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

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 eco-epidemiological 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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1371/journal.pcbi.1011375

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author
ORCID:
0000-0003-1953-2106
More by this author
Role:
Author
ORCID:
0000-0002-9318-2581
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Biology
Role:
Author
ORCID:
0000-0002-9775-4006


Publisher:
Public Library of Science
Journal:
PLoS Computational Biology More from this journal
Volume:
20
Issue:
2
Article number:
e1011375
Place of publication:
United States
Publication date:
2024-02-21
Acceptance date:
2024-02-06
DOI:
EISSN:
1553-7358
ISSN:
1553-734X
Pmid:
38381804


Language:
English
Pubs id:
1640253
Local pid:
pubs:1640253
Deposit date:
2024-04-19

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