Rare events of host switching for diseases using a susceptible-infected-recovered model with mutations

We numerically study disease dynamics that lead to the disease switching from one host species to another, resulting in diseases gaining the ability to infect, e.g., humans. Unlike previous studies that focused on branching processes starting with the first infected humans, we begin by considering a disease pathogen that initially cannot infect humans. We model the entire process, starting from an infection in the animal population, including mutations that eventually enable the disease to cause an epidemic outbreak in the human population. We use a susceptible-infected-recovered model on a network consisting of 132 dog and 1320 human nodes, with a single parameter representing the gene of the pathogen. We use numerical large-deviation techniques, specifically the 1/t Wang-Landau algorithm, to calculate the potentially very small probability of the host-switching event. With this approach we are able to resolve probabilities as small as 10^{-120}. Additionally, the 1/t Wang-Landau algorithm allows us to obtain the complete probability density function P(C) of the cumulative fraction C of infected humans, which is an indicator for the severity of the disease in the human population. We also calculate correlations of C with selected quantities q that characterize the outbreak. Due to the application of the rare-event algorithm, this is possible for the entire range of C values.