7,747 Matching Annotations
  1. Jul 2020
    1. So­cioe­co­nomic De­ter­mi­nants of COVID-19 In­fec­tions and Mortality: Evidence from England and Wales
    1. Entrepreneurial Recovery from COVID-19: Decentralization, Democratization, Demand, Distribution, and Demography
    1. EU Jobs at Highest Risk of COVID-19 Social Dis­tanc­ing: Will the Pandemic Ex­ac­er­bate Labour Market Divide?
    1. The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: a mean-field model perspective
    1. How the COVID-19 Lockdown Affected Gender In­equal­ity in Paid and Unpaid Work in Spain
    1. Do Class Size Re­duc­tions Protect Students from In­fec­tious Disease? Lessons for COVID-19 Policy from Flu Epidemic in Tokyo Met­ro­pol­i­tan Area
    1. EXCLUSIVE - PM fears UK second wave in TWO WEEKS: Boris Johnson is 'extremely concerned' after 28% rise in British Covid cases during July
    1. Between a Rock and a Hard Place: Poverty and COVID-19 in De­vel­op­ing Countries
    1. Should We Cheer Together? Gender Dif­fer­ences in In­stan­ta­neous Well-​Being during Joint and Solo Ac­tiv­i­ties
    1. Cognitive Per­for­mance in the Home Office – Evidence from Pro­fes­sional Chess
    1. Does the COVID-19 Pandemic Improve Global Air Quality? New Cross-​National Evidence on Its Un­in­tended Con­se­quences
    1. Does BMI Predict the Early Spatial Variation and Intensity of COVID-19 in De­vel­op­ing Countries? Evidence from India
    1. In­ter­gen­er­a­tional Residence Patterns and COVID-19 Fa­tal­i­ties in the EU and the US
    1. Sudden Stop: When Did Firms An­tic­i­pate the Potential Con­se­quences of COVID-19?
    1. Gender In­equal­ity in COVID-19 Times: Evidence from UK Prolific Par­tic­i­pants
    1. Initial Impact of the COVID-19 Pandemic on the Em­ploy­ment and Hours of Self-​Employed Coupled and Single Workers by Gender and Parental Status
    1. We study optimal dynamic lockdowns against Covid-19 within a commuting network. Our framework integrates canonical spatial epidemiology and trade models, and is applied to cities with varying initial viral spread: Seoul, Daegu and NYC-Metro. Spatial lockdowns achieve substantially smaller income losses than uniform lockdowns, and are not easily approximated by simple centrality-based rules. In NYM and Daegu—with large initial shocks—the optimal lockdown restricts inflows to central districts before gradual relaxation, while in Seoul it imposes low temporal but large spatial variation. Actual commuting responses were too weak in central locations in Daegu and NYM, and too strong across Seoul.
    2. Optimal Lockdown in a Commuting Network
    1. Employment Impacts of the COVID-19 Pandemic across Metropolitan Status and Size
    1. Exploring the Re­la­tion­ship between Care Homes and Excess Deaths in the COVID-19 Pandemic: Evidence from Italy
    1. COVID-19 and Mental Health De­te­ri­o­ra­tion among BAME Groups in the UK
    1. Reacting Quickly and Pro­tect­ing Jobs: The Short-​Term Impacts of the COVID-19 Lockdown on the Greek Labor Market
    1. Fathers Matter: Intra-​Household Re­spon­si­bil­i­ties and Children’s Wellbeing during the COVID-19 Lockdown in Italy
    1. Racial and Ethnic Disparities in COVID-19: Evidence from Six Large Cities
    1. How Right-Leaning Media Coverage of COVID-19 Facilitated the Spread of Misinformation in the Early Stages of the Pandemic
    1. Out of the lockdown: democratic trust in the management of epidemic crises
    1. Job Satisfaction and Work-Life Balance: Differences between Homework and Work at the Workplace of the Company
    1. An exploratory survey on the perceived risk of COVID-19 and travelling
    1. Fake news in the time of environmental disaster: Preparing framework for COVID-19
    1. As the COVID-19 outbreak progresses, increasing numbers of researchers are examining how an array of factors either hurt or help the spread of the disease. Unfortunately, the majority of available data, primarily confirmed cases of COVID-19, are widely known to be biased indicators of the spread of the disease. In this paper we present a retrospective Bayesian model that is much simpler than epidemiological models of disease progression but is still able to identify the effect of covariates on the historical infection rate. The model is validated by comparing our estimation of the count of infected to projections from expert surveys and extant disease forecasts. To apply the model, we show that as of April 10th, there are approximately 2 million infected people in the United States, and these people are increasingly concentrated in states with less wealth, better air quality, fewer smokers, fewer people under the age of 18, less public health funding and more cardiovascular deaths. On the other hand, the percentage of foreign born residents and the proportion of people who voted for President Trump in 2016 are not clear predictors of COVID-19 trends.
    2. A Retrospective Bayesian Model for Measuring Covariate Effects on Observed COVID-19 Test and Case Counts