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

High pathogenicity avian influenza (HPAI) H5N1, specifically clade 2.3.4.4b, has unleashed a global panzootic with severe consequences for wildlife and livestock populations. In response to this crisis, researchers have developed a spatiotemporal mechanistic model of infectious disease dynamics, specifically tailored for the HPAI Modelling Challenge. This model aims to enhance forecasting and policy-making in Australia, particularly in the context of emergency response.

To simulate true emergency conditions, the researchers adapted an existing model for quick deployment, rather than creating a new model from scratch. They meticulously refined the model throughout the challenge, using it to better understand the provided outbreak data. While the model successfully predicted temporal trends and local outbreak spread, it struggled to anticipate rarer, long-distance dispersal events.

The challenge concluded before Australia detected HPAI H5N1 in wildlife and commercial poultry, providing a valuable opportunity to test the researchers' preparedness for such an incursion. Through their experience, the team identified three crucial factors for Australia's HPAI H5N1 preparedness. First, they emphasized the need for targeted enhancements to the model to improve forecast precision and enable scenario-based policy evaluation.

Second, they highlighted the vital role of pre-existing modelling infrastructure in facilitating rapid emergency response. Lastly, they stressed the importance of sustained collaboration between research and policy institutions to ensure that modelling capabilities align with outbreak response requirements.