About: Predicting the second wave of COVID-19 in Washtenaw County, MI

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About: Predicting the second wave of COVID-19 in Washtenaw County, MI Goto Sponge NotDistinct Permalink

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
has title	Predicting the second wave of COVID-19 in Washtenaw County, MI
Creator	Kirschner, Denise Renardy, Marissa
Source	MedRxiv; Medline; PMC
abstract	The COVID-19 pandemic has highlighted the patchwork nature of disease epidemics, with infection spread dynamics varying wildly across countries and across states within the US. These heterogeneous patterns are also observed within individual states, with patches of concentrated outbreaks. Data is being generated daily at all of these spatial scales, and answers to questions regarded reopening strategies are desperately needed. Mathematical modeling is useful in exactly these cases, and using modeling at a county scale may be valuable to further predict disease dynamics for the purposes of public health interventions. To explore this issue, we study and predict the spread of COVID-19 in Washtenaw County, MI, the home to University of Michigan, Eastern Michigan University, and Google, as well as serving as a sister city to Detroit, MI where there has been a serious outbreak. Here, we apply a discrete and stochastic network-based modeling framework allowing us to track every individual in the county. In this framework, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households, workplaces, schools, and group quarters (such as prisons). In addition, we assign casual contacts to each individual at random. Using this framework, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We also perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases on COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular, we consider the effects of 1) different timings for reopening, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses, we explore mechanisms driving magnitude and timing of a second wave of infections upon re-opening. This model can be adapted to other US counties using synthetic population databases and data specific to those regions.
has issue date	2020-07-07(xsd:dateTime)
bibo:doi	10.1101/2020.07.06.20147223
bibo:pmid	32676613
has license	cc-by-nc-nd
sha1sum (hex)	75c574268edb99e2616f63c59325bd841012ecfd
schema:url	https://doi.org/10.1101/2020.07.06.20147223
resource representing a document's title	Predicting the second wave of COVID-19 in Washtenaw County, MI
has PubMed Central identifier	PMC7359538
has PubMed identifier	32676613
schema:publication	medRxiv
resource representing a document's body	covid:75c574268edb99e2616f63c59325bd841012ecfd#body_text
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