Abstract
Recent global spread of African swine fever (ASF) have demonstrated the devastating impact this disease can have on the swine industry, posing a threat to the livelihoods of those who rely on it. While significant progress has been made in understanding the disease's dynamics in the newly infected areas, it has been shown that the effectiveness of interventions varies depending on the environmental and socioeconomic context of the affected population. There is no ASF in the United States (US) yet, but it is likely to be introduced given the global burden of the disease. The state of Iowa is characterized by large-scale operations and intense pig trade. Given the significance and uncertainty surrounding the potential impact of introducing ASF into highly dense areas such as Iowa, we have developed a spatial-explicit agent-based model to assess the overall consequences of the disease and the effectiveness of disease control interventions. Population characteristics were aggregated within a hexagonal grid, where the unit of analysis was each cell with distinctive population attributes such as: number of farms, animal density, movement patterns, among others. The model simulates disease transmission at local and long distance, allowing us to incorporate the spatial heterogeneity of the susceptible population. Local disease dynamics are modeled using ordinary differential equations and the long distance dynamics based on simulated movements between farms. The model uses an agent based approach, where the agents are reactive to the current state of their neighborhood and the interventions implemented for disease control. Some interventions evaluated in the model included: preventive culling, movement restrictions, and improving of biosecurity. These interventions were explored in different combinations under different introduction scenarios. Given the high spatio-temporal resolution of the model, it allows us to present the results using maps and detailed transmission networks, evidencing the key agents in the transmission of the disease. Additionally, a global sensitivity analysis was conducted to identify the most influential parameters affecting our model outcomes. While the model incorporates synthetic population and network dynamics, it remains flexible enough to integrate observed data on precise farm locations and movement records, enabling the evaluation of specific scenarios and providing valuable insights to inform risk mitigation strategies. This modeling approach is also sensitive to different levels of confidentiality allowing the user to aggregate the population at different spatial scales without significant impacts in the overall epidemic impacts.