Delay in the Effect of Restricting Community Mobility on the Spread of COVID-19 during the First Wave in the United States

Shan He, Jooyoung Lee, Benjamin Langworthy, Junyi Xin, Peter James, Yang Yang, Molin Wang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background: It remains unclear how changes in human mobility shaped the transmission dynamic of coronavirus disease 2019 (COVID-19) during its first wave in the United States. Methods: By coupling a Bayesian hierarchical spatiotemporal model with reported case data and Google mobility data at the county level, we found that changes in movement were associated with notable changes in reported COVID-19 incidence rates about 5 to 7 weeks later. Results: Among all movement types, residential stay was the most influential driver of COVID-19 incidence rate, with a 10% increase 7 weeks ago reducing the disease incidence rate by 13% (95% credible interval, 6%-20%). A 10% increase in movement from home to workplaces, retail and recreation stores, public transit, grocery stores, and pharmacies 7 weeks ago was associated with an increase of 5%-8% in the COVID-10 incidence rate. In contrast, parks-related movement showed minimal impact. Conclusions: Policy-makers should anticipate such a delay when planning intervention strategies restricting human movement.

Original languageEnglish (US)
Article numberofab586
JournalOpen Forum Infectious Diseases
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

Keywords

  • community mobility
  • COVID-19
  • infectious diseases
  • spatiooral models
  • statistical modeling

PubMed: MeSH publication types

  • Journal Article

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