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COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.
Shea, Katriona; Borchering, Rebecca K; Probert, William J M; Howerton, Emily; Bogich, Tiffany L; Li, Shouli; van Panhuis, Willem G; Viboud, Cecile; Aguás, Ricardo; Belov, Artur; Bhargava, Sanjana H; Cavany, Sean; Chang, Joshua C; Chen, Cynthia; Chen, Jinghui; Chen, Shi; Chen, YangQuan; Childs, Lauren M; Chow, Carson C; Crooker, Isabel; Valle, Sara Y Del; España, Guido; Fairchild, Geoffrey; Gerkin, Richard C; Germann, Timothy C; Gu, Quanquan; Guan, Xiangyang; Guo, Lihong; Hart, Gregory R; Hladish, Thomas J; Hupert, Nathaniel; Janies, Daniel; Kerr, Cliff C; Klein, Daniel J; Klein, Eili; Lin, Gary; Manore, Carrie; Meyers, Lauren Ancel; Mittler, John; Mu, Kunpeng; Núñez, Rafael C; Oidtman, Rachel; Pasco, Remy; Piontti, Ana Pastore Y; Paul, Rajib; Pearson, Carl A B; Perdomo, Dianela R; Perkins, T Alex; Pierce, Kelly; Pillai, Alexander N.
Afiliación
  • Shea K; Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.
  • Borchering RK; Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.
  • Probert WJM; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Howerton E; Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.
  • Bogich TL; Department of Biology and Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.
  • Li S; State Key Laboratory of Grassland Agro-ecosystems, Center for Grassland Microbiome, and College of Pastoral, Agriculture Science and Technology, Lanzhou University, Lanzhou, People's Republic of China.
  • van Panhuis WG; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh.
  • Viboud C; Fogarty International Center, NIH, Bethesda, MD, USA.
  • Aguás R; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Belov A; Office of Biostatistics & Epidemiology, FDA - Center for Biologics Evaluation and Research, Silver Spring, MD, USA.
  • Bhargava SH; Department of Biology, University of Florida, Gainesville, FL, USA.
  • Cavany S; Department of Biological Sciences, University of Notre Dame.
  • Chang JC; Epidemiology and Biostatistics Section, Rehabilitation Medicine, Clinical Center, National Institutes of Health.
  • Chen C; Mederrata.
  • Chen J; Department of Civil & Environmental Engineering, University of Washington.
  • Chen S; Department of Computer Science, University of California, Los Angeles, CA, USA.
  • Chen Y; Department of Public Health Sciences, University of North Carolina at Charlotte.
  • Childs LM; School of Data Science, University of North Carolina at Charlotte.
  • Chow CC; Mechatronics, Embedded Systems and Automation Laboratory, Dept. of Engineering University of California, Merced, CA, USA.
  • Crooker I; Department of Mathematics, Virginia Tech.
  • Valle SYD; Mathematical Biology Section, LBM, NIDDK, National Institutes of Health.
  • España G; Los Alamos National Laboratory.
  • Fairchild G; Los Alamos National Laboratory.
  • Gerkin RC; Department of Biological Sciences, University of Notre Dame.
  • Germann TC; Los Alamos National Laboratory.
  • Gu Q; School of Life Sciences, Arizona State University.
  • Guan X; Los Alamos National Laboratory.
  • Guo L; Department of Computer Science, University of California, Los Angeles, CA, USA.
  • Hart GR; Department of Civil & Environmental Engineering, University of Washington.
  • Hladish TJ; Institute of Mathematics, Jilin University, Changchun 130012, P. R. China.
  • Hupert N; Institute for Disease Modeling.
  • Janies D; Department of Biology, University of Florida, Gainesville, FL, USA.
  • Kerr CC; Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
  • Klein DJ; Weill Cornell Medicine, Cornell University, USA.
  • Klein E; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte.
  • Lin G; Institute for Disease Modeling.
  • Manore C; Institute for Disease Modeling.
  • Meyers LA; Department of Emergency Medicine, Johns Hopkins University.
  • Mittler J; Center for Disease Dynamics, Economics, & Policy, Johns Hopkins University.
  • Mu K; Department of Emergency Medicine, Johns Hopkins University.
  • Núñez RC; Los Alamos National Laboratory.
  • Oidtman R; Department of Integrative Biology, The University of Texas at Austin.
  • Pasco R; Department of Microbiology, University of Washington.
  • Piontti APY; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
  • Paul R; Institute for Disease Modeling.
  • Pearson CAB; Department of Biological Sciences, University of Notre Dame.
  • Perdomo DR; Operations Research, The University of Texas at Austin.
  • Perkins TA; Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
  • Pierce K; Department of Public Health Sciences, University of North Carolina at Charlotte.
  • Pillai AN; Department of Infectious Disease Epidemiology & Centre Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
medRxiv ; 2020 Nov 05.
Article en En | MEDLINE | ID: mdl-33173914
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.

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: MedRxiv Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: MedRxiv Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos