Effective resistance against pandemics: Mobility network sparsification for high-fidelity epidemic simulations.

ABSTRACT: Network science has increasingly become central to the field of epidemiology and our ability to respond to infectious disease threats. However, many networks derived from modern datasets are not just large, but dense, with a high ratio of edges to nodes. This includes human mobility networks where most locations have a large number of links to many other locations. Simulating large-scale epidemics requires substantial computational resources and in many cases is practically infeasible. One way to reduce the computational cost of simulating epidemics on these networks is sparsification, where a representative subset of edges is selected based on some measure of their importance. We test several sparsification strategies, ranging from naive thresholding to random sampling of edges, on mobili

SUBMITTER: Mercier A 

PROVIDER: S-EPMC9681106 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature