Matan Yechezkel1, Amit Weiss1, Idan Rejwan1, Edan Shahmoon1, Shachaf Ben-Gal1, Dan Yamin2,3. 1. Laboratory for Epidemic Modeling and Analysis, Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University, 6997801, Tel Aviv, Israel. 2. Laboratory for Epidemic Modeling and Analysis, Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University, 6997801, Tel Aviv, Israel. dan.yamin@gmail.com. 3. Center for Combatting Pandemics, Tel Aviv University, 6997801, Tel Aviv, Israel. dan.yamin@gmail.com.
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.
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-19mortality, 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.
Entities:
Keywords:
COVID-19; Contact mixing patterns; Human mobility; SIR model; Transmission model
Authors: Flavio Finger; Tina Genolet; Lorenzo Mari; Guillaume Constantin de Magny; Noël Magloire Manga; Andrea Rinaldo; Enrico Bertuzzo Journal: Proc Natl Acad Sci U S A Date: 2016-05-23 Impact factor: 11.205
Authors: Temet M McMichael; Dustin W Currie; Shauna Clark; Sargis Pogosjans; Meagan Kay; Noah G Schwartz; James Lewis; Atar Baer; Vance Kawakami; Margaret D Lukoff; Jessica Ferro; Claire Brostrom-Smith; Thomas D Rea; Michael R Sayre; Francis X Riedo; Denny Russell; Brian Hiatt; Patricia Montgomery; Agam K Rao; Eric J Chow; Farrell Tobolowsky; Michael J Hughes; Ana C Bardossy; Lisa P Oakley; Jesica R Jacobs; Nimalie D Stone; Sujan C Reddy; John A Jernigan; Margaret A Honein; Thomas A Clark; Jeffrey S Duchin Journal: N Engl J Med Date: 2020-03-27 Impact factor: 91.245
Authors: Kyra H Grantz; Hannah R Meredith; Derek A T Cummings; C Jessica E Metcalf; Bryan T Grenfell; John R Giles; Shruti Mehta; Sunil Solomon; Alain Labrique; Nishant Kishore; Caroline O Buckee; Amy Wesolowski Journal: Nat Commun Date: 2020-09-30 Impact factor: 14.919
Authors: Jessica Frankeberger; Natalie Sumetsky; M Reuel Friedman; Jessica G Burke; Robert W S Coulter; Christina Mair Journal: Wellbeing Space Soc Date: 2022-07-14