ArticlePLoS computational biology2024
EPINEST, an agent-based model to simulate epidemic dynamics in large-scale poultry production and distribution networks.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Interdisciplinary modelling approaches for One Health in agri-food systems: a scoping review and conceptual analysis.BMC public health · 2026Article
- Emerging and Re-Emerging Viral Infections in Poultry: Integrating Traditional and AI-Based Control Strategies.Current microbiology · 2026Review
- Article
- Modelling a potential zoonotic spillover event of H5N1 influenza.BMC public health · 2025Article
- Harnessing Ethnographic Methods to Explore the Epidemiological Significance of Livestock Trading Practices: The Case of Chennai's Broiler Chicken Trade.Transboundary and emerging diseases · 2025Article
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Authors and funding
11 authors.
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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 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.
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