The largest outbreak of raccoon rabies in Canada was first reported in Hamilton, Ontario, in 2015 following a probable translocation event from the United States. We used a spatially-explicit agent-based model to evaluate the effectiveness of provincial control programs in an urban-centric outbreak if control interventions were used until 2025, 2020, or never used. Calibration tests suggested that a seroprevalence of protective rabies antibodies 2.1 times higher than that inferred from seroprevalence in program assessments was required in simulations to replicate observed raccoon rabies cases. Our simulation results showed that if control interventions with an adjusted seroprevalence were used until 2025 or 2020, the probability of rabies elimination due to control intervention use was 49.2% and 42.1%, respectively. However, if controls were never used, the probability that initial rabies cases failed to establish a sustained outbreak was only 18.2%. In simulations where rabies was not successfully eliminated, using control interventions until 2025 resulted in 67% fewer new infections compared to only applying controls until 2020 and in 90% fewer new infections compared to no control intervention use. However, the model likely underestimated rabies elimination rates since we did not adjust for adaptive control strategies in response to changes in rabies distributions and case numbers, as well as extending control interventions past 2025. Our agent-based model offers a cost-effective strategy to evaluate approaches to rabies control applications.

In this study, we simulated biologically realistic agent-based models over neutral landscapes to examine how spatial structure affects the spread of a rabies-like virus in a two-species system. We built landscapes with varying autocorrelation levels and simulated disease dynamics using different transmission rates for intra- and interspecies spread. The results were analysed based on combinations of spatial landscape structures and transmission rates, focusing on the median number of new reservoir and spillover cases. We found that both spatial landscape structures and viral transmission rates are key factors in determining the number of infected simulated agents and the epidemiological week when the highest number of cases occurs. While isolated habitat patches with elevated carrying capacity pose significant risks for viral transmission, they may also slow the spread compared to more connected patches, depending on the modelled scenario. This study highlights the importance of spatial landscape structure and viral transmission rates in cross-species spread. Our findings have implications for disease control strategies and suggest that future research should also focus on how landscape factors interact with pathogen dynamics, especially in those locations where susceptible agents could be more in contact with pathogens with high transmission rates.

Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library. The version of SamPy considered in this paper is available at https://github.com/sampy-project/sampy-paper .