About: Modeling Regional Disease Spread Over Time Using a Dynamic Spatio-temporal Model – With an Application to Porcine Epidemic Diarrhea Virus data in Iowa, U.S.

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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 Regional Disease Spread Over Time Using a Dynamic Spatio-temporal Model – With an Application to Porcine Epidemic Diarrhea Virus data in Iowa, U.S.
Creator	Ji, Ju Wang, C Zimmerman, J Ji, J Ji, U Rotolo, M
source	Elsevier; Medline; PMC
abstract	Regional surveillance is important for detecting the incursion of new pathogens and informing disease monitoring and control programs. Modeling disease distribution over time can provide insight into the development of more efficient regional surveillance approaches. Herein we propose a Bayesian spatio-temporal model to describe the distribution of porcine epidemic diarrhea virus (PEDV) in Iowa USA. Model parameters are estimated through a Bayesian spatio-temporal model approach which can account for missing values. For illustration, we apply the proposed model to PEDV test results from the Iowa State University Veterinary Diagnostic Laboratory (ISU-VDL). A simulation study carried out to evaluate the model showed that the proposed model captured the pattern of PEDV distribution and its spatio-temporal dependence.
has issue date	2020-06-20(xsd:dateTime)
bibo:doi	10.1016/j.prevetmed.2020.105053
bibo:pmid	32623290
has license	no-cc
sha1sum (hex)	d66010c5b1c86efa6b1e455e0eb41cc86aba9920
schema:url	https://doi.org/10.1016/j.prevetmed.2020.105053
resource representing a document's title	Modeling Regional Disease Spread Over Time Using a Dynamic Spatio-temporal Model – With an Application to Porcine Epidemic Diarrhea Virus data in Iowa, U.S.
has PubMed Central identifier	PMC7305876
has PubMed identifier	32623290
schema:publication	Prev Vet Med
resource representing a document's body	covid:d66010c5b1c86efa6b1e455e0eb41cc86aba9920#body_text
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