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BMC Public Health
. 2021 Mar 25;21(1):596.
doi: 10.1186/s12889-021-10561-x.
Human mobility and poverty as key drivers of COVID-19 transmission and control
Matan Yechezkel # 1 , Amit Weiss # 1 , Idan Rejwan # 1 , Edan Shahmoon 1 , Shachaf Ben-Gal 1 , Dan Yamin 2 3
Affiliations
- PMID: 33765977
- DOI: 10.1186/s12889-021-10561-x
Abstract
Background: Applying heavy nationwide restrictions is a powerful method to curtail COVID-19 transmission but poses a significant humanitarian and economic crisis. Thus, it is essential to improve our understanding of COVID-19 transmission, and develop more focused and effective strategies. As human mobility drives transmission, data from cellphone devices can be utilized to achieve these goals.
Methods: We analyzed aggregated and anonymized mobility data from the cell phone devices of> 3 million users between February 1, 2020, to May 16, 2020 - in which several movement restrictions were applied and lifted in Israel. We integrated these mobility patterns into age-, risk- and region-structured transmission model. Calibrated to coronavirus incidence in 250 regions covering Israel, we evaluated the efficacy and effectiveness in decreasing morbidity and mortality of applying localized and temporal lockdowns (stay-at-home order).
Results: Poorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from impoverished areas were associated with high transmission rates. Considering a horizon of 1-3 years, we found that to reduce COVID-19 mortality, school closure has an adverse effect, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduces the overall mortality and morbidity compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, and potential seasonal forcing.
Conclusions: More resources should be devoted to helping impoverished regions. Utilizing cellphone data despite being anonymized and aggregated can help policymakers worldwide identify hotspots and apply designated strategies against future COVID-19 outbreaks.
Keywords: COVID-19; Contact mixing patterns; Human mobility; SIR model; Transmission model.