African swine fever in wild boar: investigating model assumptions and structure

TL;DR

This study develops and compares various models of wild boar populations to understand African swine fever transmission, emphasizing how model assumptions influence predicted disease spread and intervention effectiveness.

Contribution

The paper introduces a comprehensive modeling framework incorporating social structure and contact functions to better understand ASF dynamics in wild boar populations.

Findings

Power law transmission functions best fit Baltic outbreak data.

Model heterogeneity reduces carcass transmission importance.

Different models suggest varying intervention strategies.

Abstract

African swine fever (ASF) is a highly virulent viral disease that affects both domestic pigs and wild boar. Current ASF transmission in Europe is in part driven by wild boar populations, which act as a disease reservoir. Wild boar are abundant throughout Europe and are highly social animals with complex social organisation. Despite the known importance of wild boar in ASF spread and persistence, there remain knowledge gaps surrounding wild boar transmission. To investigate the influence of density-contact functions and wild boar social structure on disease dynamics, we developed a wild boar modelling framework. The framework included an ordinary differential equation model, a homogeneous stochastic model, and various network-based stochastic models that explicitly included wild boar social grouping. We found that power law functions (transmission $\propto$ density$^{0.5}$) and frequency-based density-contact functions were best able to reproduce recent Baltic outbreaks; however, power law function models predicted considerable carcass transmission, while frequency-based models had negligible carcass transmission. Furthermore, increased model heterogeneity caused a decrease in the relative importance of carcass-based transmission. The different dominant transmission pathways predicted by each model type affected the efficacy of potential interventions, which highlights the importance of evaluating model type and structure when modelling systems with uncertainties.