Dataset Information

Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data.

ABSTRACT: Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high social activity levels. These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more heterogeneous strategies that are targeted based on the network effects in human mobility provide a better balance between pandemic control and regular social activities.

SUBMITTER: Wang H

PROVIDER: S-EPMC8034422 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data.

Wang Haotian H Ghosh Abhirup A Ding Jiaxin J Sarkar Rik R Gao Jie J

Scientific reports 20210408 1

Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus. Large scale lockdown of human movements are effective in reducing the spread, but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues. We find that interventions focused on these most mobile individuals an ...[more]

PMID: 33833298

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Project description:BackgroundOn January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries.ObjectiveThe first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space-time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide.MethodsWe created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population's mobility patterns at the country level were obtained from Google community mobility reports. The prospective space-time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used.ResultsWe found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread.ConclusionsThe prospective space-time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.

Dataset Information

Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data.

Publications

Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data.

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