Epidemic dynamics in homes and destinations under recurrent mobility patterns

Abstract

The structure of heterogeneous networks and human mobility patterns profoundly influence the spreading of endemic diseases. In small-scale communities, individuals engage in social interactions within confined environments, such as homes and workplaces, where daily routines facilitate virus transmission through predictable mobility pathways. Here, we introduce a metapopulation model grounded in a Microscopic Markov Chain Approach to simulate susceptible–infected–susceptible dynamics within structured populations. There are two primary types of nodes, homes and destinations, where individuals interact and transmit infections through recurrent mobility patterns. We derive analytical expressions for the epidemic threshold and validate our theoretical findings through comparative simulations on Watts–Strogatz and Barabási–Albert networks. The experimental results reveal a nonlinear relationship between mobility probability and the epidemic threshold, indicating that further increases can inhibit disease transmission beyond a certain critical mobility level.