Mobility network models of COVID-19 explain inequities and inform reopening

Chang, Serina; Pierson, Emma; Koh, Pang Wei; Gerardin, Jaline; Redbird, Beth; Grusky, David B.; Leskovec, Jure

doi:10.1038/s41586-020-2923-3

Cited by 1,358 publications

(1,463 citation statements)

References 53 publications

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“…Exposures at nonessential indoor settings and crowded outdoor settings pose a preventable risk to all participants. ** , † † Indoor venues, where distancing is not maintained and consistent use of face masks is not possible (e.g., restaurant dining), have been identified as particularly high-risk scenarios (7,10). Crowded events in outdoor settings have also been linked to spread of SARS-CoV-2, although it can be difficult to isolate the impact of crowded outdoor events from related indoor social interactions (11).…”

Section: Recommended Public Health Strategiesmentioning

confidence: 99%

Summary of Guidance for Public Health Strategies to Address High Levels of Community Transmission of SARS-CoV-2 and Related Deaths, December 2020

Honein

Christie

Rose

et al. 2020

MMWR Morb. Mortal. Wkly. Rep.

231

203

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Section: Recommended Public Health Strategiesmentioning

confidence: 99%

Summary of Guidance for Public Health Strategies to Address High Levels of Community Transmission of SARS-CoV-2 and Related Deaths, December 2020

Honein

Christie

Rose

et al. 2020

MMWR Morb. Mortal. Wkly. Rep.

231

203

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“…Government appropriations are a crucial component in reducing case fatalities in the USA [4,8]. The research that focused on analysing the impact of such policies on the spread of COVID-19 generally uses epidemic models, such as the susceptible-infectious-recovered (SIR) model and its extensions [9][10][11]. By assuming a universal magnitude of mobility reduction, the impact of different lockdown or travel restriction policies can be quantified.…”

Section: Introductionmentioning

confidence: 99%

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

Xiong

Yang

et al. 2020

J. R. Soc. Interface.

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One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.

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“…15 However, our ecological studies suggest that prevalence and mortality are more racially influenced than population density. The spread of SARS-CoV-2 may have been due to various additional sociodemographic factors, such as a multi-generational lifestyle, poor access to health care, inadequate hygiene measures due to poverty, crowded religious organizations and grocery stores, 41 and inability to reduce mobility 41 due to occupations that defy the shift to remote work. It is unclear why CFR and mortality were lower in states with higher proportions of non-Hispanic American Indians, but it may be due to interference from other infections or to lifestyle differences.…”

Section: Discussionmentioning

confidence: 99%

Epidemic trends of SARS-CoV-2 associated with immunity, race, and viral mutations

Takahashi

Kamikubo

Hattori

2020

Preprint

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The world has been plagued by complex waves of SARS-CoV-2 epidemics that vary from region to region, leaving the end of the pandemic unpredictable. Here we performed "genetic fingerprinting" to compare the local viral genotypes with epidemiological information and reveal the molecular dynamics of the SARS-CoV-2 epidemic worldwide. A multifaceted analyses of the epidemic trends and their relationship to virus genotypes, regional herd immunity, population density, and race has shown that epidemic outcomes are affected by: (1) Increased fitness of the virus due to mutations of viral proteins; (2) Immunity against previously prevalent subtypes that prevents or exacerbates COVID-19; (3) Immune evasion due to viral mutations; (4) Viral competition with coexisting subtypes; (5) Dense and crowded living environment; (6) Racial and social disparities; (7) Upper limit of viral mutations that enable natural selection. These findings provide an overview of the current epidemic, help predict the future, and develop effective countermeasures.

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Mobility network models of COVID-19 explain inequities and inform reopening

Cited by 1,358 publications

References 53 publications

Summary of Guidance for Public Health Strategies to Address High Levels of Community Transmission of SARS-CoV-2 and Related Deaths, December 2020

Summary of Guidance for Public Health Strategies to Address High Levels of Community Transmission of SARS-CoV-2 and Related Deaths, December 2020

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

Epidemic trends of SARS-CoV-2 associated with immunity, race, and viral mutations

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