A real-time forecasting framework for emerging infectious diseases affecting animal populations

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.Author summary: Infectious disease outbreaks in livestock can spread rapidly, making timely and well-informed decisions critical for limiting their impact. However, predicting how an outbreak will evolve is difficult, especially early on when little is known about how the disease spreads. In this study, we developed a flexible modelling framework that uses data collected during the early stages of an outbreak to generate real-time forecasts of how disease may spread over time and space. We applied our approach to the 2007 equine influenza outbreak in Australia and assessed how well it could predict future cases and identify areas at higher risk. We found that forecasts became more accurate as more data became available, and that short-term predictions were generally more reliable than those made further into the future. Importantly, while precise predictions of case numbers or individual affected farms were often uncertain, the model was better at identifying “hotspot” areas of higher risk. These findings suggest the model is most useful for guiding targeted surveillance and control to these higher-risk zones, particularly when its uncertainty and assumptions are carefully considered.