Assess the impacts of human mobility change on COVID-19 dynamics in Arizona, U.S.: a modeling study incorporating Google Community Mobility Reports

TL;DR

This study models how changes in human mobility, influenced by policy measures, significantly impact COVID-19 spread in Arizona, highlighting the importance of timely mobility restrictions to control the epidemic.

Contribution

It introduces a mobility integrated metapopulation model fitted to real data, demonstrating the effects of mobility reduction on epidemic peak delay and size in Arizona.

Findings

Reducing mobility delays epidemic peak.

Mobility reduction decreases total cases.

Timely restrictions are crucial for epidemic control.

Abstract

In June 2020, Arizona, U.S., emerged as one of the world's worst coronavirus disease 2019(COVID-19) spots after the stay-at-home order was lifted in the middle of May. However, with the decisions to reimpose restrictions, the number of COVID-19 cases has been declining, and Arizona is considered to be a good model in slowing the epidemic. In this paper, we aimed to examine the COVID-19 situation in Arizona and assess the impact of human mobility change. We constructed the mobility integrated metapopulation susceptible-infectious-removed model and fitted to publicly available datasets on COVID-19 cases and mobility changes in Arizona. Our simulations showed that by reducing human mobility, the peak time was delayed, and the final size of the epidemic was decreased in all three regions. Our analysis suggests that rapid and effective decision making is crucial to control human mobility and, therefore, COVID-19 epidemics. Until a vaccine is available, reimplementations of mobility restrictions in response to the increase of new COVID-19 cases might need to be considered in Arizona and beyond.