The Role of Movement Patterns in Epidemic Models on Complex Networks

Abstract

In this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.

Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico

“…A particular research work in this area is by [30] who studied the predictability of human displacement and mobility, long-term reconstruction of the society and effective humanitarian relief operations after the Haiti 2010 earthquake. In the health sector, human mobility data has become an integral part of policy formulation by public health officials as regards to the control and ending the spread of infectious diseases [10,16,45,57,44]. Analysis of real world social problems through social networks has been made possible by social graphs induced by mobile communications.…”