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COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.

Authors :
Shea K
Borchering RK
Probert WJM
Howerton E
Bogich TL
Li S
van Panhuis WG
Viboud C
Aguás R
Belov A
Bhargava SH
Cavany S
Chang JC
Chen C
Chen J
Chen S
Chen Y
Childs LM
Chow CC
Crooker I
Valle SYD
España G
Fairchild G
Gerkin RC
Germann TC
Gu Q
Guan X
Guo L
Hart GR
Hladish TJ
Hupert N
Janies D
Kerr CC
Klein DJ
Klein E
Lin G
Manore C
Meyers LA
Mittler J
Mu K
Núñez RC
Oidtman R
Pasco R
Piontti APY
Paul R
Pearson CAB
Perdomo DR
Perkins TA
Pierce K
Pillai AN
Rael RC
Rosenfeld K
Ross CW
Spencer JA
Stoltzfus AB
Toh KB
Vattikuti S
Vespignani A
Wang L
White L
Xu P
Yang Y
Yogurtcu ON
Zhang W
Zhao Y
Zou D
Ferrari M
Pannell D
Tildesley M
Seifarth J
Johnson E
Biggerstaff M
Johansson M
Slayton RB
Levander J
Stazer J
Salerno J
Runge MC
Source :
MedRxiv : the preprint server for health sciences [medRxiv] 2020 Nov 05. Date of Electronic Publication: 2020 Nov 05.
Publication Year :
2020

Abstract

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Details

Language :
English
Database :
MEDLINE
Journal :
MedRxiv : the preprint server for health sciences
Accession number :
33173914
Full Text :
https://doi.org/10.1101/2020.11.03.20225409