tetano
Editor, Senior Moderator
Open Forum Infect Dis
. 2021 Nov 22;9(1)
fab586.
doi: 10.1093/ofid/ofab586. eCollection 2022 Jan.
Delay in the Effect of Restricting Community Mobility on the Spread of COVID-19 During the First Wave in the United States
Shan He[SUP] 1 2 [/SUP], Jooyoung Lee[SUP] 3 [/SUP], Benjamin Langworthy[SUP] 4 [/SUP], Junyi Xin[SUP] 4 [/SUP], Peter James[SUP] 5 6 [/SUP], Yang Yang[SUP] 7 [/SUP], Molin Wang[SUP] 1 4 8 [/SUP]
Affiliations
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.
Keywords: COVID-19; community mobility; infectious diseases; spatio-temporal models; statistical modeling.
. 2021 Nov 22;9(1)
fab586.doi: 10.1093/ofid/ofab586. eCollection 2022 Jan.
Delay in the Effect of Restricting Community Mobility on the Spread of COVID-19 During the First Wave in the United States
Shan He[SUP] 1 2 [/SUP], Jooyoung Lee[SUP] 3 [/SUP], Benjamin Langworthy[SUP] 4 [/SUP], Junyi Xin[SUP] 4 [/SUP], Peter James[SUP] 5 6 [/SUP], Yang Yang[SUP] 7 [/SUP], Molin Wang[SUP] 1 4 8 [/SUP]
Affiliations
- PMID: 34988255
- PMCID: PMC8714371
- DOI: 10.1093/ofid/ofab586
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.
Keywords: COVID-19; community mobility; infectious diseases; spatio-temporal models; statistical modeling.