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December 2020 Bayesian inference for multistrain epidemics with application to ESCHERICHIA COLI O157:H7 in feedlot cattle
Panayiota Touloupou, Bärbel Finkenstädt, Thomas E. Besser, Nigel P. French, Simon E. F. Spencer
Ann. Appl. Stat. 14(4): 1925-1944 (December 2020). DOI: 10.1214/20-AOAS1366

Abstract

For most pathogens, testing procedures can be used to distinguish between different strains with which individuals are infected. Due to the growing availability of such data, multistrain models have increased in popularity over the past few years. Quantifying the interactions between different strains of a pathogen is crucial in order to obtain a more complete understanding of the transmission process, but statistical methods for this type of problem are still in the early stages of development. Motivated by this demand, we construct a stochastic epidemic model that incorporates additional strain information and propose a statistical algorithm for efficient inference. The model improves upon existing methods in the sense that it allows for both imperfect diagnostic test sensitivities and strain misclassification. Extensive simulation studies were conducted in order to assess the performance of our method, while the utility of the developed methodology is demonstrated on data obtained from a longitudinal study of Escherichia coli O157:H7 strains in feedlot cattle.

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Panayiota Touloupou. Bärbel Finkenstädt. Thomas E. Besser. Nigel P. French. Simon E. F. Spencer. "Bayesian inference for multistrain epidemics with application to ESCHERICHIA COLI O157:H7 in feedlot cattle." Ann. Appl. Stat. 14 (4) 1925 - 1944, December 2020. https://doi.org/10.1214/20-AOAS1366

Information

Received: 1 November 2019; Revised: 1 June 2020; Published: December 2020
First available in Project Euclid: 19 December 2020

MathSciNet: MR4194254
Digital Object Identifier: 10.1214/20-AOAS1366

Keywords: epidemiology , genotypes , Markov chain Monte Carlo , misclassification , Multistate Markov model

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.14 • No. 4 • December 2020
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