Social structure defines spatial transmission of African swine fever in wild boar

Abstract

191. African swine fever virus (ASFv) is endemic in wild boar in Eastern Europe, challenging 20 elimination in domestic swine. Estimates of the distances between transmission events 21 are crucial for predicting rates of disease spread to guide allocation of surveillance and 22 23 processes in hosts, but effects of these processes on spread are poorly understood, and 24 inferences often include only one process. 25 2. To understand effects of spatial and social processes on disease dynamics we developed 26 spatially-explicit transmission models with different assumptions about social and/or 27 spatial contact processes. We fit the models to ASFv surveillance data from Eastern 28 Poland from 2014-2015 and evaluated how inclusion of social structure affected 29 inference. 30 3. The model that accounted for social along with spatial processes provided better 31 inference of spatial spread and predicted that ~80% of transmission events are within the 32 same family group. 33 4. The models predicted dramatically different effective reproductive numbers, both in 34 magnitude and variation. 35 5. Specifying contact structure with spatial but not social processes can lead to very 36 different disease dynamics and inference of epidemiological parameters. Uncertainty in 37 these processes should be accounted for in predicting spatial spread in social species. 38 39 KEYWORDS: African swine fever, Effective reproduction number, Spatial transmission kernel, 40 Surveillance, Wild boar, 41 42 45 3 disease spread per transmission event and inform how surveillance, containment, or mitigation 46 strategies should be deployed [1]. For example, information on where cases may arise can inform 47 what spatial radius should be used for ring culling, ring vaccination, spatial quarantine or 48 intensive surveillance [2]. Without detailed genetic data or contact tracing data to reconstruct 49 transmission history, STKs are predominantly estimated indirectly by fitting disease transmission 50 models to available case data [1-3] providing valuable insight for developing intervention 51 strategies [4-7]. However, models often make simplifying assumptions based on the available 52 information that could have negative impacts on policy decisions if the models are not robust to 53 violation of these assumptions. Common assumptions in models for estimating STKs include 54 assuming a single introduction event and assuming observation of all transmission events [8]. 55Methods that account for these processes are important for providing more realistic predictions 56 of spatial spread in systems where these assumptions are violated. 57 Another common issue is that the potential scope of contact heterogeneities is often 58 simplified or lacking, such that uncertainty in model specification cannot be considered despite 59 its potential importance [9]. In non-vector-borne disease systems, key drivers of contact 60 heterogeneities include social [10] and spatial processes [2], yet our understanding of the relative 61 role of these processes in ...

Understanding the transmission of African swine fever in wild boars of South Korea: A simulation study for parameter estimation

“…For the transmission of ASF between habitats, we assumed that infectious WBs and carcasses could affect other susceptible animals in close habitats. A similar approach was applied in a previous study using a spatial transmission kernel to simulate the local transmission (Pepin et al., 2021). In the previous study, the authors showed that the spatial kernel rapidly decays after 0.5 km and concluded that most transmissions occur within 1.5 km.…”

“…Disease simulation modelling is a useful epidemiological tool to examine the dynamics of disease in a population and evaluate the efficiency of different control measures (Vynnycky & White, 2010). Several studies have been conducted to simulate ASF transmission in WB populations for different purposes such as the estimation of ASF transmission rates (Lange & Thulke, 2017; Pepin et al., 2020), elucidation of transmission mechanism (Halasa et al., 2019; Pepin et al., 2021), or support of decision‐making to control the disease (Croft et al., 2020; Gervasi et al., 2020; Lange et al., 2018). Considering the challenges of simulating emerging diseases in wildlife (Lloyd‐Smith et al., 2015), the previous models provide some useful insights to understand how ASF would persist and spread in WB populations.…”

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

“…Previous studies have shown that the probability of ASF occurrence in wild boar populations increases with proximity to previous cases at a coarse spatial scale (>10 km) (Podgórski et al., 2020), while transmission rates appear to be highest at fine scales (<2 km (Pepin et al., 2021). Here, we investigate whether distance‐dependent infection risk is influenced by genetic relatedness at a local spatial scale where relatedness might influence contact structure and, thus, impact disease transmission.…”

“…On the landscape level, ASF prevalence and spread correlate positively with wild boar density (Nurmoja et al., 2017; Podgórski et al., 2020), proportion of forest cover (Dellicour et al., 2020; Podgórski et al., 2020) and negatively with distance to previous cases (Podgórski et al., 2020) and physical barriers to wild boar movement (Dellicour et al., 2020). At fine scales, ASF transmission is likely influenced by a combination of social interactions, movements and spatial distribution of individuals (Pepin et al., 2021).…”

Optimising response to an introduction of African swine fever in wild pigs

“…Our stakeholders included emergency response personnel from the US Department of Agriculture, Veterinary Services, who specified optimisation criteria during a structured discussion. In our previous work, we found that wild pig density, spatial structure and contact structure were key determinants of ASFv spread rates and establishment risk (Pepin et al, 2020(Pepin et al, , 2021. Thus, we investigated the effects of wild pig density and spatial ecology on the optimal response area with wild pig data from two different bioclimatic regions in the United States (fragmented rangeland and wetlands in Florida [FL] and mixed forest in South Carolina [SC]).…”