A statistical modelling approach for source attribution meta-analysis of sporadic infection with foodborne pathogens.

ABSTRACT: Numerous source attribution studies for foodborne pathogens based on epidemiological and microbiological methods are available. These studies provide empirical data for modelling frameworks that synthetize the quantitative evidence at our disposal and reduce reliance on expert elicitations. Here, we develop a statistical model within a Bayesian estimation framework to integrate attribution estimates from expert elicitations with estimates from microbial subtyping and case-control studies for sporadic infections with four major bacterial zoonotic pathogens in the Netherlands (Campylobacter, Salmonella, Shiga toxin-producing E. coli [STEC] O157 and Listeria). For each pathogen, we pooled the published fractions of human cases attributable to each animal reservoir from the microbial subtyping

SUBMITTER: Mughini-Gras L 

PROVIDER: S-EPMC9545847 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature