Abstract
Cases of anthrax in livestock are infrequently and irregularly reported in the state of Victoria, Australia; however, their impact on individual livestock, farming communities and the government agencies tasked with containing these outbreaks is high. This infrequency has been anecdotally associated with differences in annual and local weather patterns. In this study, we used historical anthrax cases and meteorological data from weather stations throughout Victoria to train a generalized linear mixed effects model to predict the daily odds of a case of anthrax occurring in each shire in the coming 30 days. Meteorological variables were transformed to deviations from the mean values for temperature or cumulative values for rainfall in the shire across all years. Shire was incorporated as a random effect to account for meteorological variation between shires. The model incorporated a post hoc weighting for the frequency of historic cases within each shire and the spatial contribution of each shire to the recently redefined Australian Anthrax Belt. Our model reveals that anthrax cases were associated with drier summer conditions (OR 0.96 (95% CI 0.95–0.97) and OR 0.98 (95% CI 0.97–0.99) for every mm increase in rainfall during September and December, respectively) and cooler than average spring (OR 0.20 (95% CI 0.11–0.52) for every °C increase in minimum daily temperature during November and warmer than average summer temperatures (OR 1.45 (95% CI 1.29–1.61) for every °C increase in maximum daily temperature during January. Cases were also preceded by a 40-day period of cooler, drier temperatures (OR 0.5 (95% CI 0.27–0.74) for every °C increase in maximum daily temperature and OR 0.96 (95% CI 0.95–0.97) for every mm increase in rainfall followed by a warmer than average minimum (or nightly) temperature 10 days immediately before the case (OR 1.46 (95% CI 1.35–1.58) for every °C increase in maximum daily temperature). These coefficients of this training model were then applied daily to meteorological data for each shire, and output of these models was presented as a choropleth and timeline plot in a Shiny web application. The application builds on previous spatial modelling and provides Victorian agencies with a tool to engage at-risk farmers and guide discussions towards anthrax control. This application can contribute to the wider rejuvenation of anthrax knowledge and control in Victoria and corroborates the anecdote that increased odds of disease can be linked to meteorological events.