The impact of pandemics is magnified by the coexistence of two contradicting reactions to rare dire risks: panic and the ‘it won’t happen to me’ effect that hastens spread of the disease. We review research that clarifies the conditions that trigger the two biases, and we highlight the potential of gentle rule enforcement policies that can address these problematic conditions.

Many spreading processes in our real-life can be considered as a complex contagion, and the linear threshold (LT) model is often applied as a very representative model for this mechanism. Despite its intensive usage, the LT model suffers several limitations in describing the time evolution of the spreading. First, the discrete-time step that captures the speed of the spreading is vaguely defined. Second, the synchronous updating rule makes the nodes infected in batches, which can not take individual differences into account. Finally, the LT model is incompatible with existing models for the simple contagion. Here we consider a generalized linear threshold (GLT) model for the continuous-time stochastic complex contagion process that can be efficiently implemented by the Gillespie algorithm. The time in this model has a clear mathematical definition and the updating order is rigidly defined. We find that the traditional LT model systematically underestimates the spreading speed and the randomness in the spreading sequence order. We also show that the GLT model works seamlessly with the susceptible-infected (SI) or susceptible-infected-recovered (SIR) model. One can easily combine them to model a hybrid spreading process in which simple contagion accumulates the critical mass for the complex contagion that leads to the global cascades. Overall, the GLT model we proposed can be a useful tool to study complex contagion, especially when studying the time evolution of the spreading.

Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful “nowcasts” of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.

With so much information available about the severity of the coronavirus and the need to follow guidelines, some people still refuse to accept reality. The denial manifests itself in many ways, whether that be refusing to wear a mask or attending large gatherings. Using denial as a coping mechanism is not always a bad choice. Short-term, it gives someone the time to adjust to a situation. When it becomes a long-term crutch and puts others in harm's way, it can be dangerou

The U.S. Food and Drug Administration issued an emergency authorization on Saturday allowing public use of a saliva-based test for the coronavirus developed at Yale University and funded by the NBA and the National Basketball Players Association.The test, known as SalivaDirect, is designed for widespread public screening. The cost per sample could be as low as about $4, though the cost to consumers will likely be higher than that -- perhaps around $15 or $20 in some cases, according to expert sources.

Science is suffering from a replication crisis. Too many landmark studies can’t be repeated in independent labs, a process crucial to separating flukes and errors from solid results. The consequences are hard to overstate: Public policy, medical treatments and the way we see the world may have been built on the shakiest of foundations.

To slow the spread of SARS-CoV-2, the German government released the “Corona-Warn-App”, a smartphone application that warns users if they have come into contact with other users tested posi- tive for SARS-CoV-2. Since using the "Corona-Warn-App" is health-relevant behavior, it is essential to understand who is (and who is not) using it and why. In N = 1,972 German adults, we found that non-users were on average older, female, healthier, in training, and had low general trust in others. Most frequently named reasons by non-users were privacy concerns, doubts about the effectiveness of the app, and lack of technical equipment.

Government responses against COVID-19 has been met with salient protests across multiple Western democracies. Such protests have received significant media attention but we know little about the extent to which they reflect the views of the broader public. To fill this lacuna, this manuscript investigates how citizens across a number of Western democracies evaluate the interventions imposed by their government to contain the COVID-19 pandemic. Relying on large-scale, representative surveys from eight countries (Denmark, France, Germany, Hungary, Italy, United Kingdom, United States and Sweden), we investigate how pandemic-specific and broader political attitudes correlate with support for government lockdowns in the first wave of the pandemic (March 19 -- April 8), a period hallmarked by stringent policies in all of our countries. We find medium to high levels of government support in all eight countries. Furthermore, our results suggest that these levels of support are generated by a unique coalition of fearful, prosocial and knowledgable individuals. While such groups are often political opponents, the unprecedented nature of the COVID-19 pandemic aligns their interests.

During a global health crisis, people are exposed to vast amounts of information from a variety of sources. Here, we assessed which information sources could increase COVID- 19 knowledge by endorsing accurate information and refuting inaccurate information. A nationally representative sample of 1060 Cloud Research participants first rated the accuracy of a set of statements about COVID-19 (belief pre-test). Then, they were randomly assigned to one of 10 between-subjects conditions for which we varied the source that provided belief-relevant information: a political leader (President Trump or Vice-President Biden), a health authority (Doctor Fauci or the CDC), an anecdote (of a Democrat or a Republican), a large group of prior participants (Democrats, Republicans, or No affiliation), or no source (Control Condition). Finally, they rated the accuracy of the initial set of statements again (belief post-test). We found that, compared to the Control Condition, participants increased in COVID-19 knowledge by changing their beliefs to align with health authorities and with all three large groups of prior participants, and were not influenced by political leaders or anecdotes. Information source did not interact with participants’ political affiliation, suggesting that Democrats and Republicans are similarly affected by COVID-19 information sources.

The emergence of novel CoronaVirus Disease (COVID-19) has crossed borders at a lightening speed, infecting people all over the world within China and all over the globe. This virus has impacted in a way that has imposed mandatory lockdowns in many countries including India. However, since the lockdown has been imposed, attention is being focussed on the economic repercussions, migrants and livelihoods. Mental health issues such as anxiety, worry, fear of infection, sleep disturbance and in some cases suicide are side lined. This paper reviews the current scenario of COVID-19 in India in the context of mental health and related issues. Alongside, looks at ways to build awareness among the public on mental health during COVID-19.

Business travel, which used to represent $1.5 trillion a year – about 1.7% of world GDP – has slowed to a trickle. But the economic impact will extend far beyond lost jobs at airlines and in the hospitality industry, because it will also lead to a substantial decline in the transfer of knowledge.

On my second-to-last day in the hospital, I felt a sudden, unexplainable fatigue—nothing else. I finished work, asked the resident to page me with updates, and left early. The next morning, I awoke with a fever, sweats, headache, and mild cough. Not surprisingly, I tested positive for COVID-19. At the testing site, I became confused when someone asked for my address and insurance. Upon my return home, I isolated myself from my family in my bedroom. That was the last time I left my bedroom for 4 weeks.

Genomics could reveal details about the source of the country’s first outbreak in more than 100 days, says epidemiologist Amanda Kvalsvig.

This paper seeks to answer the simple question of what category of retail outlets generates the most physical interactions in the regular course of life. In this way, we aim to bring a marketing perspective to discussions about which businesses may be most risky from the standpoint of spreading contagious disease. We use detailed data from people's mobile devices prior to the implementation of social distancing measures in the United States. With this data, we examine a number of potential indicators of risk of contagion: The absolute number of visits and visitors, how many of the visits are generated by the same people, the median average distance traveled by the visitor to the retailer, and the number of customers from Canada and Mexico. We find that retailers with a single outlet tend to attract relatively few visitors, fewer one-off visitors, and have fewer international customers. For retailers that have multiple stores the patterns are non-linear. Retailers that have such a large number of stores that they are ubiquitous, tend to exhibit fewer visits and visitors and attract customers from a smaller distance. However, retailers that have a large enough footprint to be well known, but not large enough to be ubiquitous tend to attract a large number of visitors who make one-off visits, travel a long distance, and are disproportionately international.

What are the characteristics of workers in jobs likely to be initially affected by broad social distancing and later by narrower policy tailored to jobs with low risk of disease transmission? We use O NET to construct a measure of the likelihood that jobs can be conducted from home (a variant of Dingel and Neiman, 2020) and a measure of low physical proximity to others at work. We validate the measures by showing how they relate to similar measures constructed using time use data from ATUS. Our main finding is that workers in low-work-from-home or high-physical- proximity jobs are more economically vulnerable across various measures constructed from the CPS and PSID: they are less educated, of lower income, have fewer liquid assets relative to income, and are more likely renters. We further substantiate the measures with behavior during the epidemic. First, we show that MSAs with less pre-virus employment in work-from-home jobs experienced smaller declines in the incidence of `staying-at-home', as measured using SafeGraph cell phone data. Second, we show that both occupations and types of workers predicted to be employed in low work-from-home jobs experienced greater declines in employment according to the March 2020 CPS. For example, non-college educated workers experienced a 4ppt larger decline in employment relative to those with a college degree.

This paper studies how better access to public health insurance affects infant mortality during pandemics. Our analysis combines cross-state variation in mandated eligibility for Medicaid with two influenza pandemics — the 1957-58 "Asian Flu" pandemic and the 1968-69 "Hong Kong Flu" — that arrived shortly before and after the program's introduction. Exploiting heterogeneity in the underlying severity of these two shocks across counties, we find no relationship between Medicaid eligibility and pandemic infant mortality during the 1957-58 outbreak. After Medicaid implementation, we find that better access to insurance in high-eligibility states substantially reduced infant mortality during the 1968-69 pandemic. The reductions in pandemic infant mortality are too large to be attributable solely to new Medicaid recipients, suggesting that the expansion in health insurance coverage mitigated disease transmission among the broader population.

We develop and calibrate a search-theoretic model of the labor market in order to forecast the evolution of the aggregate US labor market during and after the coronavirus pandemic. The model is designed to capture the heterogeneity of the transitions of individual workers across states of unemployment, employment and across different employers. The model is also designed to capture the trade-offs in the choice between temporary and permanent layoffs. Under reasonable parametrizations of the model, the lockdown instituted to prevent the spread of the novel coronavirus is shown to have long-lasting negative effects on unemployment. This is so because the lockdown disproportionately disrupts the employment of workers who need years to find stable jobs.

A key driver in biopharmaceutical investment decisions is the probability of success of a drug development program. We estimate the probabilities of success (PoSs) of clinical trials for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 nonvaccine programs targeting infectious diseases. The overall estimated PoS for an industry-sponsored vaccine program is 39.6%, and 16.3% for an industry-sponsored anti-infective therapeutic. Among industry-sponsored vaccines programs, only 12 out of 27 disease categories have seen at least one approval, with the most successful being against monkeypox (100%), rotavirus (78.7%), and Japanese encephalitis (67.6%). The three infectious diseases with the highest PoSs for industry-sponsored nonvaccine therapeutics are smallpox (100%), cytomegalovirus (CMV) infection (31.8%), and onychomycosis (29.8%). Non-industry-sponsored vaccine and nonvaccine development programs have lower overall PoSs: 6.8% and 8.2%, respectively. Viruses involved in recent outbreaks—Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS), Ebola, and Zika—have had a combined total of only 45 nonvaccine development programs initiated over the past two decades, and no approved therapy to date. These estimates offer guidance both to biopharma investors as well as to policymakers seeking to identify areas most likely to be underserved by private sector engagement and in need of public sector support.

We use micro data on earnings together with the details of each state’s UI system under the CARES Act to compute the entire distribution of current UI benefits. The median replacement rate is 134%. Two-thirds of UI eligible workers can receive benefits which exceed lost earnings and one-fifth can receive benefits at least double lost earnings. There is sizable variation in the effects of the CARES Act across occupations and states, with important distributional consequences. We show how alternative UI expansion policies would change the distribution of UI benefits and thus affect resulting liquidity provision, progressivity, and labor supply incentives.

We evaluate the effects of COVID19 restrictions and fiscal policy in a model featuring economic slack. The restrictions can reduce current-period GDP by more than is directly associated with the restrictions themselves even if prices and wages are flexible, households can smooth consumption, and workers are mobile across sectors. The most effective fiscal policies depend on (a) the joint distribution of capital operating costs with respect to firm revenues, (b) the extent to which the price of capital adjusts, and (c) additional factors that determine whether the economy will enter a boom or a slump after the restrictions are lifted, such as the effect of the restrictions on inequality and on spending by high-income households.

This paper assesses the age specificity of the infection fatality rate (IFR) for COVID-19. Our benchmark meta-regression synthesizes the age-specific IFRs from six recent large-scale seroprevalence studies conducted in Belgium, Geneva, Indiana, New York, Spain, and Sweden. The estimated IFR is close to zero for children and younger adults but rises exponentially with age, reaching about 0.3 percent for ages 50-59, 1.3 percent for ages 60-69, 4.6 percent for ages 70-79, and 25 percent for ages 80 and above. We compare those predictions to the age-specific IFRs implied by recent seroprevalence estimates for nine other U.S. locations, three smale-scale studies, and three countries (Iceland, New Zealand, and Republic of Korea) that have engaged in comprehensive tracking and tracing of COVID-19 infections. We also review seroprevalence studies of 32 other locations whose design was not well-suited for estimating age-specific IFRs. Our findings indicate that COVID-19 is not just dangerous for the elderly and infirm but also for healthy middle-aged adults, for whom the fatality rate is more than 50 times greater than the risk of dying in an automobile accident. Consequently, the overall IFR for a given location is intrinsically linked to the age-specific pattern of infections. In a scenario where the U.S. infection rate reaches 20 percent, our analysis indicates that protecting vulnerable age groups could prevent more than 200,000 deaths.

As of June 2020, the coronavirus pandemic has led to more than 2.3 million confirmed infections and 121 thousand fatalities in the United States, with starkly different incidence by race and ethnicity. Our study examines racial and ethnic disparities in confirmed COVID-19 cases across six diverse cities – Atlanta, Baltimore, Chicago, New York City, San Diego, and St. Louis – at the ZIP code level (covering 436 “neighborhoods” with a population of 17.7 million). Our analysis links these outcomes to six separate data sources to control for demographics; housing; socioeconomic status; occupation; transportation modes; health care access; long-run opportunity, as measured by income mobility and incarceration rates; human mobility; and underlying population health. We find that the proportions of black and Hispanic residents in a ZIP code are both positively and statistically significantly associated with COVID-19 cases per capita. The magnitudes are sizeable for both black and Hispanic, but even larger for Hispanic. Although some of these disparities can be explained by differences in long-run opportunity, human mobility, and demographics, most of the disparities remain unexplained even after including an extensive list of covariates related to possible mechanisms. For two cities – Chicago and New York – we also examine COVID-19 fatalities, finding that differences in confirmed COVID-19 cases explain the majority of the observed disparities in fatalities. In other words, the higher death toll of COVID-19 in predominantly black and Hispanic communities mostly reflects higher case rates, rather than higher fatality rates for confirmed cases.

We simulate a spatial behavioral model of the diffusion of an infection to understand the role of geographic characteristics: the number and distribution of outbreaks, population size, density, and agents’ movements. We show that several invariance properties of the SIR model with respect to these variables do not hold when agents interact with neighbors in a (two dimensional) geographical space. Indeed, the local interactions arising in the spatial model give rise to matching frictions and local herd immunity effects which play a fundamental role in the dynamics of the infection. We also show that geographical factors affect how behavioral responses affect the epidemics. We derive relevant implications for the estimation of epidemiological models with panel data from several geographical units.

We develop a quantitative framework for exploring how individuals trade off the utility benefit of social activity against the internal and external health risks that come with social interactions during a pandemic. We calibrate the model to external targets and then compare its predictions with daily data on social activity, fatalities, and the estimated effective reproduction number R(t) from the COVID-19 pandemic in March-June 2020. While the laissez- faire equilibrium is consistent with much of the decline in social activity that we observed in US data, optimal policy further imposes immediate and highly persistent social distancing. Notably, neither equilibrium nor optimal social distancing is extremely restrictive, in the sense that the effective reproduction number never falls far below 1. The expected cost of COVID-19 in the US is substantial, $12,700 in the laissez-faire equilibrium and $8,100 per person under an optimal policy. Optimal policy generates this large welfare gain by shifting the composition of costs from fatalities to persistent social distancing.

This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the response to the novel Coronavirus in the United States. The WEI shows a strong and sudden decline in economic activity starting in the week ending March 21, 2020. In the most recent week ending March 28, the WEI indicates economic activity has fallen further to -6.19% scaled to 4 quarter growth in GDP.

This paper measures the role of the diffusion of high-speed Internet on an individual's ability to self-isolate during a global pandemic. We use data that tracks 20 million mobile devices and their movements across physical locations, and whether the mobile devices leave their homes that day. We show that while income is correlated with differences in the ability to stay at home, the unequal diffusion of high-speed Internet in homes across regions drives much of this observed income effect. We examine compliance with state-level directives to avoid leaving your home. Devices in regions with either high-income or high-speed Internet are less likely to leave their homes after such a directive. However, the combination of having both high income and high-speed Internet appears to be the biggest driver of propensity to stay at home. Our results suggest that the digital divide---or the fact that income and home Internet access are correlated---appears to explain much inequality we observe in people's ability to self-isolate.

The outbreak of covid19 has significantly disrupted the economy. This note attempts to quantify the macroeconomic impact of costly and deadly disasters in recent US history, and to translate these estimates into an analysis of the likely impact of covid19. A costly disaster series is constructed over the sample 1980:1-2019:12 and the dynamic impact of a costly disaster shock on economic activity and on uncertainty is studied using a VAR. Unlike past natural disasters, covid19 is a multi-month shock that is not local in nature, disrupts labor market activities rather than destroys capital, and harms the social and physical well being of individuals. Calibrating different shock profiles to reflect these features, we find that the effects of the event last from two months to over a year, depending on the sector of the economy. Even a conservative calibration of a 3-month, 60 standard deviation shock is forecast to lead to a cumulative loss in industrial production of 12.75% and in service sector employment of nearly 17% or 24 million jobs over a period of ten months, with increases in macro uncertainty that last five months.

We analyze the repercussions of the 1918 Influenza Pandemic on demographic measures, human capital formation, and productivity markers in the state of Sao Paulo, Brazil's financial center and the most populous city in South America today. Leveraging temporal and spatial variation in district-level estimates of influenza-related deaths for the period 1917-1920 combined with a unique database on socio-economic, health and productivity outcomes constructed from historical and contemporary documents for all districts in Sao Paulo, we find that the 1918 Influenza pandemic had significant negative impacts on infant mortality and sex ratios at birth in 1920 (the short-run). We find robust evidence of persistent effects on health, educational attainment and productivity more than twenty years later. Our study highlights the importance of documenting the legacy of historical shocks in understanding the development trajectories of countries over time.

Before answering, I should say that I am responding from a particular perspective, given that I was part of an open letter to the UK government on this issue in March of this year. The following piece outlines in a bit more detail our thinking in writing that letter:https://behavioralscientist.org/why-a-group-of-behavioural-scientists-penned-an-open-letter-to-the-uk-government-questioning-its-coronavirus-response-covid-19-social-distancing/

There seems to have been a lot of criticism for the use of the term "behavioural fatigue", and its potential impact on policy, by the UK government in the early part of the pandemic

With the onset of COVID-19, many research labs have had to shut down and temporarily suspend their research efforts. Months later, labs are slowly beginning to reopen, but have had to make adaptations to their research methods in response to the pandemic. This webinar will present ways to get your research project back on track. Our panelists will describe how they were able to successfully recover from delays and lab closures by having a solid lab reopening plan; changing research protocols; taking into account the stressors that a pandemic has had on research participants; and finding creative ways to get back to data collection.

The Covid-19 pandemic is reshaping almost every aspect of our society, including our health care systems. In this global emergency, in which the coronavirus continues its inexorable spread with a disproportional impact on communities of color, the urgent search for effective therapeutics and an effective vaccine has led to unprecedented collaborations between health care and drug companies, academic researchers, nonprofits and governments. Even so, some stresses to this collaborative approach are evident, mostly from governments eager to secure first for their domestic populations treatments and potential vaccines.

During the covid-19 pandemic we've been bombarded with stats – making it more important than ever to understand what the numbers can and can't tell us

Coronavirus Timeline/Overview By The Numbers Latest Coverage News Campus City Obituaries Sports Ice Hockey Basketball Soccer Field Hockey Lacrosse Softball Cross Country Track and Field Club Sports Columnists Features Community Business Science Arts Opinion Columns Editorial Comics Letters to Editor Op-Ed Blogs The Now Boston Hockey Blog Full Court Press Podcasts East to West Terrier Hockey Talk Clout Chasing Photo Video Campus, News Boston University instates policy to issue degrees to students after death, starting this Fall August 12, 2020 12:48 pm by Angela Yang Students who die while attending Boston University will be able to undergo an official process to obtain a posthumous degree, starting this Fall. The University released the policy June 12, but did not make an announcement or notify the BU community otherwise.

The Collaborative Filtering (CF) algorithm based on trust has been the main method used to solve the cold start problem in Recommendation Systems (RSs) for the past few years. Nevertheless, the current trust-based CF algorithm ignores the implicit influence contained in the ratings and trust data. In this paper, we propose a new rating prediction model named the Rating-Trust-based Recommendation Model (RTRM) to explore the influence of internal factors among the users. The proposed user internal factors include the user reliability and popularity. The internal factors derived from the explicit behavior data (ratings and trust), which can help us understand the user better and model the user more accurately. In addition, we incorporate the proposed internal factors into the Singular Value Decomposition Plus Plus (SVD + +) model to perform the rating prediction task. Experimental studies on two common datasets show that utilizing ratings and trust data simultaneously to mine the factors that influence the relationships among different users can improve the accuracy of rating prediction and effectively relieve the cold start problem.

Online discussion threads are important means for individual decision-making and for aggregating collective judgments, e.g. the `wisdom of crowds'. Empirical investigations of the wisdom of crowds are currently ambivalent about the role played by social information. While some findings suggest that social information undermines crowd accuracy due to correlated judgment errors, others show that accuracy improves. We investigate experimentally the accuracy of threads in which participants make magnitude estimates of varying difficulty while seeing a varying number of previous estimates. We demonstrate that, for difficult tasks, seeing preceding estimates aids the wisdom of crowds. If, however, participants only see extreme estimates, wisdom quickly turns into folly. Using a Gaussian Mixture Model, we assign a persuadability score to each participant and show that persuadability increases with task difficulty and with the amount of social information provided. In filtered threads, we see an increasing gap between highly persuadable participants and skeptics.

The synchronization of human networks is essential for our civilization and understanding its dynamics is important to many aspects of our lives. Human ensembles were investigated, but in noisy environments and with limited control over the network parameters which govern the network dynamics. Specifically, research has focused predominantly on all-to-all coupling, whereas current social networks and human interactions are often based on complex coupling configurations. Here, we study the synchronization between violin players in complex networks with full and accurate control over the network connectivity, coupling strength, and delay. We show that the players can tune their playing period and delete connections by ignoring frustrating signals, to find a stable solution. These additional degrees of freedom enable new strategies and yield better solutions than are possible within current models such as the Kuramoto model. Our results may influence numerous fields, including traffic management, epidemic control, and stock market dynamics.

Across Britain, parks have offered millions of people respite from the coronavirus crisis – a breathing space amid infection anxieties, crowded flats, home-schooling and job insecurities. With other venues closed, people turned to the nation’s 27,000 urban green spaces, from manicured landscapes to patchy neighbourhood parks and playing fields

The effect of social media consumption on perceptions of the seriousness of the Covid-19 pandemic, attitudes to public health requirements, and intentions towards a future Covid-19 vaccine are of live public health interest. There are also public health and security concerns that the pandemic has been accompanied and arguably further amplified by an ‘infodemic’ spreading misinformation. Tests of the effect of social media consumption on future Covid-19 vaccine intentions using population samples have been relatively few to date. This study contributes to the evidence base by examining social media consumption and vaccine intentions using British and US population samples. Methods: Data were gathered on 1,663 GB adults and 1,198 US adults from an online panel on attitudes towards a future vaccine alongside self-reported social and legacy broadcast and print media consumption. Ordered and binomial logit models were used to assess reported intentions regarding a future Covid-19 vaccine, testing the effects of media consumption type. Respondents were categorised in terms of their media consumption using a fourfold typology, as less frequent social, less frequent legacy media consumers (low-low); high social, low legacy media consumers (high-low); low social, high legacy (low-high); and high social, high legacy (high-high). Results: In the British sample, regression results indicate that those who receive Covid-19 updates more frequently via legacy media (low-high), and those being updated more than daily via both online and legacy media consumers, tend to provide significantly less Covid-19 vaccine-hesitant responses than low-low consumers. There is no significant difference between high social, low legacy media consumers and low-low consumers. In the US sample, membership of the low-high group is associated with lower Covid-19 vaccine hesitancy compared with low-low consumers. However, respondents consuming both social and legacy media several times daily exhibit similar vaccine intentions on average to those consuming social media daily and legacy media less often, providing a contrast with the UK sample. We also identify differences in Covid-19 vaccine intentions relating to demographics and political values. Conclusions: Differences in vaccine attentions are associated with the extent and balance of consumption of news relating to Covid-19 and its source. Political values and ethnic identity also appear to structure attitudes to a putative future Covid-19 vaccine.

Lithuania’s capital, Vilnius, has opened up public outdoor spaces to cafés and bars to boost the local economy, but the move has created conflicts as well as benefits.

As a response to the replication crisis, reforms call for the implementation of open science standards. In this regard, open science badges are a promising method to signal a study’s adherence to open science practices (OSP). In an experimental study, we investigated whether badges on journal article title pages affect non-scientists’ trust in scientists. Furthermore, we analyzed the moderating role of epistemic beliefs in this regard. We randomly assigned 270 non-scientists to two of three conditions: Badges awarded (visible compliance to OSP), badges not awarded (visible non-compliance to OSP) and no badges (compliance not visible, control condition). Results indicate that badges influence trust in scientists as well as the epistemic beliefs of participants. However, epistemic beliefs did not moderate the effect of badges on trust. In sum, our paper provides support to the notion that badges are an effective means to promote epistemic beliefs and trust in scientists.

The French government on Friday declared Paris and Marseille and its surrounding area high-risk zones for the coronavirus, granting authorities there powers to impose localised curbs to contain the spread of the disease. The declaration, made in a government decree, follows a sharp increase in COVID-19 infections in France over the past two weeks.

The government has quietly removed 1.3m coronavirus tests from its data because of double counting, raising fresh questions about the accuracy of the testing figures. In the government’s daily coronavirus update on Wednesday, it announced it had lowered the figure for “tests made available” by about 10% and discontinued the metric.

The number of people in hospital with Covid-19 fell in coronavirus hotspots in June and July, according to data released by NHS England.

England's coronavirus death toll is being revised downward by more than 5,000 fatalities after experts belatedly concluded they were probably overcounting deaths.This recently discovered “statistical anomaly” means that on Wednesday Britain’s official tally of deaths due to covid-19 was trimmed from 46,706 to 41,329 — a reduction of more than 10 percent.Support our journalism. Subscribe today.arrow-rightA review revealed that a government agency had been counting people as having died of the virus regardless of when they tested positive — meaning even an asymptomatic carrier who was infected in March but was killed in a traffic accident in July would be considered a covid-19 death.

Despite recent close attention to issues related to the reliability of psychological research (e.g., the replication crisis), issues of the validity of this research have not been considered to the same extent. This paper highlights an issue that calls into question the validity of the common research practice of studying samples of individuals, and using sample-based statistics to infer generalisations that are applied not only to the parent population, but to individuals. The lack of ergodicity in human data means that such generalizations are not justified. This problem is illustrated with respect to two common scenarios in psychological research that raise questions for the sorts of theories that are typically proposed to explain human behaviour and cognition. The paper presents a method of data analysis that requires closer attention to the range of behaviours exhibited by individuals in our research to determine the pervasiveness of effects observed in sample data. Such an approach to data analysis will produce results that are more in tune with the types of generalisations typical in reports of psychological research than mainstream analysis methods.

Although conspiracy theories are endorsed by about half the population and occasionally turn out to be true, they are more typically false beliefs that, by definition, have a paranoid theme. Consequently, psychological research to date has focused on determining whether there are traits that account for belief in conspiracy theories (BCT) within a deficit model. Alternatively, a two-component, socio-epistemic model of BCT is proposed that seeks to account for the ubiquity of conspiracy theories, their variance along a continuum, and the inconsistency of research findings likening them to psychopathology. Within this model, epistemic mistrust is the core component underlying conspiracist ideation that manifests as the rejection of authoritative information, focuses the specificity of conspiracy theory beliefs, and can sometimes be understood as a sociocultural response to breaches of trust, inequities of power, and existing racial prejudices. Once voices of authority are negated due to mistrust, the resulting epistemic vacuum can send individuals “down the rabbit hole” looking for answers where they are vulnerable to the biased processing of information and misinformation within an increasingly “post-truth” world. The two-component, socio-epistemic model of BCT argues for mitigation strategies that address both mistrust and misinformation processing, with interventions for individuals, institutions of authority, and society as a whole.

Public Health England has changed its definition of deaths. The new definition is now death in a person with a laboratory-confirmed positive COVID-19 test and died within (equal to or less than) 28 days of the first positive specimen date will now be reported

Hailed as “heroes” at the onset of the pandemic, supermarket employees now say they are overworked, overwhelmed and feeling expendable again.

The coronavirus pandemic has had a catastrophic effect on the nutritional health of the UK’s poorest citizens with as many as one in 10 forced to use food banks, and vast numbers skipping meals and going hungry, according to the government’s food safety watchdog. Food insecurity has shot up even further since lockdown as people’s income reduced, the Food Standards Agency (FSA) said, heightening the risk both of malnutrition and obesity as struggling families adopted highly restrictive “basic sustenance” diets that largely cut out healthy foods.

Background: The novel coronavirus disease 2019 (COVID-19) has negatively impacted mortality, economic conditions, and mental health. A large scale study on psychological reactions to the pandemic to inform ongoing population-level symptom tracking and response to treatment is currently lacking. Methods: Average intake scores for standard depression and anxiety symptom scales were tracked from January 1, 2017 to June 9, 2020 for patients seeking treatment from a digital mental health service to gauge the relationship between COVID-19 and self-reported symptoms. We applied natural language processing (NLP) to unstructured therapy transcript data from patients seeking treatment during the height of the pandemic in the United States between March 1, 2020 and June 9, 2020 to identify words associated with COVID-19 mentions. This analysis was used to identify symptoms that were present beyond those assessed by standard depression and anxiety measures. Results: Depression and anxiety symptoms reported by 169,889 patients between January 1, 2017 and June 9, 2020 were identified. There was no detectable change in intake depression symptom scores. Intake anxiety symptom scores increased 1.42 scale points [95% CI: 1.18, 1.65] between March 15, 2020 and April 1, 2020, when scores peaked. In the transcript data of these 169,889 patients, plus an expanded sample of 49,267 patients without symptom reports, term frequency-inverse document frequency (tf-idf) identified 2,377 positively correlated and 661 negatively correlated terms that were significantly (FDR<.01) associated with mentions of the virus. These terms were classifiable into 24 symptoms beyond those included in the diagnostic criteria for anxiety or depression. Conclusions: The COVID-19 pandemic may have increased intake anxiety symptoms for individuals seeking digital mental health treatment. NLP analyses suggest that standard symptom scales for depression and anxiety alone are inadequate to fully assess and track psychological reactions to the pandemic. Symptoms of grief, trauma, obsession-compulsion, agoraphobia, hypochondriasis, panic, and non- suicidal self-injury should be monitored as part of a new COVID-19 Syndrome category.

The COVID-19 pandemic poses significant threat to humans’ physical and mental wellbeing. In response, there has been an urgent “call to action” for psychological interventions that enhance positive emotion and psychological resilience. Extending upon past research documenting the wellbeing benefits of generous action, we conducted two online pre-registered experiments (N =1,623) during the pandemic in which participants were randomly assigned to engage in other- or self-beneficial action. Specifically, participants made charitable donations or gained money for themselves (Experiment 1); purchased COVID-19-related or COVID-19-unrelated items for someone else or for themselves (Experiment 2). Results showed that prosocial behavior led to greater positive affect, meaningfulness, empathy and social connectedness. Affect benefits were detectable whether prosocial spending was COVID-19-related or not. These findings provide support for one strategy to bolster wellbeing during the pandemic – generous action – which may also promote cooperation and social cohesiveness needed to contain and overcome the virus.

Exposure to right-wing media has been shown to relate to lower perceived threat from COVID-19, lower compliance with prophylactic measures against it, and higher incidence of infection and death. What features of right-wing media messages account for these effects? In a preregistered cross-sectional study (N = 554) we test a model that differentiates perceived consequences of two CDC recommendations—washing hands and staying home—for basic human values. People who consumed more right-wing media perceived these behaviors as less beneficial for their personal security, for the well-being of close ones, and the well-being of society at large. Perceived consequences of following the CDC recommendations mediated the relationship between media consumption and compliance with recommendations. Implications for public health messaging are discussed.

In response to the coronavirus disease 2019 (COVID-19) schools around the world have been closed to protect against the spread of coronavirus. In several countries, homeschooling has been introduced to replace classroom schooling. With a focus on individual differences, the present study examined 138 schoolers (age range = 6 to 21 years) regarding their self-control and boredom proneness. The results showed that both traits were important in predicting adherence to homeschooling. Schoolers with higher levels of self-control perceived homeschooling as less difficult, which in turn increased homeschooling adherence. In contrast, schoolers with higher levels of boredom proneness perceived homeschooling as more difficult, which in turn reduced homeschooling adherence. These results partially hold when it comes to studying in the classroom. However, boredom threatened adherence only in the homeschooling context. Our results indicate that boredom proneness is a critical construct to consider when educational systems switch to homeschooling during a pandemic.

Dr. Bonnie Henry kept the disease in check in British Columbia without harsh enforcement methods. Now, she is leading the way out of lockdown.

Swedes are largely following the government agencies’ advice and recommendations. This has been shown through surveys and data concerning movement patterns. Now travel within Sweden is permitted again – but if the guidelines are not followed, the Government is prepared to take measures.

On Friday, a group of behavioural scientists penned an open letter to the U.K. government questioning the decision to not enact strict social distancing policies. Social distancing measures, like closing restaurants and pubs, cancelling school and events, and working from home are being enforced across Europe. The U.K. government, however, has taken a decidedly different approach. At the time of publishing, around 1,400 cases of the virus had been detected in the U.K., up 1,000 from the week before. The open letter raises questions about the behavioural science evidence that may have been used to justify this decision—though a lack of transparency from the government has made it hard to discern what the official policy is. The letter has been signed by nearly 600 behavioural scientists from around the United Kingdom (at the time of publishing). In this op-ed, the authors of the letter explain why they are calling on the government to explain the scientific rationale behind their policy decisions thus far.

We're now more than seven months into the coronavirus pandemic that has upended the lives of most of Earth's inhabitants. And while it is true that the scientific community has learned many things about the SARS-CoV-2 virus and the disease it causes, Covid-19, there are also many gaps in our understanding.

COVID-19 has affected the livelihoods of an estimated 1.3 billion workers who represent nearly half of the world’s workforce, amplifying economic and social inequalities. In this session titled Redesigning Social Contracts in Crisis, the latest in the World Economic Forum’s Great Reset series, speakers from a range of locations and sectors respond to calls for stronger, more sustainable and more inclusive social contract.

The price the UK government was prepared to pay to save lives during the COVID-19 pandemic was far lower than in many other developed nations, a study has revealed.

To cover, or not to cover? That is the question. Not just a question, in fact, but a divisive issue that has led to protests in some parts of the world – most notably in the United States – and explosions of fake news on the internet.

Idaho lawmakers are moving to strip the authority of public health districts to close schools in emergencies or require masks for safety reasons.

As social media become the major platforms for discussions of important topics like national politics, public health, and environmental policy, there is a growing concern about the manipulation of these information ecosystems and their users. Malicious techniques include astroturf, amplification of misinformation, and trolling. Such abuses can be carried out by humans as well as social bots --- inauthentic accounts controlled in part by software. The resulting biased reality can fool even professional researchers. While researchers are increasingly interested in detecting and studying these malicious activities, there are serious challenges. First, the collection and analysis of data from social media require significant storage and computing resources. Second, knowledge, experience, and advanced computational skills are necessary to find patterns and signals of suspicious behaviors in large datasets. In this tutorial, we will present free tools from the Observatory of Social Media (OSoMe, pronounced “awe·some”) at Indiana University. We will focus on three tools that aim to help researchers and the general public combat online manipulation: Botometer, which helps detect social bots on Twitter; Hoaxy, which can track and visualize the diffusion of misinformation; and BotSlayer, which helps track and detect potential manipulation of information spreading on Twitter in real time. These tools are equipped with state-of-the-art algorithms and carefully designed user interfaces. They also provide public APIs to allow querying in bulk. They have served as the foundation for hundreds of research papers, and have helped thousands of users combat manipulation on social media.

Most empirical studies of complex networks do not return direct, error-free measurements of network structure. Instead, they typically rely on indirect measurements that are often error-prone and unreliable. A fundamental problem in empirical network science is how to make the best possible estimates of network structure given such unreliable data. In this paper we describe a fully Bayesian method for reconstructing networks from observational data in any format, even when the data contain substantial measurement error and when the nature and magnitude of that error is unknown. The method is introduced through pedagogical case studies using real-world example networks, and specifically tailored to allow straightforward, computationally efficient implementation with a minimum of technical input. Computer code implementing the method is publicly available.

Forecasts from Good Judgment's Professional Superforecasters. Probability values are updated daily at 7:00AM US Eastern time. 

State-level reports are the best publicly available data on child COVID-19 cases in the United States. The American Academy of Pediatrics and the Children’s Hospital Association are collaborating to collect and share all publicly available data from states on child COVID-19 cases (definition of “child” case is based on varying age ranges reported across states; see report Appendix for details and links to all data sources). On August 6, the age distribution of reported COVID-19 cases was provided on the health department websites of 49 states, New York City, the District of Columbia, Puerto Rico, and Guam. While children represented only 9.1% of all cases in states reporting cases by age, over 380,000 children have tested positive for COVID-19 since the onset of the pandemic. A smaller subset of states reported on hospitalizations and mortality by age, but the available data indicated that COVID-19-associated hospitalization and death is uncommon in children.

Julia Marcus, an epidemiologist and assistant professor at Harvard Medical School, discusses how to assess risk when engaging in different social activities.

New coronavirus infections have nearly doubled in France in recent weeks as Prime Minister Jean Castex warned that the country had been going "the wrong way" for two weeks.

BERLIN (Reuters) - Germany recorded the biggest daily increase in new coronavirus cases in more than three months, data showed on Wednesday, with the health minister warning of outbreaks in nearly all parts of the country due to holiday returnees and party-goers.

A total of 312,789 cases have been confirmed in the UK since the beginning of the pandemic.

Years of spending cuts and deepening inequality meant the worst off limped into this pandemic in a parlous state

The pandemic has put paid to the thrill of recounting illicit activities. And we’re all the poorer for it

A campaign has highlighted plight of unmarried couples from different countries parted for months

Safety will not be compromised for a Covid-19 vaccine, the US Food and Drug Administration commissioner said Monday.Dr. Stephen Hahn made the declaration in a video briefing with the American Medical Association. More than 5 million Americans have been infected with coronavirus, and more than 163,000 have died, according to Johns Hopkins data.

The COVID-19 pandemic response is affecting maternal and neonatal health services all over the world. We aimed to assess the number of institutional births, their outcomes (institutional stillbirth and neonatal mortality rate), and quality of intrapartum care before and during the national COVID-19 lockdown in Nepal.

Mandates for mask use in public during the recent COVID-19 pandemic, worsened by global shortage of commercial supplies, have led to widespread use of homemade masks and mask alternatives. It is assumed that wearing such masks reduces the likelihood for an infected person to spread the disease, but many of these mask designs have not been tested in practice. We have demonstrated a simple optical measurement method to evaluate the efficacy of masks to reduce the transmission of respiratory droplets during regular speech. In proof-of-principle studies, we compared a variety of commonly available mask types and observed that some mask types approach the performance of standard surgical masks, while some mask alternatives, such as neck fleece or bandanas, offer very little protection. Our measurement setup is inexpensive and can be built and operated by non-experts, allowing for rapid evaluation of mask performance during speech, sneezing, or coughing.

As US leaders work to control the spread of coronavirus, researchers across the globe are working to answer the mysteries that remain around infections. One of those mysteries: why the experience can be so different from person to person. One expert says the answer may involve looking at previous vaccines individuals have had.

Which is too bad because we really need to understand how the immune system reacts to the coronavirus.

PROVIDENCE, R.I. (AP) — Vilified, threatened with violence and in some cases suffering from burnout, dozens of state and local public health leaders around the U.S. have resigned or have been fired amid the coronavirus outbreak, a testament to how politically combustible masks, lockdowns and infection data have become.

Nearly half of Black small businesses had been wiped out by the end of April as the pandemic ravaged minority communities disproportionately, according to a report from the New York Fed.

Pandemic policy must include defining and measuring what we mean by mild infection.

Importance  Interpreting randomized clinical trials (RCTs) and their clinical relevance is challenging when P values are either marginally above or below the P = .05 threshold.Objective  To use the concept of reverse fragility index (RFI) to provide a measure of confidence in the neutrality of RCT results when assessed from the clinical perspective.Design, Setting, and Participants  In this cross-sectional study, a MEDLINE search was conducted for RCTs published from January 1, 2013, to December 31, 2018, in JAMA, the New England Journal of Medicine (NEJM), and The Lancet. Eligible studies were phase 3 and 4 trials with 1:1 randomization and statistically nonsignificant binary primary end points. Data analysis was performed from August 1, 2019, to August 31, 2019.Exposures  Single vs multicenter enrollment, total number of events, private vs government funding, placebo vs active control, and time to event vs frequency data.Main Outcomes and Measures  The primary outcome was the median RFI with interquartile range (IQR) at the P = .05 threshold. Secondary outcomes were the number of RCTs in which the number of participants lost to follow-up was greater than the RFI; the median RFI with IQR at different P value thresholds; the median reverse fragility quotient with IQR; and the correlation between sample sizes, number of events, and P values of the RCT and RFI.Results  Of the 167 RCTs included, 76 (46%) were published in the NEJM, 50 (30%) in JAMA, and 41 (24%) in The Lancet. The median (IQR) sample size was 970 (470-3427) participants, and the median (IQR) number of events was 251 (105-570). The median (IQR) RFI at the P = .05 threshold was 8 (5-13). Fifty-seven RCTs (34%) had an RFI of 5 or lower, and in 68 RCTs (41%) the number of participants lost to follow-up was greater than the RFI. Trials with P values ranging from P = .06 to P = .10 had a median (IQR) RFI of 3 (2-4). When compared, median (IQR) RFIs were not statistically significant for single-center vs multicenter enrollment (5 [4-13] vs 8 [5-13]; P = .41), private vs government-funded studies (9 [5-13] vs 8 [5-13]; P = .34), and time-to-event primary end points vs frequency data (9 [5-14] vs 7 [4-13]; P = .43). The median (IQR) RFI at the P = .01 threshold was 12 (7-19) and at the P = .005 threshold was 14 (9-21).Conclusions and Relevance  This cross-sectional study found that a relatively small number of events (median of 8) had to change to move the primary end point of an RCT from nonsignificant to statistically significant. These findings emphasize the nuance required when interpreting trial results that did not meet prespecified significance thresholds.

Preventing the spread of COVID-19 requires persuading the vast majority of the public to significantly change their behavior in numerous, costly ways. However, it is unclear which persuasive strategies are most effective at convincing people who are not fully compliant to take recommended actions, such as wearing a mask and staying home more often. In five studies (N = 5,351) conducted from March - July 2020, we evaluated 56 short messages aimed at convincing people to comply with public health guidelines. In two within-subjects studies, participants rated the persuasiveness of many short messages drawn from both past research on persuasion and original crowdsourcing. In three pre-registered, between-subjects experiments, we tested whether the four top-rated messages from the previous studies led people who were not fully compliant to increase their intentions to comply. We do not find consistent effects of any message, though a message emphasizing civic responsibility to reciprocate healthcare workers’ sacrifices performed best in three of five studies. Overall, these findings suggest that short messages are largely ineffective in increasing compliance with public health guidelines during advanced stages of the pandemic.

The home advantage (HA) is a robust phenomenon in soccer whereby the home team wins more games and scores more goals than the away team. One explanation is that the home crowd spurs on home team performance and causes the referee to unconsciously favour the home team. The Covid-19 pandemic provided a unique opportunity to assess this explanation for HA, as European soccer leagues played part of the 2019/2020 season with crowds present and concluded with crowds absent. Using multi-level modelling we compared team performance and referee decisions pre-Covid (crowd present) and post-Covid (crowd absent) across 9,528 games from 15 leagues in 11 countries. HA (goals scored and points gained) was significantly reduced post pandemic, which reflected the inferior performance of the home team. In addition, referees awarded significantly fewer sanctions against the away teams, and home teams created significantly fewer attacking opportunities when they played without fans.

Despite a tentative easing of the national lockdown, the lingering economic and social consequences of this policy are frighteningly obvious to anyone venturing into central London.

Even before the pandemic, the whole fashion industry had started to unravel. What happens now that no one has a reason to dress up?

Because money appears to be one of the best protections against the coronavirus, why not party like it’s 2019?

after nearly a decade of doing keg stands in university lectures, funnelling beers next to police and provoking hockey dads, they’ve become two of the most recognizable personalities for young people in North America: Jesse Sebastiani and Kyle Forgeard, a pair of heavy-drinking, hard-talking Ontarians, better known as “Nelk.”Boasting nearly 5.5 million subscribers on YouTube alone, their provocative prank videos — which have led to arrests — have garnered more than 700 million video views. On Instagram, flanked by a crew of abrasive, hypermasculine personalities, Nelk broadcasts their brand of pranking and partying to more than 3.4 million followers (including Drake and Justin Bieber).But the crew’s actions during the COVID-19 pandemic, which include organizing packed “brotests” to push California to open its gyms, lavish partying and constant travel within the U.S., are being criticized by fans who want them to set a better example.

We test for and measure the effects of cable news in the US on regional differences in compliance with recommendations by health experts to practice social distancing during the early stages of the COVID-19 pandemic. We use a quasi-experimental design to estimate the causal effect of Fox News viewership on stay-at-home behavior by using only the incremental local viewership due to the quasi-random assignment of channel positions in a local cable line-up. We find that a 10% increase in Fox News cable viewership (approximately 0:13 higher viewer rating points) leads to a 1.3 percentage point reduction in the propensity to stay at home. We find a persuasion rate of Fox News on non-compliance with stay-at-home behavior during the crisis of about 5:7% - 28:4% across our various social distancing metrics.

We use a repeated large-scale survey of households in the Nielsen Homescan panel to characterize how labor markets are being affected by the covid-19 pandemic. We document several facts. First, job loss has been significantly larger than implied by new unemployment claims: we estimate 20 million lost jobs by April 6th, far more than jobs lost over the entire Great Recession. Second, many of those losing jobs are not actively looking to find new ones. As a result, we estimate the rise in the unemployment rate over the corresponding period to be surprisingly small, only about 2 percentage points. Third, participation in the labor force has declined by 7 percentage points, an unparalleled fall that dwarfs the three percentage point cumulative decline that occurred from 2008 to 2016.

The Covid-19 crisis has lead to a reduction in the demand and supply of sectors that produce goods that need social interaction to be produced or consumed. We interpret the Covid-19 shock as a shock that reduces utility stemming from “social” goods in a two-sector economy with incomplete markets. We compare the advantages of lump-sum transfers versus a credit policy. For the same path of government debt, transfers are preferable when debt limits are tight, whereas credit policy is preferable when they are slack. A credit policy has the advantage of targeting fiscal resources toward agents that matter most for stabilizing demand. We illustrate this result with a calibrated model. We discuss various shortcomings and possible extensions to the model.

During a pandemic, an individual's choices can determine outcomes not only for the individual but also for the entire community. Beliefs, constraints and preferences may shape behavior. This paper documents demographic differences in behaviors, beliefs, constraints and risk preferences across gender, income and political affiliation lines during the new coronavirus disease (COVID-19) pandemic. Our main analyses are based on data from an original nationally representative survey covering 5,500 adult respondents in the U.S. We find substantial gaps in behaviors and beliefs across gender, income and partisanship lines; in constraints across income levels; and in risk tolerance among men and women. Based on location data from a large sample of smartphones, we also document significant differences in mobility across demographics, which are consistent with our findings based on the survey data.

I use Current Population Survey Data from February and April 2020 to examine how individual workers have transitioned between labor-market states and which workers have been hurt most by the COVID-19 pandemic. I find not only large effects on workers becoming unemployed but also a decline in labor-force participation, an increase in absence from one’s job, and a decrease in hours worked. Generally, more vulnerable populations—racial and ethnic minorities, those born outside the U.S., women with children, the least educated, and workers with a disability—have experienced the largest declines in the likelihood of (full-time) work and work hours.

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about six percent on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and therefore expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.

This paper quantitatively analyzes how policy responses to the COVID-19 pandemic should differ in developing countries. To do so we build an incomplete-markets macroeconomic model with heterogeneous agents and epidemiological dynamics that features several of the key distinctions between advanced and developing economies germane to the pandemic. We focus in particular on differences in: age structure, fiscal capacity, healthcare capacity, informality, and the frequency of contacts between individuals at home, work, school and other locations. The model predicts that blanket lockdowns are less effective in developing countries, saving fewer lives per unit of lost GDP. In contrast, age-specific policies are even more effective, since they focus scarce public funds on shielding the smaller population of older individuals. School closures are also more effective at saving lives in developing countries, providing a greater reduction in secondary transmissions between children and older adults at home.

How effective are restrictions on geographic mobility in limiting the spread of the COVID-19 pandemic? Using zip code data for Atlanta, Boston, Chicago, New York (NYC), and Philadelphia, we estimate that total COVID-19 cases per capita decrease on average by approximately 20 percent for every ten percentage point fall in mobility between February and May 2020. To address endogeneity concerns, we instrument for travel by the share of workers in remote work friendly occupations, and find a somewhat larger average decline of COVID-19 cases per capita of 27 percent. Using weekly data by zip code for NYC and a panel data specification including week and zip code fixed effects, we estimate a similar average decline of around 17 percent, which becomes larger when we measure mobility using NYC turnstile data rather than cellphone data. We find substantial heterogeneity across both space and over time, with stronger effects for NYC, Boston and Philadelphia than for Atlanta and Chicago, and the largest estimated coefficients for NYC in the early stages of the pandemic.

We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

No previous infectious disease outbreak, including the Spanish Flu, has impacted the stock market as forcefully as the COVID-19 pandemic. In fact, previous pandemics left only mild traces on the U.S. stock market. We use text-based methods to develop these points with respect to large daily stock market moves back to 1900 and with respect to overall stock market volatility back to 1985. We also evaluate potential explanations for the unprecedented stock market reaction to the COVID-19 pandemic. The evidence we amass suggests that government restrictions on commercial activity and voluntary social distancing, operating with powerful effects in a service-oriented economy, are the main reasons the U.S. stock market reacted so much more forcefully to COVID-19 than to previous pandemics in 1918-19, 1957-58 and 1968.

COVID-19 abruptly impacted the labor market with the unemployment rate jumping to 14.7 percent less than two months after state governments began adopting social distancing measures. Unemployment of this magnitude has not been seen since the Great Depression. This paper provides the first study of how the pandemic impacted minority unemployment using CPS microdata through April 2020. African-Americans experienced an increase in unemployment to 16.6 percent, less than anticipated based on previous recessions. In contrast, Latinx, with an unemployment rate of 18.2 percent, were disproportionately hard hit by COVID-19. Adjusting for concerns of the BLS regarding misclassification of unemployment, we create an upper-bound measure of the national unemployment rate of 26.5 percent, which is higher than the peak observed in the Great Depression. The April 2020 upper-bound unemployment rates are an alarming 31.8 percent for blacks and 31.4 percent for Latinx. Difference-in-difference estimates suggest that blacks were, at most, only slightly disproportionately impacted by COVID-19. Non-linear decomposition estimates indicate that a slightly favorable industry distribution partly protected them from being hit harder by COVID-19. The most impacted group are Latinx. Difference-in-difference estimates unequivocally indicate that Latinx were disproportionately impacted by COVID-19. An unfavorable occupational distribution and lower skills contributed to why Latinx experienced much higher unemployment rates than whites. These findings of early impacts of COVID-19 on unemployment raise important concerns about long-term economic effects for minorities.

We critically analyze the currently available status indicators of the COVID-19 epidemic so that state governors will have the guideposts necessary to decide whether to further loosen or instead retighten controls on social and economic activity. Overreliance on aggregate, state-level data in Wisconsin, we find, confounds the effects of the spring primary elections and the outbreak among meat packers. Relaxed testing standards in Los Angeles may have upwardly biased the observed trend in new infection rates. Reanalysis of New Jersey data, based upon the date an ultimately fatal case first became ill rather than the date of death, reveals that deaths have already peaked in that state. Evidence from Cook County, Illinois shows that trends in the percentage of positive tests can be wholly misleading. Trends on emergency department visits for influenza-like illness, advocated by the White House Guidelines, are unlikely to be informative. Data on hospital census counts in Orange County, California suggest that healthcare system-based indicators are likely to be more reliable and informative. An analysis of cumulative infections in San Antonio, Texas, shows how mathematical models intended to guide decisions on relaxation of social distancing are severely limited by untested assumptions. Universal coronavirus testing may not on its own solve difficult problems of data interpretation and causal inference.

As countries around the world grapple with Covid-19, their economies are grinding to a halt. For the first time since the Great Depression both advanced economies and developing economies are in recession. Governments and central banks have responded to the pandemic and the economic crisis using both fiscal and monetary tools on a scale that the world has not witnessed before. This paper analyzes the determinants of fiscal and monetary policies during the Covid-19 crisis. We find that high-income countries announced larger fiscal policies than lower-income countries. We also find that a country’s credit rating is the most important determinant of its fiscal spending during the pandemic. High-income countries entered the crisis with historically low interest rates and as a result were more likely to use nonconventional monetary policy tools. These findings raise the concern that countries with poor credit histories – those with lower credit ratings and, in particular, lower-income countries – will not be able to deploy fiscal policy tools effectively during economic crises.

Using data on over 6,000 firms across 56 economies during the first quarter of 2020, we evaluate the connection between corporate characteristics and stock price reactions to COVID-19 cases. We find that the pandemic-induced drop in stock prices was milder among firms with (a) stronger pre-2020 finances (more cash, less debt, and larger profits), (b) less exposure to COVID-19 through global supply chains and customer locations, (c) more CSR activities, and (d) less entrenched executives. Furthermore, the stock prices of firms with greater hedge fund ownership performed worse, and those of firms with larger non-financial corporate ownership performed better. We believe ours is the first paper to assess international, cross-firm stock price reactions to COVID-19 as functions of these pre-shock corporate characteristics.

We analyze the con­se­quences of the decision of French gov­ern­ment to maintain the first round of the municipal elections on March 15, 2020 on local excess mortality in the following weeks. We exploit het­ero­gene­ity across mu­nic­i­pal­i­ties in voter turnout, which we in­stru­ment using a measure of the intensity of local com­pe­ti­tion. The results reveal that a higher turnout was as­so­ci­ated with a sig­nif­i­cantly higher death counts for the elderly pop­u­la­tion in the five weeks after the elections. If the his­tor­i­cally low turnout in 2020 had been at its 2014 level, the number of deaths would have been 21.8 percent higher than the one that was recorded. More than three quarters of these ad­di­tional deaths would have occurred among the in­di­vid­u­als aged 80 and above.

This paper provides a critical review of models of the spread of the coronavirus (SARS-CoV-2) epidemic that have been influential in recent policy decisions. There is tremendous opportunity for social scientists to advance the relevant literature as new and better data becomes available to bolster economic outcomes and save lives.

We analyze the behavior of 401(k) plan participants during the first quarter of 2020, when COVID-19 generated historic volatility, large negative returns, and significant unemployment. Only 2.1% of participants invested in TDFs made any changes to their portfolios, with even lower rates of change among those defaulted into robo-advised managed accounts, suggesting that delegation can decrease the likelihood of portfolio mistakes by less sophisticated participants. While 16.6% of non-delegated participants made portfolio changes, these changes were more likely among more sophisticated participants and appear not to have reduced participants’ quarterly returns. Consistent with liquidity constraints, however, withdrawals spike following job loss.

We construct a quantitative model of an economy hit by an epidemic. People differ by age and skill, and choose occupations and whether to commute to work or work from home, to maximize their income and minimize their fear of infection. Occupations differ by wage, infection risk, and the productivity loss when working from home. By setting the model parameters to replicate the progression of COVID-19 in South Korea and the United Kingdom, we obtain three key results. First, government-imposed lock-downs may not present a clear trade-off between GDP and public health, as commonly believed, even though its immediate effect is to reduce GDP and infections by forcing people to work from home. A premature lifting of the lock-down raises GDP temporarily, but infections rise over the next months to a level at which many people choose to work from home, where they are less productive, driven by the fear of infection. A longer lock-down eventually mitigates the GDP loss as well as flattens the infection curve. Second, if the UK had adopted South Korean policies, its GDP loss and infections would have been substantially smaller both in the short and the long run. This is not because Korea implemented policies sooner, but because aggressive testing and tracking more effectively reduce infections and disrupt the economy less than a blanket lock-down. Finally, low-skill workers and self-employed lose the most from the epidemic and also from the government policies. However, the policy of issuing “visas” to those who have antibodies will disproportionately benefit the low-skilled, by relieving them of the fear of infection and also by allowing them to get back to work.

We study the effects of the temporary federal paid sick leave mandate that became effective April 1st, 2020 on ‘social distancing,’ as proxied by physical mobility behavior gleaned from cellular devices. The national paid leave policy was implemented in response to the COVID-19 outbreak and provided many private and many public employees, including individuals employed in the gig economy, with up to two weeks of paid leave. We study the early impact of the federal paid sick leave policy using interrupted time series analyses and difference-in-differences methods leveraging pre-FFCRA county-level differences in mobility. Our proxies for the ability to social distance are the share of cellular devices that are located in the workplace eight or more hours per day (‘full-time work’) and leave the home for less than one hour per day (‘at home’) in each county. Our findings suggest that the federal mandate decreased our full-time work proxy and increased our at home proxy. In particular, we find an initial decrease in working full-time of 17.7% and increase in staying home of 7.5%, with effects dissipating within three weeks. Given that up to 47% of employees are covered by the federal mandate, our effect sizes are arguably non-trivial

The Paycheck Protection Program (PPP) aimed to quickly deliver hundreds of billions of dollars of loans to small businesses, with the loans administered via private banks. In this paper, we use firm-level data to document the demand and supply of PPP funds. Using an instrumental variables approach, we find that PPP loans led to a 14 to 30 percentage point increase in a business’s expected survival, and a positive but imprecise effect on employment. Moreover, the effects on survival were heterogeneous and highlight an important tradeoff faced by policymakers: while administering the loans via private banks allowed for rapid delivery of funds, it also limited the government’s ability to target the funding - instead allowing pre-existing connections between businesses and banks to determine which firms would benefit from the program.

Using a large-scale survey of U.S. households during the Covid-19 pandemic, we study how new information about fiscal and monetary policy responses to the crisis affects households’ expectations. We provide random subsets of participants in the Nielsen Homescan panel with different combinations of information about the severity of the pandemic, recent actions by the Federal Reserve, stimulus measures, as well as recommendations from health officials. This experiment allows us to assess to what extent these policy announcements alter the beliefs and spending plans of households. In short, they do not, contrary to the powerful effects they have in standard macroeconomic models.

We study the optimal lockdown policy for a planner who wants to control the fatalities of a pandemic while minimizing the output costs of the lockdown. We use the SIR epidemiology model and a linear economy to formalize the planner's dynamic control problem. The optimal policy depends on the fraction of infected and susceptible in the population. We parametrize the model using data on the COVID19 pandemic and the economic breadth of the lockdown. The quantitative analysis identifies the features that shape the intensity and duration of the optimal lockdown policy. Our baseline parametrization is conditional on a 1% of infected agents at the outbreak, no cure for the disease, and the possibility of testing. The optimal policy prescribes a severe lockdown beginning two weeks after the outbreak, covers 60% of the population after a month, and is gradually withdrawn covering 20% of the population after 3 months. The intensity of the lockdown depends on the gradient of the fatality rate as a function of the infected, and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown, and shortens the duration of the optimal lockdown which ends more abruptly. Welfare under the optimal policy with testing is higher, equivalent to a one-time payment of 2% of GDP.

Evaluating the economic impact of "social distancing" measures taken to arrest the spread of COVID-19 raises a fundamental question about the modern economy: how many jobs can be performed at home? We classify the feasibility of working at home for all occupations and merge this classification with occupational employment counts. We find that 37 percent of jobs in the United States can be performed entirely at home, with significant variation across cities and industries. These jobs typically pay more than jobs that cannot be done at home and account for 46 percent of all US wages. Applying our occupational classification to 85 other countries reveals that lower-income economies have a lower share of jobs that can be done at home.

We study supply and demand shocks in a general disaggregated model with multiple sectors, multiple factors, input-output linkages, downward nominal wage rigidities, credit-constraints, and a zero lower bound. We use the model to understand how the Covid-19 crisis, an omnibus of supply and demand shocks, affects output, unemployment, and inflation, and leads to the coexistence of tight and slack labor markets. Negative sectoral supply shocks are stagflationary, whereas negative sectoral demand shocks are deflationary. Furthermore, complementarities in production amplify Keynesian spillovers from negative supply shocks but mitigate them for negative demand shocks. In a stylized quantitative model of the US, we find supply and demand shocks each explain about half the reduction in real GDP. Although there is as much as 7% Keynesian unemployment, this is concentrated in certain markets. Hence, aggregate demand stimulus is less than half as effective as in a typical recession where all labor markets are slack.

We examine how policymakers should react to a pandemic when there is significant uncertainty regarding key parameters relating to the disease. In particular, this paper explores how optimal mitigation policies change when incorporating uncertainty regarding the Case Fatality Rate (CFR) and the Basic Reproduction Rate (R0) into a macroeconomic SIR model in a robust control framework. This paper finds that optimal policy under parameter uncertainty generates an asymmetric optimal mitigation response across different scenarios: when the disease’s severity is initially underestimated the planner increases mitigation to nearly approximate the optimal response based on the true model, and when the disease’s severity is initially overestimated the planner maintains lower mitigation as if there is no uncertainty in order to limit excess economic costs.

Globalization is expected to be reversed, at least partially, in the post pandemic era. The Great Financial Recession of 2008–10 marked a historic turning point in the direction of weakening the degree of global economic integration. Now, in the post-pandemic era, policymakers appear poised to take deliberate steps to reinforce the movement toward de-globalization. At the same time, safety nets are expected to be strengthened. In this paper, we develop a model, with which we analyze central macroeconomic interactions between globalization and safety nets. We put together stylized elements of trade globalization, financial globalization, international tax competition, immigration, and welfare state, all in a two-skill, two-period stylized model, where policy (taxes and social benefits) is determined through majority voting.

In response to the ongoing COVID-19 pandemic, the US government brought about a collection of fiscal stimulus measures: the 2020 CARES Act. Among other provisions, this Act directed cash payments to households. We analyze households’ spending responses using high-frequency transaction data. We also explore heterogeneity by income levels, recent income declines, and liquidity. We find that households respond rapidly to receipt of stimulus payments, with spending increasing by $0.25-$0.35 per dollar of stimulus during the first 10 days. Households with lower incomes, greater income drops, and lower levels of liquidity display stronger responses. Liquidity plays the most important role, with no observed spending response for households with high levels of bank account balances. Relative to the effects of previous economic stimulus programs in 2001 and 2008, we see much smaller increases in durables spending and larger increases in spending on food, likely reflecting the impact of shelter-in-place orders and supply disruptions. We hope that our results inform the current debate about appropriate policy measures.

The U.S. economy currently faces a truly extraordinary degree of uncertainty as a consequence of the COVID-19 pandemic. In these circumstances, the Federal Reserve could begin highlighting alternative scenarios to illustrate key risks to the economic outlook, and such scenarios could inform the Fed’s policy strategy and public communications. In this paper, we present a set of illustrative scenarios, including a baseline scenario with a rapid but incomplete recovery this year (an upward-tilting checkmark), a benign scenario in which an effective cure or vaccine becomes available and facilitates a nearly complete recovery by mid-2021, and a severely adverse scenario involving persistently high unemployment and disinflationary pressures. Insights into these scenarios can be drawn from key historical episodes, including the Spanish flu, the Great Depression, the end of World War II, and the global financial crisis. We conclude by identifying key challenges that the Federal Reserve might face in adjusting its monetary policy and emergency credit facilities under each of these alternative scenarios.

Using weekly administrative payroll data from the largest U.S. payroll processing company, we measure the evolution of the U.S. labor market during the first four months of the global COVID-19 pandemic. After aggregate employment fell by 21 percent through late-April, employment rebounded somewhat through late-June. The re-opening of temporarily shuttered businesses contributed significantly to the employment rebound, particularly for smaller businesses. We show that worker recall has been an important component of recent employment gains for both re-opening and continuing businesses. Employment losses have been concentrated disproportionately among lower wage workers; as of late June employment for workers in the lowest wage quintile was still 20 percent lower relative to mid-February levels. As a result, average base wages increased between February and June, though this increase arose entirely through a composition effect. Finally, we document that businesses have cut nominal wages for almost 7 million workers while forgoing regularly scheduled wage increases for many others.

The “shutdown” economy of April 2020 is compared to a normally functioning economy both in terms of market and nonmarket activities. Three novel methods and data indicate that the shutdown puts market production 25-28 percent below normal in the short run. At an annual rate, the shutdown is costing $7 trillion, or about $15,000 per household per quarter. Employment already fell 28 million by early April 2020. These costs indicate, among other things, the value of innovation in both health and general business sectors that can accelerate the time when normal activity resumes.

In SIR models, homogeneous or with a network structure, infection rates are assumed to be exogenous. However, individuals adjust their behavior. Using daily data for 89 cities worldwide, we document that mobility falls in response to fear, as approximated by Google search terms. Combining these data with experimentally validated measures of social preferences at the regional level, we find that stringency measures matter less if individuals are more patient and altruistic preference traits, and exhibit less negative reciprocity community traits. We modify the homogeneous SIR and the SIR-network model to include agents' optimizing decisions on social interactions. Susceptible individuals internalize infection risk based on their patience, infected ones do so based on their altruism, and reciprocity matters for internalizing risk in SIR networks. A planner further restricts interactions due to a static and a dynamic inefficiency in the homogeneous SIR model, and due to an additional reciprocity inefficiency in the SIR-network model. We show that partial or targeted lockdown policies are efficient only when it is possible to identify infected individuals.

Rationing of medical resources is a critical issue in the COVID-19 pandemic. Most existing triage protocols are based on a priority point system, in which a formula specifies the order in which the supply of a resource, such as a ventilator, is to be rationed for patients. A priority point system generates an identical priority ranking specifying claims on all units. Triage protocols in some states (e.g. Michigan) prioritize frontline health workers giving heavier weight to the ethical principle of instrumental value. Others (e.g. New York) do not, reasoning that if frontline workers obtain high enough priority, there is a risk that they obtain all units and none remain for the general community. This debate is pressing given substantial COVID-19 health risks for frontline workers. In this paper, we analyze the consequences of rationing medical resources through a reserve system. In a reserve system, resources are placed into multiple categories. Priorities guiding allocation of units can reflect different ethical values between these categories. A reserve system provides additional flexibility over a priority point system because it does not dictate a single priority order for the allocation of all units. It offers a middle-ground approach that balances competing objectives, such as in the medical worker debate. This flexibility requires attention to implementation, especially the processing order of reserve categories. We describe our model of a reserve system, characterize its potential outcomes, and examine distributional implications of particular reserve systems. We also discuss several practical considerations with triage protocol design.

New York City’s multipronged subway system was a major disseminator – if not the principal transmission vehicle – of coronavirus infection during the initial takeoff of the massive epidemic that became evident throughout the city during March 2020. The near shutoff of subway ridership in Manhattan – down by over 90 percent at the end of March – correlates strongly with the substantial increase in the doubling time of new cases in this borough. Subway lines with the largest drop in ridership during the second and third weeks of March had the lowest subsequent rates of infection in the zip codes traversed by their routes. Maps of subway station turnstile entries, superimposed upon zip code-level maps of reported coronavirus incidence, are strongly consistent with subway-facilitated disease propagation. Reciprocal seeding of infection appears to be the best explanation for the emergence of a single hotspot in Midtown West in Manhattan.

We study the role of global supply chains in the impact of the Covid-19 pandemic on GDP growth for 64 countries. We discipline the labor supply shock across sectors and countries using the fraction of work in the sector that can be done from home, interacted with the stringency with which countries imposed lockdown measures. Using the quantitative framework and methods developed in Huo, Levchenko, and Pandalai-Nayar (2020), we show that the average real GDP downturn due to the Covid-19 shock is expected to be - 29:6%, with one quarter of the total due to transmission through global supply chains. However, "renationalization" of global supply chains does not in general make countries more resilient to pandemic-induced contractions in labor supply. The average GDP drop would have been - 30:2% in a world without trade in inputs and final goods. This is because eliminating reliance on foreign inputs increases reliance on the domestic inputs, which are also disrupted due to nationwide lockdowns. In fact, trade can insulate a country imposing a stringent lockdown from the pandemic-shock, as its foreign inputs are less disrupted than its domestic ones. Finally, unilateral lifting of the lockdowns in the largest economies can contribute as much as 2.5% to GDP growth in some of their smaller trade partners.