About: A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts
Creator	Yeo, Wan-Jin Yeo, Yao-Rui Yeo, Yao-Yu
Source	MedRxiv
abstract	The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to make a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coasts. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April and begin to abate by July, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model also showed how small changes in variables can make large differences in outcomes and highlights the importance of healthcare preparedness during pandemics.
has issue date	2020-03-27(xsd:dateTime)
bibo:doi	10.1101/2020.03.24.20043026
has license	medrxiv
sha1sum (hex)	cf779757265b3301e6e0672c5212b3dd55fe8a12
schema:url	https://doi.org/10.1101/2020.03.24.20043026
resource representing a document's title	A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coasts
resource representing a document's body	covid:cf779757265b3301e6e0672c5212b3dd55fe8a12#body_text
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