Mapping county-level mobility pattern changes in the United States in response to COVID-19

Song Gao; Jinmeng Rao; Yuhao Kang; Yunlei Liang; Jake Kruse

Mapping county-level mobility pattern changes in the United States in response to COVID-19

Physics and Society 2020-05-19 v2 Social and Information Networks Populations and Evolution

Authors: Song Gao , Jinmeng Rao , Yuhao Kang , Yunlei Liang , Jake Kruse

Abstract

To contain the Coronavirus disease (COVID-19) pandemic, one of the non-pharmacological epidemic control measures in response to the COVID-19 outbreak is reducing the transmission rate of SARS-COV-2 in the population through (physical) social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancing guidelines was developed with the support of the National Science Foundation (NSF). It integrates geographic information systems (GIS) and daily updated human mobility statistical patterns derived from large-scale anonymized and aggregated smartphone location big data at the county-level in the United States, and aims to increase risk awareness of the public, support governmental decision-making, and help enhance community responses to the COVID-19 outbreak.

Keywords

epidemic modeling and pandemic dynamics

Cite

@article{arxiv.2004.04544,
  title  = {Mapping county-level mobility pattern changes in the United States in response to COVID-19},
  author = {Song Gao and Jinmeng Rao and Yuhao Kang and Yunlei Liang and Jake Kruse},
  journal= {arXiv preprint arXiv:2004.04544},
  year   = {2020}
}

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11 pages, 10 figures

Mapping county-level mobility pattern changes in the United States in response to COVID-19

Abstract

Keywords

Cite

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