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bioRxiv · 10.64898/2026.08.24.746897

Forecasting high pathogenicity avian influenza with a stochastic mechanistic model: performance and lessons for Australia

Theng, M.·Lee, S.·Wille, M.·Le, T. P.·Breed, A. C.·Donoghue, C.·Baker, C.·Firestone, S. P.
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Abstract

High pathogenicity avian influenza (HPAI) H5N1 clade 2.3.4.4b has caused a global panzootic with unprecedented impacts on wildlife and livestock, making evidence-based disease mitigation and outbreak response critical. In this paper, we describe a spatiotemporal mechanistic model of infectious disease dynamics developed for the HPAI Modelling Challenge and its implications for forecasting and policy in Australia. To emulate emergency response conditions, we adapted an existing model for rapid deployment rather than developing a bespoke model. We refined the model iteratively across the challenge to better analyse the provided outbreak data. Throughout the challenge, we accurately forecast temporal trends and local outbreak spread, but could not predict rarer, long-distance dispersal events. The challenge ended before HPAI H5N1 was first detected in Australia (June 2026), providing a critical opportunity to test our response modelling readiness for an incursion in wildlife and potential spillover into commercial poultry. Our experience identifies three key considerations for Australia's HPAI H5N1 preparedness: targeted enhancements to our model to improve forecast precision and enable scenario-based policy evaluation; the critical value of pre-existing modelling infrastructure for rapid emergency response; and sustained collaboration between research and policy institutions to align modelling capabilities with outbreak response requirements.

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Theng, M., Lee, S., Wille, M., Le, T. P., Breed, A. C., Donoghue, C., Baker, C., Firestone, S. P.. 2026-08-25. Forecasting high pathogenicity avian influenza with a stochastic mechanistic model: performance and lessons for Australia. https://doi.org/10.64898/2026.08.24.746897

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