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

Abstract

African swine fever virus (ASFv) is a virulent pathogen that threatens domestic swine industries globally and persists in wild boar populations in some countries. Persistence in wild boar can challenge elimination and prevent disease-free status, making it necessary to address wild swine in proactive response plans. In the United States, invasive wild pigs are abundant and found across a wide range of ecological conditions that could drive different epidemiological dynamics among populations. Information on the size of the control areas required to rapidly eliminate the ASFv in wild pigs and how this area should change with management constraints and local ecology is needed to optimize response planning. We developed a spatially explicit disease transmission model contrasting wild pig movement and contact ecology in two ecosystems in Southeastern United States. We simulated ASFv spread and determined the optimal response area (reported as the radius of a circle) for eliminating ASFv rapidly over a range of detection times (when ASFv was detected relative to the true date of introduction), culling capacities (proportion of wild pigs in the culling zone removed weekly) and wild pig densities.Large radii for response areas (14 km) were needed under most conditions but could be shortened with early detection (≤ 8 weeks) and high culling capacities (≥ 15% weekly).Under most conditions, the ASFv was eliminated in less than 22 weeks using optimal control radii, although ecological conditions with high rates of wild pig movement required higher culling capacities (≥ 10% weekly) for elimination within 1 year. The results highlight the importance of adjusting response plans based on local ecology and show that wild pig movement is a better predictor of the optimal response area than the number of ASFv cases early in the outbreak trajectory. Our framework provides a tool for determining optimal control plans in different areas, guiding expectations of response impacts, and planning resources needed for rapid elimination.

Impact of Infectious Diseases on Wild Bovidae Populations in Thailand: Insights from Population Modelling and Disease Dynamics

“…Here, we chose a relatively high reintroduction rate ( ∼ 2 per year into the initial population), which likely represents a worst-case scenario. However, to improve this study, we encourage adding the specific environmental factors for each disease and incorporating spatial analyses [85, 86].…”

African swine fever at the wildlife-livestock interface: challenges for management and outbreak response within invasive wild pigs in the United States

“…Radii defining the control area were determined based on observations of wild pig movement distances (63), wild pig contact distances (64,65), and domestic swine disease response policies. However, as evidenced from animal movement studies conducted across a breadth of invaded ecosystems (63)(64)(65), it is likely that the most effective radii differ based on local population attributes and ecological factors (66).…”

“…As a tool to integrate ecosystem-and population-specific knowledge of movement patterns and contact rates into an ASF response, a spatial disease transmission model was developed based on the epidemiological characteristics of genotype II virus circulating in Europe (66). This epidemiological model was used to evaluate the potential impacts of control area size under different ecological conditions and management intensities.…”

“…Modeling efforts highlighted uncertainties in parameters that could improve confidence in predictions of the epidemic duration and spatial spread under different response strategies (66). In particular, feasible rates of removal can vary dramatically depending on local conditions such as ecosystem attributes (e.g., vegetation density or terrain ruggedness), road access, and landownership with potential restrictions for accessing private property.…”

Estimating the effectiveness of control actions on African swine fever transmission in commercial swine populations in the United States

“…Another limiting factor of our model is the paucity of data regarding additional transmission routes, such as the movements of rendering vehicles, the distribution of independent backyard producers, and the density of feral swine (Yoo et al, 2021; Galli et al, 2022; Pepin et al, 2022). As the access to such data becomes available in the future, subsequent iterations of this model should take into consideration the influence that these transmission routes and population dynamics may have on ASF dissemination (Neumann et al, 2021; Cheng and Ward, 2022).…”