A spatiotemporal mechanistic model of infectious disease dynamics developed for the HPAI Modelling Challenge and its implications for forecasting and policy in Australia are described.
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.
A data-driven spatio-temporal framework that integrates geospatial, ecological and climatic datasets to explain and forecast the dynamics of H5N1 outbreaks between 2021 and 2024 indicates that H5N1 transmission is structured by ecological drivers and local persistence mechanisms rather than purely seasonal effects.
Mehak Jindal, Samsung Lim, C. MacIntyre· The International Archives o...· 0 citations
Predicting the risk of HPAI H5N1 poultry outbreaks across Australia at the local government area (LGA) level using a range of influential risk factors provides a spatially explicit framework for targeted surveillance, preparedness, and biosecurity measures aimed at mitigating the impact of future HPAI H5N1 outbreaks in Australian poultry.
Pan Zhang, Samsung Lim, A. Quigley et al.· bioRxiv· 0 citations
Highly pathogenic avian influenza H5Nx viruses remain a major zoonotic threat, yet global attention has focused largely on clade 2.3.4.4b, potentially overlooking major changes within long-endemic H5N1 lineages in Asia. Recent reports from South and Southeast Asia describe the emergence of reassortant clade 2.3.2.1 viruses alongside renewed human infections after apparent prolonged epidemiologic stability. Collectively, those events suggest a regional pattern rather than isolated anomalies. In this article, we argue that reassortment, rather than point mutation alone, might be an underrecognized driver of zoonotic risk in endemic H5N1 lineages and is reshaping those lineages. We examine why such events might be underrecognized in settings with entrenched poultry influenza, identify limitations of current surveillance systems, and call for integrated, real-time approaches linking genomic detection with phenotypic assessment across animal and human health sectors to enable timely risk assessment and coordinated public health action.
A. M. Byrne, Lorcan Carnegie, N. Lewis et al.· Emerging Infectious Diseases· 0 citations
Infectious disease forecasting has become increasingly important in public health. However, forecasting tools for emergency animal diseases, particularly those offering real-time decision support when parameters governing disease dynamics are unknown, remain limited. We introduce a generalised modelling framework for near-real-time forecasting of the temporal and spatial spread of infectious livestock diseases using data from the early stages of an outbreak. We applied the framework to the 2007 equine influenza outbreak in Australia, generating forecasts at three timepoints across four regional clusters. Prediction targets included future daily case counts, outbreak size, peak timing and duration, and spatial distributions of future spread. We evaluated how well the forecasts predicted daily cases and the spatial distribution of case counts, using skill scores (a measure of probabilistic forecast accuracy) as a benchmark for future model improvements. Forecast accuracy, certainty, and skill improved after formation of the outbreak’s peak, while early forecasts were more uncertain or prone to overestimation, highlighting the need for caution when interpreting pre-peak predictions, particularly when the impacts of control policies on future transmission are not adequately represented in the model. Spatial forecasts of broad, relative risk patterns were more robust than precise predictions of risk at the individual premises level or exact daily cases counts, supporting geographically targeted response strategies. Overall, this framework supports real-time decision-making in livestock disease outbreaks when applied with appropriate consideration of uncertainty, and establishes a foundation for future refinements and applications to other animal diseases.
M. Theng, Si-Min Lee, Andrew C. Breed et al.· PLoS Computational Biology· 0 citations
The 2025/26 influenza season is characterised by early but not unusually rapid growth, and Laboratory evidence for antibody escape does not directly translate to large reductions in population immunity, supporting the need for complementary real-time epidemiological analyses and modelling.
James A Hay, P. Alahakoon, Alexander Greenshields-Watson et al.· Communications Health· 0 citations
Influenza A viruses (IAVs) remain a persistent global threat because of rapid antigenic change, frequent genetic reassortment, and cross-species transmission. Swine act as key “mixing vessels,” permitting co-infection with avian and human strains and accelerating viral diversification and zoonotic risk. Effective veterinary surveillance is therefore critical for detecting viral evolution within swine populations and for providing early warning at the animal–human interface. Taiwan, home to approximately 5.5 million pigs and 23.4 million people, offers a representative case for examining how swine influenza surveillance has evolved in response to these challenges. This review describes the development of veterinary surveillance strategies from fragmented, event-driven monitoring to a coordinated One Health governance framework incorporating routine whole-genome sequencing, targeted serological surveillance, and coordinated data exchange across sectors. Key historical milestones include mass vaccination and culling programs, the 2009 pandemic response and recent zoonotic detections of H1N2v in humans (2021–2023), collectively evidencing ongoing bidirectional viral exchange. Episodes of severe human seasonal influenza activity, including H3N2-associated outbreaks, further highlighted the importance of coordinated surveillance across human and animal health sectors. By focusing on surveillance practices applied to animals, this review highlights the practical value of integrating molecular epidemiology, veterinary field monitoring, artificial intelligence (AI)-enabled technologies and cross-sector communication under a One Health framework. The Taiwanese experience demonstrates how strengthened veterinary surveillance can improve the sensitivity for detecting reassortment events, enhance traceability of viral spread, and support timely risk assessment in intensive livestock systems. These lessons offer a practical, replicable pathway for other countries aiming to overcome swine influenza surveillance fragmentation and strengthen pandemic preparedness under a One Health framework. The review traces Taiwan’s surveillance evolution from fragmented monitoring of swine influenza A viruses to a coordinated One Health governance model. A(H1N1)pdm09 and human H1N2v infections exposed gaps in notification capacity. The surveillance systems, genome sequencing and AI-enabled analytics are established to link animal, human, and environmental health for pandemic preparedness. The review traces Taiwan’s surveillance evolution from fragmented monitoring of swine influenza A viruses to a coordinated One Health governance model. A(H1N1)pdm09 and human H1N2v infections exposed gaps in notification capacity. The surveillance systems, genome sequencing and AI-enabled analytics are established to link animal, human, and environmental health for pandemic preparedness.
Cheng-Han Lin, Tzu-Min Lin, Meng‐Wei Lin et al.· Veterinary research communic...· 0 citations
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