A localised risk model for liver fluke infection

Precise and focused risk assessments of liver fluke infection in cattle can be used to increase awareness and promote management uptake. However, accurate estimation of the risk of liver fluke infection in cattle is challenging. Liver fluke disease in adult cattle is typically sub-clinical - meaning infected animals often have no visible symptoms. This is exacerbated by the complex liver fluke life-cycle, which is highly sensitive to climate conditions and requires the presence of the intermediate snail host. The aim of this work was to create a predictive modelling tool that can be used to predict the location-specific risk of infection with respect to changing climate conditions. The study utilised processor data of over 4 million cattle processed between 2016 and 2020 at one of Australia's largest processors. A binary indicator variable for liver fluke infection (liver fluke or no liver fluke) was observed at processing for each animal, with no further information as to when the infection may have occurred. We propose a spatio-temporal model to predict the risk of liver fluke infection, utilising location specific and time-varying covariates. The model output was used to create localised risk profiles for each Australian postcodes (a four-digit government allocated number used to identify postal delivery regions within Australia). Cross-validation results showed that both the Random Forest (RF) and Generalised Additive Model (GAM) performed comparably on test data, with the RF model slightly preferred for its ability to capture complex nonlinear patterns in liver fluke infection. Forecasts for 2022 reflected seasonal variation and demonstrated the potential utility of the model for informing targeted management decisions in high-risk, wetter regions. The work in this study can be used to help inform cattle producers about the risk of infection on their property.