Climate-informed short-term forecasting of influenza incidence in Australia

Abstract

Background and aims: Climate variability is increasingly recognised as an important driver of infectious disease dynamics; however, climate-informed forecasts of influenza burden remain limited in Australia. Such forecasts could support timely public health preparedness and response. This study aimed to develop a data-driven Bayesian framework to improve short-term influenza forecasting across Australian states and territories by incorporating climate information. Method: We used a spatiotemporal Bayesian hierarchical model to forecast laboratory-confirmed monthly influenza notifications from 2008 to 2022 (excluding the COVID-19 period). Monthly temperature and relative humidity data were included as predictors, with temperature modelled using contemporaneous and one-month lagged effects, and humidity categorised into three levels. The model incorporated region-specific temporally autocorrelated random effects and a time-invariant spatial random effect. Five model variants, ranging from a baseline without meteorological predictors to models with linear, nonlinear, and interaction effects, were evaluated using a scaled root-mean-square error metric. Forecasts were generated for lead times of 1–12 months using a one-month sliding training window. Sensitivity analysis varied training window duration (3–10 years) to identify the minimum data required for stable forecasts. Results: Climate-informed models consistently outperformed the baseline model at short to intermediate lead times. Temperature-based models, particularly those allowing for nonlinear and lagged effects, achieved the highest forecast skill, with improvements observed up to lead times of 4–5 months. Reliable forecasts were obtained using as little as five years of data. Relative humidity provided limited additional predictive value, and forecast skill declined beyond approximately six months. Spatial and temporal random effects indicated persistent regional and temporal variability, while exposure-response analyses showed increased influenza risk at lower temperatures, especially for lagged temperature. Conclusion: Temperature is a key driver of short-term influenza dynamics in Australia. Climate-informed Bayesian models provide skilful and operationally relevant forecasts, supporting the development of early warning systems for public health decision-making.