Which COVID policies are most effective? A Bayesian analysis of COVID-19 by jurisdiction

This paper reports the results of a Bayesian analysis on large-scale empirical data to assess the effectiveness of eleven types of COVID-control policies that have been implemented at various levels of intensity in 40 countries and U.S. states since the onset of the pandemic. The analysis estimates the marginal impact of each type and level of policy as implemented in concert with other policies. The purpose is to provide policymakers and the general public with an estimate of the relative effectiveness of various COVID-control strategies. We find that a set of widely implemented core policies reduces the spread of virus but not by enough to contain the pandemic except in a few highly compliant jurisdictions. The core policies include the cancellation of public events, restriction of gatherings to fewer than 100 people, recommendation to stay at home, recommended restrictions on internal movement, implementation of a partial international travel ban, and coordination of information campaigns. For the median jurisdiction, these policies reduce growth rate in new infections from an estimated 270% per week to approximately 49% per week, but this impact is insufficient to prevent eventual transmission throughout the population because containment occurs only when a jurisdiction reduces growth in COVID infection to below zero. Most jurisdictions must also implement additional policies, each of which has the potential to reduce weekly COVID growth rate by 10 percentage points or more. The slate of these additional high-impact policies includes targeted or full workplace closings for all but essential workers, stay-at-home requirements, and targeted school closures.

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Which COVID policies are most effective? A Bayesian analysis of COVID-19 by jurisdiction

10.1101/2020.12.01.20241695 ◽

2020 ◽

Author(s):

Phebo Wibbens ◽

Wesley Wu-Yi Koo ◽

Anita M. McGahan

Keyword(s):

Growth Rate ◽

Bayesian Analysis ◽

Large Scale ◽

Control Strategies ◽

Relative Effectiveness ◽

International Travel ◽

School Closures ◽

Information Campaigns ◽

Public Events ◽

At Home

AbstractThis paper reports the results of a Bayesian analysis on large-scale empirical data to assess the effectiveness of eleven types of COVID-control policies that have been implemented at various levels of intensity in 40 countries and U.S. states since the onset of the pandemic. The analysis estimates the marginal impact of each type and level of policy as implemented in concert with other policies. The purpose is to provide policymakers and the general public with an estimate of the relative effectiveness of various COVID-control strategies. We find that a set of widely implemented core policies reduces the spread of virus but not by enough to contain the pandemic except in a few highly compliant jurisdictions. The core policies include the cancellation of public events, restriction of gatherings to fewer than 100 people, recommendation to stay at home, recommended restrictions on internal movement, implementation of a partial international travel ban, and coordination of information campaigns. For the median jurisdiction, these policies reduce growth rate in new infections from an estimated 270% per week to approximately 49% per week, but this impact is insufficient to prevent eventual transmission throughout the population because containment occurs only when a jurisdiction reduces growth in COVID infection to below zero. Most jurisdictions must also implement additional policies, each of which has the potential to reduce weekly COVID growth rate by 10 percentage points or more. The slate of these additional high-impact policies includes targeted or full workplace closings for all but essential workers, stay-at-home requirements, and targeted school closures.

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The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18030996 ◽

2021 ◽

Vol 18 (3) ◽

pp. 996

Author(s):

Yun Li ◽

Moming Li ◽

Megan Rice ◽

Haoyuan Zhang ◽

Dexuan Sha ◽

...

Keyword(s):

Growth Rate ◽

Human Mobility ◽

Public Information ◽

Key Factors ◽

Information Campaigns ◽

Rapid Spread ◽

The Us ◽

Policy Measures ◽

The Impact ◽

At Home

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

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Variational-LSTM Autoencoder to forecast the spread of coronavirus across the globe

10.1101/2020.04.20.20070938 ◽

2020 ◽

Cited By ~ 5

Author(s):

Mohamed R. Ibrahim ◽

James Haworth ◽

Aldo Lipani ◽

Nilufer Aslam ◽

Tao Cheng ◽

...

Keyword(s):

Demographic Data ◽

Public Information ◽

International Travel ◽

Contact Tracing ◽

Information Campaigns ◽

School Closing ◽

Public Events ◽

Short And Long Term ◽

Spatio Temporal

AbstractModelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational LSTM-Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represent the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

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Where There's a Will There's a Way

10.4018/978-1-7998-5200-1.ch013 ◽

2022 ◽

pp. 271-295

Author(s):

Gordon A. Crews ◽

Garrison Allen Crews ◽

Samantha Leigh Crews

Keyword(s):

School Closures ◽

Mass Violence ◽

Public Events ◽

The Us ◽

At Home

“Where there's a will there's a way” is a proverb that simply means if someone is determined to do something, he or she will find a way to accomplish it regardless of obstacles. Unfortunately, this is very true for those who wish to commit acts of violence wish to commit acts of violence. The purpose of this chapter is to examine the possible impacts of the COVID-19 pandemic on incidents of mass and multiple victim violence in the US. More specifically, what impact did efforts such as stay-at-home/shelter-in-place orders, telecommuting options for workers, school closures, cancellation of large public events, and the suspension of non-essential travel have upon the extent and characteristics of mass violence.

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Remote Learning, COVID-19, and Children With Disabilities

AERA Open ◽

10.1177/23328584211058471 ◽

2021 ◽

Vol 7 ◽

pp. 233285842110584

Author(s):

Kate Henley Averett

Keyword(s):

Public Education ◽

Large Scale ◽

Children With Disabilities ◽

Interview Study ◽

School Closures ◽

Learning At Home ◽

K 12 ◽

Remote Learning ◽

At Home

While the COVID-19 pandemic affected the education of nearly all schoolchildren worldwide, pandemic-related school closures did not affect all children in equal ways. Between March and August, 2020, I interviewed 31 parents of children with disabilities as part of a larger interview study of U.S. parents of children in grades K–12. In this article, I analyze these parents’ narratives about their families’ experiences of pandemic-related remote learning to identify the particular challenges children with disabilities and their families faced with remote learning. I find that most, but not all, families struggled with remote learning, both when children’s specific needs while learning at home differed from their needs at school, and when schools failed to provide adequate accommodations and services remotely. These narratives demonstrate how children with disabilities are particularly vulnerable to the type of large-scale systemic shock to U.S. public education that the pandemic has presented.

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Effects of worldwide interventions and vaccination on COVID-19 between waves and countries

10.21203/rs.3.rs-396989/v1 ◽

2021 ◽

Author(s):

Yong Ge ◽

Wenbin Zhang ◽

Haiyan Liu ◽

Corrine W Ruktanonchai ◽

Maogui Hu ◽

...

Keyword(s):

Infectious Diseases ◽

Large Scale ◽

International Travel ◽

Spatiotemporal Variations ◽

School Closures ◽

Vaccination Strategies ◽

Travel Restrictions ◽

The Impact ◽

Second Wave ◽

Pharmaceutical Interventions

Abstract Worldwide governments have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic, together with the large-scale rollout of vaccines since late 2020. However, the effect of these individual NPI and vaccination measures across space and time has not been sufficiently explored. By the decay ratio in the suppression of COVID-19 infections, we investigated the performance of different NPIs across waves in 133 countries, and their integration with vaccine rollouts in 63 countries as of 25 March 2021. The most effective NPIs were gathering restrictions (contributing 27.83% in the infection rate reductions), facial coverings (16.79%) and school closures (10.08%) in the first wave, and changed to facial coverings (30.04%), gathering restrictions (17.51%) and international travel restrictions (9.22%) in the second wave. The impact of NPIs had obvious spatiotemporal variations across countries by waves before vaccine rollouts, with facial coverings being one of the most effective measures consistently. Vaccinations had gradually contributed to the suppression of COVID-19 transmission, from 0.71% and 0.86% within 15 days and 30 days since Day 12 after vaccination, to 1.23% as of 25 March 2021, while NPIs still dominated the pandemic mitigation. Our findings have important implications for continued tailoring of integrated NPI or NPI-vaccination strategies against future COVID-19 waves or similar infectious diseases.

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Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe

PLoS ONE ◽

10.1371/journal.pone.0246120 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0246120

Author(s):

Mohamed R. Ibrahim ◽

James Haworth ◽

Aldo Lipani ◽

Nilufer Aslam ◽

Tao Cheng ◽

...

Keyword(s):

Demographic Data ◽

Public Information ◽

International Travel ◽

Contact Tracing ◽

Information Campaigns ◽

School Closing ◽

Public Events ◽

Short And Long Term ◽

Spatio Temporal

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

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The health policy response to COVID-19 in Malawi

BMJ Global Health ◽

10.1136/bmjgh-2021-006035 ◽

2021 ◽

Vol 6 (5) ◽

pp. e006035

Author(s):

Grace W Mzumara ◽

Marlen Chawani ◽

Melody Sakala ◽

Lily Mwandira ◽

Elias Phiri ◽

...

Keyword(s):

Central Committee ◽

International Travel ◽

School Closures ◽

Public Events ◽

University Of Oxford ◽

Head Of State ◽

Social Cash Transfers ◽

Policy Direction ◽

The University ◽

National Disaster

Malawi declared a state of national disaster due to the COVID-19 pandemic on 20th March 2020 and registered its first confirmed coronavirus case on the 2 April 2020. The aim of this paper was to document policy decisions made in response to the COVID-19 pandemic from January to August 2020. We reviewed policy documents from the Public Health Institute of Malawi, the Malawi Gazette, the Malawi Ministry of Health and Population and the University of Oxford Coronavirus Government Response Tracker. We found that the Malawi response to the COVID-19 pandemic was multisectoral and implemented through 15 focused working groups termed clusters. Each cluster was charged with providing policy direction in their own area of focus. All clusters then fed into one central committee for major decisions and reporting to head of state. Key policies identified during the review include international travel ban, school closures at all levels, cancellation of public events, decongesting workplaces and public transport, and mandatory face coverings and a testing policy covering symptomatic people. Supportive interventions included risk communication and community engagement in multiple languages and over a variety of mediums, efforts to improve access to water, sanitation, nutrition and unconditional social-cash transfers for poor urban and rural households.

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Policy and Environmental Predictors of Park Visits During the First Months of the COVID-19 Pandemic: Getting Out While Staying in

Environment and Behavior ◽

10.1177/00139165211031199 ◽

2021 ◽

pp. 001391652110311

Author(s):

David S. Curtis ◽

Alessandro Rigolon ◽

Dorothy L. Schmalz ◽

Barbara B. Brown

Keyword(s):

Growth Rate ◽

Human Health ◽

Cell Phone ◽

Environmental Predictors ◽

School Closures ◽

Park Use ◽

Location Data ◽

At Home ◽

Recreation And Leisure

The COVID-19 pandemic may have altered visitation patterns to parks, with potential effects on human health. Little is known about park use early in the pandemic, how park availability influenced use, and whether park visits accelerated COVID-19 spread. Using weekly cell phone location data for 620 U.S. counties, we show park visits decreased by an average 26% between March 15 and May 9, 2020. Net of weekly trends, park visits were 2.2% lower when stay-at-home orders were in effect, yet increased by 8.4% with school closures and 4.4% with business closures. Park visits decreased less during the pandemic in counties where park availability was high. Levels of park visits were not associated with COVID-19 growth rate or incidence in the following weeks. Thus, parks served as recreation and leisure outlets when schools and businesses closed, especially where parks were more available, with no evidence of park use increasing COVID-19 spread.

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Social distancing compliance: A video observational analysis

PLoS ONE ◽

10.1371/journal.pone.0248221 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0248221

Author(s):

Evelien M. Hoeben ◽

Wim Bernasco ◽

Lasse Suonperä Liebst ◽

Carlijn van Baak ◽

Marie Rosenkrantz Lindegaard

Keyword(s):

Public Space ◽

Large Scale ◽

Control Strategies ◽

Physical Distance ◽

Video Observation ◽

Social Distancing ◽

Location Data ◽

Crowd Control ◽

Google Search ◽

At Home

Purpose Virus epidemics may be mitigated if people comply with directives to stay at home and keep their distance from strangers in public. As such, there is a public health interest in social distancing compliance. The available evidence on distancing practices in public space is limited, however, by the lack of observational data. Here, we apply video observation as a method to examine to what extent members of the public comply with social distancing directives. Data Closed Circuit Television (CCTV) footage of interactions in public was collected in inner-city Amsterdam, the Netherlands. From the footage, we observed instances of people violating the 1.5-meter distance directives in the weeks before, during, and after these directives were introduced to mitigate the COVID-19 pandemic. Results We find that people complied with the 1.5-meter distance directives when these directives were first introduced, but that the level of compliance started to decline soon after. We also find that violation of the 1.5-meter distance directives is strongly associated with the number of people observed on the street and with non-compliance to stay-at-home directives, operationalized with large-scale aggregated location data from cell phones. All three measures correlate to a varying extent with temporal patterns in the transmission of the COVID-19 virus, temperature, COVID-19 related Google search queries, and media attention to the topic. Conclusion Compliance with 1.5 meter distance directives is short-lived and coincides with the number of people on the street and with compliance to stay-at-home directives. Potential implications of these findings are that keep- distance directives may work best in combination with stay-at-home directives and place-specific crowd-control strategies, and that the number of people on the street and community-wide mobility as captured with cell phone data offer easily measurable proxies for the extent to which people keep sufficient physical distance from others at specific times and locations.

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