Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic
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Supporting information
Supporting information
Author(s)
Dutta, R
Gomes, S
Kalise, D
Pacchiardi, L
Type
Journal Article
Abstract
A mathematical model for the COVID-19 pandemic spread, which integratesage-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with realmobile phone data accounting for the population mobility, is presented. The dynamicalmodel adjustment is performed via Approximate Bayesian Computation. Optimallockdown and exit strategies are determined based on nonlinear model predictivecontrol, constrained to public-health and socio-economic factors. Through an extensivecomputational validation of the methodology, it is shown that it is possible to computerobust exit strategies with realistic reduced mobility values to inform public policymaking, and we exemplify the applicability of the methodology using datasets fromEngland and France.
Date Issued
2021-08-12
Date Acceptance
2021-07-02
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS Computational Biology
Volume
17
Issue
8
Copyright Statement
© 2021 Dutta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
EPIDEMIC
Bayes Theorem
COVID-19
England
France
Humans
Pandemics
Quarantine
SARS-CoV-2
Smartphone
Travel
Humans
Bayes Theorem
Quarantine
Travel
France
England
Pandemics
Smartphone
COVID-19
SARS-CoV-2
stat.AP
stat.AP
physics.soc-ph
q-bio.PE
Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status
Published
OA Location
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009236
Article Number
ARTN e1009236