Estimating probability of infection
For every sampled individual (n = 323), we selected ASF-positive individuals that could have been the source of infection using a temporal window (30 days) that reflects the upper limit of lifespan after ASF infection, i.e. infection-to-death time (Gallardo et al., 2017; Pietschmann et al., 2015). For samples originating from carcasses, the window was set to 60 days prior to sampling and included infection-to-death time and carcass decomposition time during which the sample was taken. A decomposition time of 30 days was chosen arbitrarily because the decomposition status of most carcasses was unknown and the exact date of death could not be determined. This time frame was conservative given usually longer decomposition times of wild boar tissues (Probst et al., 2020) and ASF virus persistence in them (Fischer et al., 2020). Additionally, the temporal window was extended by 7 days post-sampling of the focal individual to account for the retrieval and reporting lag of the ASF-positives. ASF-positive individuals selected within those time frames were then categorized into four distance classes (0-2 km, 2-5 km, 5-10 km, >10 km). The proportion of ASF cases in each distance class were used as covariates explaining the binary response of ASF infection status of the focal individual (0 - negative, 1- positive).
We analysed the effects of explanatory variables on the probability of ASF infection using a generalized linear model with a binomial response and ”logit” link function (Zuur, 2009). The probability of ASF infection reflects the likelihood of any sampled individual from the study area testing positive for ASF within the 2.5 years of the study period based on the covariates. Proportion of ASF-positive individuals within the four aforementioned distance classes and relatedness to ASF-positive individuals within 0-10 km was used as covariates to verify the predictions (Table 1). Sample sizes across covariate categories were unbalanced and precluded formulation of a global model including all explanatory variables. Therefore, the structure of the models was developed progressively, starting with simple formulations where each model included just one covariate explaining infection risk (Supplementary Information: Table S1). We then contrasted, in four separate models (Table 2), information on relatedness to ASF-positive individuals within 0-10 km with the proportion of ASF-positive individuals in each of the four distance classes. This approach allowed us to test the relative effect of relatedness and spatial proximity to infectees on infection risk while accounting for the greater number of highly related individuals at close distances resulting from wild boar social structure. Explanatory variables did not show significant collinearity (variance inflation factors - VIFs < 1.3 for all models). We did not perform model selection for reduced models within each hypothesis as we were interested in testing specific predictions represented by each of the candidate explanatory variables in our full models and we could not compare across models because the response variable was different for each model. We quantified variation explained by the fixed effects of the model by calculating marginalR 2GLMM (Nakagawa et al., 2017) using the MuMIn R-package (Bartoń 2020). All statistical analyses were performed in R 4.0.2 (R Core Team 2020). The models were computed using the ”lme4” package (Bates et al. 2015).