A Bayesian approach for detecting a disease that is not being modeled.
PLoS One
; 15(2): e0229658, 2020.
Article
in English
| MEDLINE | ID: covidwho-1453108
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Disease Outbreaks
/
Models, Biological
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
PLoS One
Journal subject:
Science
/
Medicine
Year:
2020
Document Type:
Article
Affiliation country:
Journal.pone.0229658
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