About: Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions
Creator	Maslov, Sergei Goldenfeld, Nigel Wong, George Weiner, Zachary Elbanna, Ahmed Tkachenko, Alexei
source	MedRxiv
abstract	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 (MCMC) 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 sub-regions in order to account for the wide disparities in population size and density. Without prior information on non-pharmaceutical interventions (NPIs), 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.
has issue date	2020-06-05(xsd:dateTime)
bibo:doi	10.1101/2020.06.03.20120691
has license	medrxiv
sha1sum (hex)	45fcb4737ff75beb595975c2c90bf8b1cf1cd009
schema:url	https://doi.org/10.1101/2020.06.03.20120691
resource representing a document's title	Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions
resource representing a document's body	covid:45fcb4737ff75beb595975c2c90bf8b1cf1cd009#body_text
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