EPINEST, an agent-based model to simulate epidemic dynamics in large-scale poultry production and distribution networks

Francesco Pinotti*, José Lourenço, Sunetra Gupta, Suman Das Gupta, Joerg Henning, Damer Blake, Fiona Tomley, Tony Barnett, Dirk Pfeiffer, Md. Ahasanul Hoque, Guillaume Fournié

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

The rapid intensification of poultry production raises important concerns about the associated risks of zoonotic infections. Here, we introduce EPINEST (EPIdemic NEtwork Simulation in poultry Transportation systems): an agent-based modelling framework designed to simulate pathogen transmission within realistic poultry production and distribution networks. We provide example applications to broiler production in Bangladesh, but the modular structure of the model allows for easy parameterization to suit specific countries and system configurations. Moreover, the framework enables the replication of a wide range of ecoepidemiological scenarios by incorporating diverse pathogen life-history traits, modes of transmission and interactions between multiple strains and/or pathogens. EPINEST was developed in the context of an interdisciplinary multi-centre study conducted in Bangladesh, India, Vietnam and Sri Lanka, and will facilitate the investigation of the spreading patterns of various health hazards such as avian influenza, Campylobacter, Salmonella and antimicrobial resistance in these countries. Furthermore, this modelling framework holds potential for broader application in veterinary epidemiology and One Health research, extending its relevance beyond poultry to encompass other livestock species and disease systems. © 2024 Pinotti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Original languageEnglish
Article numbere1011375
JournalPLoS Computational Biology
Volume20
Issue number2
Online published21 Feb 2024
DOIs
Publication statusPublished - Feb 2024

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