SrasV1, 2020, Analysis, bibRecord, 000A53

A Bayesian approach for detecting a disease that is not being modeled.

Identifieur interne : 000A53 ( 2020/Analysis ); précédent : 000A52; suivant : 000A54

A Bayesian approach for detecting a disease that is not being modeled.

Auteurs : John M. Aronis [États-Unis] ; Jeffrey P. Ferraro [États-Unis] ; Per H. Gesteland [États-Unis] ; Fuchiang Tsui [États-Unis] ; Ye Ye [États-Unis] ; Michael M. Wagner [États-Unis] ; Gregory F. Cooper [États-Unis]

Source :

PloS one [ 1932-6203 ] ; 2020.

RBID : pubmed:32109254

Abstract

Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.

DOI: 10.1371/journal.pone.0229658
PubMed: 32109254

Affiliations:

Links to Exploration step

pubmed:32109254

Le document en format XML

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A Bayesian approach for detecting a disease that is not being modeled.

A Bayesian approach for detecting a disease that is not being modeled.

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