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One Health
. 2025 Oct 11:21:101238.
doi: 10.1016/j.onehlt.2025.101238. eCollection 2025 Dec. Projecting the impacts of climate and land-use change on avian influenza suitability in Bangladesh
Adam C Castonguay 1 2 , Sukanta Chowdhury 3 , Ireen Sultana Shanta 3 , Bente Schrijver 4 , Remco Schrijver 4 , Mohammad Mahmudul Hassan 2 , Shiyong Wang 5 , Ricardo J Soares Magalhães 2 6
Affiliations
Climate and land-use change are expected to influence the future dynamics of zoonotic disease outbreaks, including avian influenza, a prime example of a One Health challenge. Environmental and socio-economic conditions modulate the risk of virus spillover from wild birds to farmed animals and, subsequently, to humans through multiple transmission pathways. However, the extent to which changing environmental conditions may alter the spatial suitability for avian influenza transmission across regions and interfaces in the future remains uncertain. To address this gap, we developed a spatially explicit, integrated modelling framework that simulates wild and farmed bird distributions and incorporates this information into a spatial structural equation model of avian influenza exposure suitability. This model captures the complex interactions between the environment, wildlife, and poultry production and retail systems in Bangladesh. In addition, the framework includes an assessment of the population at risk living in areas considered suitable for virus exposure. Our approach allows us to project the spatial suitability for avian influenza exposure under current and future climate, land cover and population density, and to better understand the key drivers and underlying mechanisms of exposure suitability. Results indicate that suitability is expected to increase significantly in poultry farming areas, driven by growing chicken density. We find that this shift could increase the human population at risk of exposure to avian influenza by up to 79 million by 2050. This modelling approach provides an evidence-based decision support tool to help prioritise surveillance and preventive interventions in key transmission interfaces across the country.
Keywords: Climate change; Land cover change; Machine learning; One health; Spatial modelling; Zoonotic diseases.
. 2025 Oct 11:21:101238.
doi: 10.1016/j.onehlt.2025.101238. eCollection 2025 Dec. Projecting the impacts of climate and land-use change on avian influenza suitability in Bangladesh
Adam C Castonguay 1 2 , Sukanta Chowdhury 3 , Ireen Sultana Shanta 3 , Bente Schrijver 4 , Remco Schrijver 4 , Mohammad Mahmudul Hassan 2 , Shiyong Wang 5 , Ricardo J Soares Magalhães 2 6
Affiliations
- PMID: 41141933
- PMCID: PMC12550288
- DOI: 10.1016/j.onehlt.2025.101238
Climate and land-use change are expected to influence the future dynamics of zoonotic disease outbreaks, including avian influenza, a prime example of a One Health challenge. Environmental and socio-economic conditions modulate the risk of virus spillover from wild birds to farmed animals and, subsequently, to humans through multiple transmission pathways. However, the extent to which changing environmental conditions may alter the spatial suitability for avian influenza transmission across regions and interfaces in the future remains uncertain. To address this gap, we developed a spatially explicit, integrated modelling framework that simulates wild and farmed bird distributions and incorporates this information into a spatial structural equation model of avian influenza exposure suitability. This model captures the complex interactions between the environment, wildlife, and poultry production and retail systems in Bangladesh. In addition, the framework includes an assessment of the population at risk living in areas considered suitable for virus exposure. Our approach allows us to project the spatial suitability for avian influenza exposure under current and future climate, land cover and population density, and to better understand the key drivers and underlying mechanisms of exposure suitability. Results indicate that suitability is expected to increase significantly in poultry farming areas, driven by growing chicken density. We find that this shift could increase the human population at risk of exposure to avian influenza by up to 79 million by 2050. This modelling approach provides an evidence-based decision support tool to help prioritise surveillance and preventive interventions in key transmission interfaces across the country.
Keywords: Climate change; Land cover change; Machine learning; One health; Spatial modelling; Zoonotic diseases.