Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions

We present modeling of the COVID-19 epidemic in Illinois, USA, capturing the implementation of a stay-at-home order and scenarios for its eventual release. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. Bay...

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Autores principales:	George N. Wong, Zachary J. Weiner, Alexei V. Tkachenko, Ahmed Elbanna, Sergei Maslov, Nigel Goldenfeld
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2020
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/d288f78cca974191a0df1aebb80651c5
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spelling	oai:doaj.org-article:d288f78cca974191a0df1aebb80651c52021-12-02T12:42:30ZModeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions10.1103/PhysRevX.10.0410332160-3308https://doaj.org/article/d288f78cca974191a0df1aebb80651c52020-11-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.10.041033http://doi.org/10.1103/PhysRevX.10.041033https://doaj.org/toc/2160-3308We present modeling of the COVID-19 epidemic in Illinois, USA, capturing the implementation of a stay-at-home order and scenarios for its eventual release. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. Bayesian estimation of model parameters is carried out using Markov chain Monte Carlo methods. This framework allows us to treat all available input information, including both the previously published parameters of the epidemic and available local data, in a uniform manner. To accurately model deaths as well as demand on the healthcare system, we calibrate our predictions to total and in-hospital deaths as well as hospital and ICU bed occupancy by COVID-19 patients. We apply this model not only to the state as a whole but also its subregions in order to account for the wide disparities in population size and density. Without prior information on nonpharmaceutical interventions, the model independently reproduces a mitigation trend closely matching mobility data reported by Google and Unacast. Forward predictions of the model provide robust estimates of the peak position and severity and also enable forecasting the regional-dependent results of releasing stay-at-home orders. The resulting highly constrained narrative of the epidemic is able to provide estimates of its unseen progression and inform scenarios for sustainable monitoring and control of the epidemic.George N. WongZachary J. WeinerAlexei V. TkachenkoAhmed ElbannaSergei MaslovNigel GoldenfeldAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 10, Iss 4, p 041033 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 George N. Wong Zachary J. Weiner Alexei V. Tkachenko Ahmed Elbanna Sergei Maslov Nigel Goldenfeld Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions
description	We present modeling of the COVID-19 epidemic in Illinois, USA, capturing the implementation of a stay-at-home order and scenarios for its eventual release. We use a non-Markovian age-of-infection model that is capable of handling long and variable time delays without changing its model topology. Bayesian estimation of model parameters is carried out using Markov chain Monte Carlo methods. This framework allows us to treat all available input information, including both the previously published parameters of the epidemic and available local data, in a uniform manner. To accurately model deaths as well as demand on the healthcare system, we calibrate our predictions to total and in-hospital deaths as well as hospital and ICU bed occupancy by COVID-19 patients. We apply this model not only to the state as a whole but also its subregions in order to account for the wide disparities in population size and density. Without prior information on nonpharmaceutical interventions, the model independently reproduces a mitigation trend closely matching mobility data reported by Google and Unacast. Forward predictions of the model provide robust estimates of the peak position and severity and also enable forecasting the regional-dependent results of releasing stay-at-home orders. The resulting highly constrained narrative of the epidemic is able to provide estimates of its unseen progression and inform scenarios for sustainable monitoring and control of the epidemic.
format	article
author	George N. Wong Zachary J. Weiner Alexei V. Tkachenko Ahmed Elbanna Sergei Maslov Nigel Goldenfeld
author_facet	George N. Wong Zachary J. Weiner Alexei V. Tkachenko Ahmed Elbanna Sergei Maslov Nigel Goldenfeld
author_sort	George N. Wong
title	Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions
title_short	Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions
title_full	Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions
title_fullStr	Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions
title_full_unstemmed	Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions
title_sort	modeling covid-19 dynamics in illinois under nonpharmaceutical interventions
publisher	American Physical Society
publishDate	2020
url	https://doaj.org/article/d288f78cca974191a0df1aebb80651c5
work_keys_str_mv	AT georgenwong modelingcovid19dynamicsinillinoisundernonpharmaceuticalinterventions AT zacharyjweiner modelingcovid19dynamicsinillinoisundernonpharmaceuticalinterventions AT alexeivtkachenko modelingcovid19dynamicsinillinoisundernonpharmaceuticalinterventions AT ahmedelbanna modelingcovid19dynamicsinillinoisundernonpharmaceuticalinterventions AT sergeimaslov modelingcovid19dynamicsinillinoisundernonpharmaceuticalinterventions AT nigelgoldenfeld modelingcovid19dynamicsinillinoisundernonpharmaceuticalinterventions
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Modeling COVID-19 Dynamics in Illinois under Nonpharmaceutical Interventions

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