A spatial transmission model for evaluating interventions during highly pathogenic avian influenza outbreaks
- 1University of Oxford
Abstract
During infectious disease outbreaks, epidemiological models can be used to project future outbreak dynamics and to explore scenarios under different control measures. Here, we present the spatially explicit avian influenza transmission model that we developed during the 2026 WiLiMan-ID consortium Highly Pathogenic Avian Influenza Modelling Challenge, which involved evaluating control measures (preventive and reactive culling, confinement and restocking bans) during a hypothetical avian influenza outbreak. We show that the control measures that were expected to reduce transmission effectively varied between different outbreak stages. For example, policy-makers could choose whether to focus culling efforts on: i) faster reactive culling and no preventive culling; or ii) slower reactive culling and preventive culling. Relatively early in the outbreak, when the number of available culling teams was limited, our results indicate that the two culling strategies were projected to lead to similar numbers of premises with notified infections over the following 28 days. Later in the outbreak, with more culling teams available, the second strategy was projected to lead to substantially fewer notified premises than the first strategy. In addition, we demonstrate that whether a strategy is beneficial or detrimental depends on policy-makers' objectives. For example, preventive culling may reduce transmission, but at the cost of culling poultry on premises without infections, illustrating a trade-off between limiting transmission and the cost of interventions. Our results highlight the importance of choosing policy objectives carefully and reassessing interventions at different outbreak stages to ensure that control policies remain appropriate for the current epidemiological situation.
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BibTeX
@misc{bergstrom2026spatial,
title = {{A spatial transmission model for evaluating interventions during highly pathogenic avian influenza outbreaks}},
author = {F. Bergstrom and E. M. Hill and C. Rago and C. N. Davis and M. J. Tildesley and R. N. Thompson},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.14.758777},
url = {https://www.biorxiv.org/content/10.64898/2026.10.14.758777v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A spatial transmission model for evaluating interventions during highly pathogenic avian influenza outbreaks
AU - F. Bergstrom
AU - E. M. Hill
AU - C. Rago
AU - C. N. Davis
AU - M. J. Tildesley
AU - R. N. Thompson
PY - 2026
DA - 2026/10/14
PB - bioRxiv
DO - 10.64898/2026.10.14.758777
UR - https://www.biorxiv.org/content/10.64898/2026.10.14.758777v1
AB - During infectious disease outbreaks, epidemiological models can be used to project future outbreak dynamics and to explore scenarios under different control measures. Here, we present the spatially explicit avian influenza transmission model that we developed during the 2026 WiLiMan-ID consortium Highly Pathogenic Avian Influenza Modelling Challenge, which involved evaluating control measures (preventive and reactive culling, confinement and restocking bans) during a hypothetical avian influenza outbreak. We show that the control measures that were expected to reduce transmission effectively varied between different outbreak stages. For example, policy-makers could choose whether to focus culling efforts on: i) faster reactive culling and no preventive culling; or ii) slower reactive culling and preventive culling. Relatively early in the outbreak, when the number of available culling teams was limited, our results indicate that the two culling strategies were projected to lead to similar numbers of premises with notified infections over the following 28 days. Later in the outbreak, with more culling teams available, the second strategy was projected to lead to substantially fewer notified premises than the first strategy. In addition, we demonstrate that whether a strategy is beneficial or detrimental depends on policy-makers' objectives. For example, preventive culling may reduce transmission, but at the cost of culling poultry on premises without infections, illustrating a trade-off between limiting transmission and the cost of interventions. Our results highlight the importance of choosing policy objectives carefully and reassessing interventions at different outbreak stages to ensure that control policies remain appropriate for the current epidemiological situation.
ER -