Unravelling the swine web: Decoding feral pig networks for disease prevention in Australia

Abstract

Feral pigs pose a significant threat to wildlife and inflict substantial economic damage to Australian agriculture. They also serve as potential hosts for numerous infectious diseases, including foot-and-mouth disease (FMD), swine vesicular disease, Aujeszky’s disease, African swine fever (ASF), and classical swine fever, all which are exotic to Australia.
National preparedness for the incursion of these diseases hinges on the development and validation of disease transmission models. Estimating contact rates between individual animals is crucial to support valid transmission models. However, current models rely on contact rates from feral pig populations outside Australia, which may not adequately represent within and between Australian feral pig social units (sounders).
This study aims to fill this gap by quantifying interactions within and between feral pig sounders in Queensland and New South Wales. We utilized GPS-tracking data from feral pigs and employed network analysis to estimate direct and indirect contact rates for each year, season, and pig population. Our datasets were fitted to a continuous-time movement model (CTMM) to infer trajectories at a finer scale (five-minute intervals). We also examined the impact of varying distance thresholds on determining direct or indirect contact and explored factors that may influence contact rates, such as sex, season, and location.
Our analysis of 139,940 location fixes from 146 animals tracked from 2017 to 2023 revealed that the majority of direct (96%) and indirect (69%) contacts occurred within the same sounder. Indirect contacts within and between sounders were most prevalent during winter and less in summer. The sex of the dyad significantly influenced the contact rate. Our results provide locally relevant estimates of contact rates to support disease modelling. Importantly, our findings also underscore the importance of considering demographic and seasonal variations when estimating contact rates, given they will significantly affect contact rates and likely the dynamics of disease transmission.