10.1038/s41562-020-00939-z

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.

Complacency, panic, and the value of gentle rule enforcement in addressing pandemics

2008.06834

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.

A generalized linear threshold model for an improved description of the spreading dynamics

10.1371/journal.pcbi.1008117

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.

Real-time estimation of disease activity in emerging outbreaks using internet search information

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.

Op-Ed: We rely on science. Why is it letting us down when we need it most?

10.31234/osf.io/e9fu3

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.

Who does or does not use the “Corona-Warn-App” and why?

10.31234/osf.io/4ske2

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.

Lockdown Evaluations During the First Wave of the COVID-19 Pandemic

10.31234/osf.io/5xkst

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.

Information Sources Differentially Trigger Coronavirus-Related Belief Change

10.31234/osf.io/fkjsx

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.

Impact of COVID-19 on Mental Health in India

10.3386/w27042

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.

Which Retail Outlets Generate the Most Physical Interactions?

10.3386/w27085

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.

Which Workers Bear the Burden of Social Distancing Policies?

10.3386/w27120

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.

The Value of Health Insurance during a Crisis: Effects of Medicaid Implementation on Pandemic Influenza Mortality

10.3386/w27105

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.

Pandemic Recession: L or V-Shaped?

10.3386/w27176

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.

Estimating Probabilities of Success of Vaccine and Other Anti-Infective Therapeutic Development Programs

10.3386/w27216

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.

US Unemployment Insurance Replacement Rates During the Pandemic

10.3386/w27366

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.

Fiscal Policy and COVID19 Restrictions in a Demand-Determined Economy

10.3386/w27597

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.

Assessing the Age Specificity of Infection Fatality Rates for COVID-19: Meta-Analysis & Public Policy Implications

10.3386/w27592

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.

Racial and Ethnic Disparities in COVID-19: Evidence from Six Large Cities

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.

Learning Epidemiology by Doing: The Empirical Implications of a Spatial-SIR Model with Behavioral Responses

10.31234/osf.io/j7srx

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.

Mode and Frequency of Covid-19 Information Updates, Political Values, and Future Covid-19 Vaccine Attitudes

10.31234/osf.io/43ec2

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.

(Re)Building Trust? Journals’ Open Science Badges Influence 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.

Paris, Marseille named as high-risk COVID zones, making curbs likelier

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.

Government quietly drops 1.3m Covid tests from England tally

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

Fewer hospital patients in Covid-19 hotspots

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.

Britain says it overcounted coronavirus death toll by 5,377

10.31234/osf.io/bxzm4

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.

Statements about the Pervasiveness of Behaviour Require Data about the Pervasiveness of Behaviour

10.31234/osf.io/xhw52

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.

Mistrust and Misinformation: A Two-Component, Socio-Epistemic Model of Belief in Conspiracy Theories

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

The Top Doctor Who Aced the Coronavirus Test

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.

Social distancing and markedly reduced travel in Sweden

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.

Identifying social media manipulation with OSoMe tools

2008.03334

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.

Robust Bayesian inference of network structure from unreliable data

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.

Children and COVID-19: State-Level Data Report

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

How to Have a Life in the Pandemic

"School children are not the drivers of community spread" of COVID. --@GovRonDeSantis

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

Sweatpants Forever

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

Lifestyles of the Rich and Reckless: Posh Pandemic Parties

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.

Canadian pranksters Nelk have millions of young followers. Their ‘dangerous’ decision to party during the pandemic is good for business

10.3386/w27237

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.

The Persuasive Effect of Fox News: Non-Compliance with Social Distancing During the Covid-19 Pandemic

10.3386/w27017