Modeling, state estimation, and optimal control for the US COVID-19 outbreak.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
01 07 2020
01 07 2020
Historique:
received:
18
04
2020
accepted:
03
06
2020
entrez:
3
7
2020
pubmed:
3
7
2020
medline:
16
7
2020
Statut:
epublish
Résumé
The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that "on-off" policies alternating between strict social distancing and relaxing such restrictions can be effective at "flattening" the curve while likely minimizing social and economic cost.
Identifiants
pubmed: 32612204
doi: 10.1038/s41598-020-67459-8
pii: 10.1038/s41598-020-67459-8
pmc: PMC7329889
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
10711Subventions
Organisme : National Science Foundation
ID : CAREER Award 1454433
Pays : International
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