The panzootic highly pathogenic avian influenza (HPAI) H5N1 virus has now been detected on the Australian mainland, with incursions from the sub-Antarctic region posing an increasing threat to domestic wildlife and poultry populations. Our study aimed to predict the risk of HPAI H5N1 poultry outbreaks across Australia at the local government area (LGA) level using a range of influential risk factors. We first used a Maximum Entropy (MaxEnt) model to estimate the environmental suitability for HPAI H5N1 occurrence across Australia. The resulting suitability layer was then integrated with five additional predictor layers, including abundance data for two Southern Ocean wild birds, one of which has introduced HPAI H5N1 into Australia; abundance data for 28 native Australian wild birds; native bird flyways across Australia; Australian chicken density; and poultry farm density. The six layers were aggregated and averaged to generate an HPAI H5N1 risk map for poultry outbreaks across Australian LGAs. Although most incursions have occurred in Western Australia (WA) and South Australia (SA), we identified New South Wales (NSW) and Victoria (VIC) as having the highest predicted risk of HPAI H5N1 poultry outbreaks. Additional high-risk areas were identified in WA, SA, and Tasmania (TAS). In contrast, the Northern Territory (NT) and large parts of Queensland (QLD), WA, and SA were predicted to be at low risk. These findings provide a spatially explicit framework to support 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
Abstract. Highly Pathogenic Avian Influenza (HPAI), particularly the H5N1 strain, poses a significant ongoing threat to animal health, biodiversity and food security across Europe. Understanding where and when avian influenza risks intensify is essential for targeted surveillance and rapid response. This study develops 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. Weekly country-level outbreak counts (208 weeks, 37 countries) were analysed using a hierarchical endemic-epidemic model with an assumption of Negative Binomial distribution. Environmental covariates, bird-species densities, and human population metrics were incorporated into endemic and autoregressive components. Model performance was evaluated using rolling one-step-ahead forecasts assessed by proper scoring rules (logarithmic score and ranked probability score) and calibration diagnostics. The proposed framework substantially outperformed a regression-only Negative Binomial baseline, reducing mean logS by approximately 29% and RPS by 49%, while exhibiting improved probabilistic calibration. Results indicate that H5N1 transmission is structured by ecological drivers and local persistence mechanisms rather than purely seasonal effects. Anseriformes, Charadriiformes and Pelecaniformes densities were identified as the key migratory bird families contributing to the viral spread. The endemic-epidemic model achieved high forecast accuracy, with majority of the of observed weekly outbreak counts falling within central predictive intervals (RPS = 0.76, logS = 0.61). Overall, the proposed framework provides a scalable approach for integrating ecological and spatial information into early-warning systems for HPAI surveillance.
Mehak Jindal, Samsung Lim, Raina MacIntyre· The International Archives o...· 0 citations