Hybrid Modeling of Ebola Propagation.


Journal

Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering
ISSN: 2159-5410
Titre abrégé: Proc IEEE Int Symp Bioinformatics Bioeng
Pays: United States
ID NLM: 101522833

Informations de publication

Date de publication:
Oct 2019
Historique:
entrez: 20 6 2020
pubmed: 20 6 2020
medline: 20 6 2020
Statut: ppublish

Résumé

The Ebola virus disease (EVD) epidemic that occurred in West Africa between 2014-16 resulted in over 28,000 cases and 11,000 deaths - one of the deadliest to date. A generalized model of the spatiotemporal progression of EVD for Liberia, Guinea, and Sierra Leone in 2014-16 remains elusive. There is also a disconnect in the literature on which interventions are most effective in curbing disease progression. To solve these two key issues, we designed a hybrid agent-based and compartmental model that switches from one paradigm to the other on a stochastic threshold. We modeled disease progression with promising accuracy using WHO datasets.

Identifiants

pubmed: 32551185
doi: 10.1109/bibe.2019.00044
pmc: PMC7302111
mid: NIHMS1595290
doi:

Types de publication

Journal Article

Langues

eng

Pagination

204-210

Subventions

Organisme : NCI NIH HHS
ID : U54 CA119338
Pays : United States

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Auteurs

Cyrus Tanade (C)

Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.

Nathanael Pate (N)

Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.

Elianna Paljug (E)

Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.

Ryan A Hoffman (RA)

Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.

May D Wang (MD)

Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.

Classifications MeSH