­ment rate, decreased hours of work and labor force par­tic­i­pa­tion, es­pe­cially for younger workers, non-white, not married and less-​educated workers. We built four indexes (exposure to disease, proximity to coworkers, work remotely and critical workers) to study the impact of COVID-19. We find that workers that can work remotely are sig­nif­i­cantly less likely to have their labor market outcomes affected, while workers working in proximity to coworkers are more affected. The un­em­ploy­ment effects are sig­nif­i­cantly larger for states that im­ple­mented stay-​at-home orders. Our estimates suggest that, as of early May, these policies increased un­em­ploy­ment by nearly 4 per­cent­age points, but reduced COVID-19 cases by 186,600– 311,000, and deaths by 17,851–23,325. We apply our estimates to compute lost income ($18.6–$21.4 billion), reduced gov­ern­ment income tax revenues ($3.4–$5.5 billion), increased un­em­ploy­ment insurance benefit payments ($5–$5.8 billion) and reduced hospital costs ($0.7–$1.2 billion). Despite the jobs lost, age adjusted value of sta­tis­ti­cal life suggests that stay-​at-home orders are cost effective.

We develop a multiple-​events model and exploit within and between country variation in the timing, type and level of intensity of various non-​pharmaceutical in­ter­ven­tions to study their dynamic effects on the daily incidence of COVID-19 and on pop­u­la­tion mobility patterns across 135 countries. Taking into account the con­tem­po­ra­ne­ous presence of multiple in­ter­ven­tions, we remove con­cur­rent policy bias from the effect of each policy of interest, and we establish that policies curb the epidemic by changing pop­u­la­tion mobility patterns in a manner con­sis­tent with time-use and epi­demi­o­log­i­cally relevant con­sid­er­a­tions. We are thus able to shed light on the mech­a­nisms through which con­fine­ment measures con­tribute to “flat­ten­ing the curve”.

A growing number of contact tracing apps are being developed to complement manual contact tracing. A key question is whether users will be willing to adopt these contact tracing apps. In this work, we survey over 4,500 Americans to evaluate (1) the effect of both accuracy and privacy concerns on reported willingness to install COVID19 contact tracing apps and (2) how different groups of users weight accuracy vs. privacy. Drawing on our findings from these first two research questions, we (3) quantitatively model how the amount of public health benefit (reduction in infection rate), amount of individual benefit (true-positive detection of exposures to COVID), and degree of privacy risk in a hypothetical contact tracing app may influence American's willingness to install. Our work takes a descriptive ethics approach toward offering implications for the development of policy and app designs related to COVID19.

AMID rising global numbers of daily coronavirus infections, a fresh flush of vaccine trial results is offering hope for the longer run

Restricting movement on the basis of biology threatens freedom, fairness and public health.

The contact structure of a population plays an important role in transmission of infection. Many ``structured models'' capture aspects of the contact structure through an underlying network or a mixing matrix. An important observation in such models, is that once a fraction 1−1/01-1/\mathcal{R}_0 has been infected, the residual susceptible population can no longer sustain an epidemic. A recent observation of some structured models is that this threshold can be crossed with a smaller fraction of infected individuals, because the disease acts like a targeted vaccine, preferentially immunizing higher-risk individuals who play a greater role in transmission. Therefore, a limited ``first wave'' may leave behind a residual population that cannot support a second wave once interventions are lifted. In this paper, we systematically analyse a number of mean-field models for networks and other structured populations to address issues relevant to the Covid-19 pandemic. In particular, we consider herd-immunity under several scenarios. We confirm that, in networks with high degree heterogeneity, the first wave confers herd-immunity with significantly fewer infections than equivalent models with lower degree heterogeneity. However, if modelling the intervention as a change in the contact network, then this effect might become more subtle. Indeed, modifying the structure can shield highly connected nodes from becoming infected during the first wave and make the second wave more substantial. We confirm this finding by using an age-structured compartmental model parameterised with real data and comparing lockdown periods implemented either as a global scaling of the mixing matrix or age-specific structural changes. We find that results regarding herd immunity levels are strongly dependent on the model, the duration of lockdown and how lockdown is implemented.

The efficacy of digital contact tracing against COVID-19 epidemic is debated: smartphone penetration is limited in many countries, non-uniform across age groups, with low coverage among elderly, the most vulnerable to SARS-CoV-2. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact-survey and epidemiological information to describe the risk factors for exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity and transmissibility. Assuming a reproductive ratio R=2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence of ~36%. With R=1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.

The covid-19 pandemic led many countries to close schools and declare lockdowns during the Spring of 2020, with important impacts on the labor market. We document the effects of the covid-19 lockdown in Spain, which was hit early and hard by the pandemic and suffered one of the strictest lockdowns in Europe. We collected rich household survey data in early May of 2020. We document large em­ploy­ment losses during the lockdown, es­pe­cially in “quar­an­tined” sectors and non-​essential sectors that do not allow for remote work. Em­ploy­ment losses were mostly temporary, and hit lower-​educated workers par­tic­u­larly hard. Women were slightly more likely to lose their job than men, and those who remained employed were more likely to work from home. The lockdown led to a large increase in childcare and housework, given the closing of schools and the inability to outsource. We find that men increased their par­tic­i­pa­tion in housework and childcare slightly, but most of the burden fell on women, who were already doing most of the housework before the lockdown. Overall, we find that the covid-19 crisis appears to have increased gender in­equal­i­ties in both paid and unpaid work in the short-​term.

Scientific knowledge and advances are a cornerstone of modern society. They improve our understanding of the world we live in and help us navigate global challenges including emerging infectious diseases, climate change and the biodiversity crisis. For any scientist, whether they work primarily in fundamental knowledge generation or in the applied sciences, it is important to understand how science fits into a decision-making framework. Decision science is a field that aims to pinpoint evidence-based management strategies. It provides a framework for scientists to directly impact decisions or to understand how their work will fit into a decision process. Decision science is more than undertaking targeted and relevant scientific research or providing tools to assist policy makers; it is an approach to problem formulation, bringing together mathematical modelling, stakeholder values and logistical constraints to support decision making. In this paper we describe decision science, its use in different contexts, and highlight current gaps in methodology and application. The COVID-19 pandemic has thrust mathematical models into the public spotlight, but it is one of innumerable examples in which modelling informs decision making. Other examples include models of storm systems (eg. cyclones, hurricanes) and climate change. Although the decision timescale in these examples differs enormously (from hours to decades), the underlying decision science approach is common across all problems. Bridging communication gaps between different groups is one of the greatest challenges for scientists. However, by better understanding and engaging with the decision-making processes, scientists will have greater impact and make stronger contributions to important societal problems.

We evaluate the causal effect of class size (i.e., number of students in a classroom) on incidence of class closure due to flu epidemic in 2015, 2016, and 2017, applying an in­stru­men­tal variable method with the Mai­monides rule to ad­min­is­tra­tive data of public primary and middle school students in one of the largest mu­nic­i­pal­i­ties within the City of Tokyo Met­ro­pol­i­tan Area. Given the classroom area of 63m2 set by reg­u­la­tion in Japan, class size reduction improves social dis­tanc­ing among students in a classroom. We find that class size reduction is effective to reduce class closure due to flu: one unit reduction of class size decreases class closure by about 5%; and forming small classes with 27 students at most, sat­is­fy­ing the social dis­tanc­ing of 1.5 m rec­om­mended to prevent droplet infection including influenza and COVID-19, reduces class closure by about 90%. In addition, we find that the older are students, the larger are the effects of class size reduction. Our findings provide sup­port­ive evidence for the ef­fec­tive­ness of social dis­tanc­ing policy in primary and middle schools to protect students from droplet in­fec­tious disease including COVID-19.

The pandemic exposed a glaring health gap caused by systemic racism. Here’s how to narrow it

Government source told Mail the PM was 'extremely concerned' by outbreaksRises were recorded each day last week for the first time since the April peak The seven-day average stands at almost 700, 28 per cent up on three weeks ago Ministers fear a second wave of the pandemic could hit before this winter 

The COVID-19 pandemic has upended the U.S. economy and labor market. We assess the initial spike in un­em­ploy­ment due to the virus response and possible paths for the official un­em­ploy­ment rate through 2021. Sub­stan­tial un­cer­tainty surrounds the path for measured un­em­ploy­ment, depending on the path of the virus and con­tain­ment measures and their impact on reported job search activity. We assess potential un­em­ploy­ment paths based on his­tor­i­cal patterns of monthly flows in and out of un­em­ploy­ment, adjusted for unique features of the virus economy. The possible paths vary widely, but absent hiring activity on an un­prece­dented scale, un­em­ploy­ment could remain in double-​digits into 2021. We also find that the increase in measured un­em­ploy­ment could be mean­ing­fully tempered by a sub­stan­tial reduction in labor force par­tic­i­pa­tion.

In March 2020, shelter-​in-place and social-​distancing policies have been enforced or rec­om­mended all over the world to fight the COVID-19 pandemic. However, strict con­tain­ment is hardly achiev­able in low-​income countries, as large parts of pop­u­la­tion are forced to continue income-​generating ac­tiv­i­ties to escape extreme poverty or hunger. To assess the trade-off between poverty and a higher risk of catching COVID-19, we use regional mobility to work and poverty rates across 241 regions of 9 countries from Latin America and Africa. With a difference-​in-difference approach around the time of lockdown an­nounce­ments, we measure the dif­fer­en­tial time variation in work mobility between high and low-​poverty regions. We find that the degree of work mobility reduction is sig­nif­i­cantly driven by the intensity of poverty. Con­sis­tently, human movements vary sig­nif­i­cantly more between poverty levels when it come to work rather than less vital ac­tiv­i­ties. We also estimate how higher poverty rates translate into a faster spread of COVID-19 cases through the channel of work mobility.

The COVID19 pandemic has caused shocks to the demand for home childcare (with the closure of schools and nurseries) and the supply of home childcare (with many people not working). We collect real-time data on daily lives to document that UK families with young children have been doing the equiv­a­lent of a working week in childcare. Women have been doing the greater share, but overall, the gender childcare gap (the dif­fer­ence between the share of childcare done by women and the share done by men) for the ad­di­tional, post-​COVID19 hours is smaller than that for the al­lo­ca­tion of pre-COVID19 childcare. However, the amount of ad­di­tional childcare provided by men is very sensitive to their em­ploy­ment – the al­lo­ca­tion has become more equal in house­holds where men are working from home and where they have been fur­loughed/ lost their job. There are likely to be long-term im­pli­ca­tions from these changes – po­ten­tially negative for the careers of parents of young children; but also, more pos­i­tively for some families, for sharing the burden of childcare more equally in the future.

The COVID-19 pandemic has confined millions in their homes, rep­re­sent­ing an un­prece­dented case for spending more time together with family members. This situation is a challenge for house­holds, given that more time with the partner or children may not nec­es­sar­ily translate into increased well-​being. This paper explores sub­jec­tive well-​being in the uses of time for US and UK workers, dif­fer­en­ti­at­ing between solo ac­tiv­i­ties and ac­tiv­i­ties done with family members. Using the American and British time use surveys, we compute the instant utility as­so­ci­ated with paid work, unpaid work, leisure, and childcare ac­tiv­i­ties. The results show that workers prefer joint leisure to solo leisure, and sig­nif­i­cant dif­fer­ences exist between female and male workers for solo and joint market work and housework. The con­clu­sions of this paper indicate that there are gender dif­fer­ences in the effects of the COVID-19 pandemic on well-​being, affecting the time spent by in­di­vid­u­als in both paid and unpaid work.

During the recent COVID-19 pandemic, tra­di­tional (offline) chess tour­na­ments were pro­hib­ited and instead held online. We exploit this as a unique setting to assess the impact of moving offline tasks online on the cognitive per­for­mance of in­di­vid­u­als. We use the Ar­ti­fi­cial In­tel­li­gence embodied in a powerful chess engine to assess the quality of chess moves and as­so­ci­ated errors. Using within-​player com­par­isons, we find a sta­tis­ti­cally and eco­nom­i­cally sig­nif­i­cant decrease in per­for­mance when competing online compared to competing offline. Our results suggest that tele­work­ing might have adverse effects on workers per­form­ing cognitive tasks.

Despite a growing lit­er­a­ture on the impacts of the COVID-19 pandemic, scant evidence currently exists on its impacts on air quality. We offer the first study that provides cross-​national evidence on the causal impacts of COVID-19 on air pollution. We assemble a rich database con­sist­ing of daily, sub-​national level data of air quality for 178 countries before and after the COVID-19 lockdowns, and in­ves­ti­gate their impacts on air quality using a Re­gres­sion Dis­con­ti­nu­ity Design approach. We find the lockdowns to result in sig­nif­i­cant decreases in global air pollution. These results are con­sis­tent across measures of air quality and data sources and robust to various model spec­i­fi­ca­tions. Some limited evidence emerges that countries with a higher share of trade and man­u­fac­tur­ing in the economy or with an initially lower level of air pollution witness more reduced air pollution after the lockdowns; but the opposite result holds for countries near the equator. We also find that mobility re­stric­tions following the lockdowns are a possible ex­pla­na­tion for improved air quality.

This paper studies BMI as a correlate of the early spatial dis­tri­b­u­tion and intensity of Covid-19 across the districts of India and finds that con­di­tional on a range of in­di­vid­ual, household, and regional char­ac­ter­is­tics, adult BMI sig­nif­i­cantly predicts the like­li­hood that the district is a hotspot, the natural log of the confirmed number of cases, the case fatality rate, and the propen­sity that the district is a red zone. Con­trol­ling for air-​pollution, rainfall, tem­per­a­ture, de­mo­graphic factors that measure pop­u­la­tion density, the pro­por­tion of the elderly, and health in­fra­struc­ture including per capita health spending, the pro­por­tion of res­pi­ra­tory cases, and the number of viral disease outbreaks in the recent past, does not diminish the pre­dic­tive power of BMI in in­flu­enc­ing the spatial incidence and spread of the virus. The as­so­ci­a­tion between adult BMI and measures of spatial outcomes is es­pe­cially pro­nounced among educated pop­u­la­tions in urban settings, and im­per­vi­ous to con­di­tion­ing on dif­fer­ences in testing rates across states. We find that among women, BMI proxies for a range of co­mor­bidi­ties (he­mo­glo­bin, high blood pressure and high glucose levels) that affects the severity of the virus while among men, these health in­di­ca­tors are less important and exposure to risk of con­tract­ing the virus as measured by work propen­si­ties is ex­plana­tory. We conduct het­ero­gene­ity and sen­si­tiv­ity checks and control for dif­fer­ences that may arise due to vari­a­tions in timing of onset. Our results provide a readily available health marker that may be used to identify es­pe­cially at-risk pop­u­la­tions in de­vel­op­ing countries like India.

We study how patterns of in­ter­gen­er­a­tional residence possibly influence fa­tal­i­ties from Covid-19. We use aggregate data on Covid-19 deaths, the share of young adults living with their parents, and a number of other sta­tis­tics, for the 27 countries in the European Union, the UK, and all US states. Con­trol­ling for pop­u­la­tion size, we find that more people died from Covid in countries or states with higher rates of in­ter­gen­er­a­tional co-​residence. This positive cor­re­la­tion persists even when con­trol­ling for date of first death, presence of lockdown, Covid tests pc, hospital beds per capita, pro­por­tion of elderly, GDP pc, gov­ern­ment’s political ori­en­ta­tion, per­cent­age urban, and rental prices. The positive as­so­ci­a­tion between co-​residence and fa­tal­i­ties is led by the US. Our estimates pass the Oster test for selection on un­ob­serv­ables.