Effects of social structure and management on risk of disease establishment in wild pigs

Abstract

Contact heterogeneity among hosts determines invasion and spreading dynamics of infectious disease, thus its characterization is essential for identifying effective disease control strategies. Yet, little is known about the factors shaping contact networks in many wildlife species and how wildlife management actions might affect contact networks. Wild pigs in North America are an invasive, socially structured species that pose a health concern for domestic swine given their ability to transmit numerous devastating diseases such as African swine fever (ASF). Using proximity loggers and GPS data from 48 wild pigs in Florida and South Carolina, USA, we employed a probabilistic framework to estimate weighted contact networks. We determined the effects of sex, social group and spatial distribution (monthly home‐range overlap and distance) on wild pig contact. We also estimated the impacts of management‐induced perturbations on contact and inferred their effects on ASF establishment in wild pigs with simulation. Social group membership was the primary factor influencing contacts. Between‐group contacts depended primarily on space use characteristics, with fewer contacts among groups separated by >2 km and no contacts among groups >4 km apart within a month. Modelling ASF dynamics on the contact network demonstrated that indirect contacts resulting from baiting (a typical method of attracting wild pigs or game species to a site to enhance recreational hunting) increased the risk of disease establishment by ~33% relative to direct contact. Low‐intensity population reduction (<5.9% of the population) had no detectable impact on contact structure but reduced predicted ASF establishment risk relative to no population reduction. We demonstrate an approach for understanding the relative role of spatial, social and individual‐level characteristics in shaping contact networks and predicting their effects on disease establishment risk, thus providing insight for optimizing disease control in spatially and socially structured wildlife species.

Basic reproduction number of African swine fever in wild boars (Sus scrofa) and its spatiotemporal heterogeneity in South Korea

“…The estimated R 0 ranged from 1.11 to 2.37 with a mean of 1.54, which was similar to the estimates from previous studies [ 11 29 30 31 ]. However, the current study revealed spatiotemporal variations in the values.…”

How do genetic relatedness and spatial proximity shape African swine fever infections in wild boar?

“…Similarly, kinship was shown to drive bovine tuberculosis infections in badger cubs exposed to infectious females in a natal sett (Benton et al, 2016). Inter-group contacts in wild boar occur most frequently at a spatial scale similar to interactions within groups Yang et al, 2020). This spatial overlap in social connectivity may confound the effects of inter-vs. intra-group transmission.…”

“…Wild boar social structure is based on cohesive, matrilineal social units (Gabor et al, 1999;Kaminski et al, 2005;Podgórski et al, 2014b). Contact rates are strongly structured socially and spatially Yang et al, 2020). The rate of inter-group interactions is relatively low and declines sharply with distance between the groups.…”

“…We expected relationships of infection risk and proximity or genetic relatedness to become weaker with increasing distance between individuals due to decay in contact rates and genetic similarity (Podgórski et al, 2014a;Poteaux et al, 2009) (H3, Table 1). Based on current knowledge of wild boar socio-spatial ecology, we analysed infection risk in four distance classes: 1) 'high-contact' zone (0-2 km): social contacts among individuals are most frequent, both within and between groups Yang et al, 2020) , 2) 'medium-contact' zone (2-5 km): interactions among neighbouring social groups , 3) 'low-contact' zone (5-10 km): sporadic contacts between distant groups with non-overlapping home ranges, distance of most natal dispersal (Keuling et al, 2010;Podgórski et al, 2014a;Prévot and Licoppe, 2013), 4) 'no-contact' zone (>10 km): groups do not interact, occasional long-distance movements (Andrzejewski and Jezierski, 1978;Podgórski et al, 2014a). We predicted that variation in infection risk would be explained by relatedness and proximity to infected individuals in high-contact and medium-contact zones (P3.1).…”

Defining an epidemiological landscape by connecting host movement to pathogen transmission

“…If instruments are not deployed in proportion to local host densities, then areas with a higher ratio of collars to animals may be flagged as potential transmission hubs due to sampling alone. There are statistical methods for drawing inference from partially-observed contact data (e.g., Cross et al 2013;Yang et al 2021), but higher collar densities may also be necessary in some settings (Gilbertson, White, & Craft 2021). Scaling up from individuals to populations often requires information about inter-individual attraction, along with auxiliary data on population densities or group sizes.…”