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

Abstract

Infectious disease forecasting has become increasingly important in public health, as demonstrated during the COVID-19 pandemic. 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 prediction targets at three timepoints across four regional clusters. Our 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 as a benchmark for future model improvements. Forecast accuracy, certainty, and skill improved significantly after the outbreaks peak, while early predictions were more variable, suggesting that pre-peak forecasts should be interpreted with caution. Spatial forecasts maintained positive skill throughout the outbreak, supporting their use in guiding response priorities. This framework provides a tool for real-time decision-making during livestock disease outbreaks and establishes a foundation for future refinements and applications to other animal diseases.