Modeling Competing Infectious Pathogens from a Bayesian Perspective: Application to Influenza Studies with Incomplete Laboratory Results.

TitleModeling Competing Infectious Pathogens from a Bayesian Perspective: Application to Influenza Studies with Incomplete Laboratory Results.
Publication TypeJournal Article
Year of Publication2010
AuthorsYang Y, M Halloran E, Daniels MJ, Longini IM, Burke DS, Cummings DAT
JournalJ Am Stat Assoc
Volume105
Issue492
Pagination1310-1322
Date Published2010 Dec
ISSN0162-1459
Abstract

In seasonal influenza epidemics, pathogens such as respiratory syncytial virus (RSV) often co-circulate with influenza and cause influenza-like illness (ILI) in human hosts. However, it is often impractical to test for each potential pathogen or to collect specimens for each observed ILI episode, making inference about influenza transmission difficult. In the setting of infectious diseases, missing outcomes impose a particular challenge because of the dependence among individuals. We propose a Bayesian competing-risk model for multiple co-circulating pathogens for inference on transmissibility and intervention efficacies under the assumption that missingness in the biological confirmation of the pathogen is ignorable. Simulation studies indicate a reasonable performance of the proposed model even if the number of potential pathogens is misspecified. They also show that a moderate amount of missing laboratory test results has only a small impact on inference about key parameters in the setting of close contact groups. Using the proposed model, we found that a non-pharmaceutical intervention is marginally protective against transmission of influenza A in a study conducted in elementary schools.

DOI10.1198/jasa.2010.ap09581
Alternate JournalJ Am Stat Assoc
PubMed ID21472041
PubMed Central IDPMC3070363
Grant ListR01 AI032042 / AI / NIAID NIH HHS / United States
R01 AI032042-16 / AI / NIAID NIH HHS / United States
R01 CA085295 / CA / NCI NIH HHS / United States
R01 CA085295-09A1 / CA / NCI NIH HHS / United States
R37 AI032042 / AI / NIAID NIH HHS / United States
U01 GM070749 / GM / NIGMS NIH HHS / United States
U01 GM070749-07 / GM / NIGMS NIH HHS / United States
U54 GM088491 / GM / NIGMS NIH HHS / United States
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