Dynamic Treatment Allocation for Epidemic Control in Arbitrary Networks

While static epidemic control, e.g. using vaccination, has been extensively studied for various network types, controlling epidemics dynamically remains an open issue. In this work, we first propose a general model formulation for the dynamic treatment allocation problem for the SusceptibleInfected-Susceptible diffusion model. Then, we investigate dynamic control strategies and further propose the novel Largest Reduction in Infectious Edges (LRIE) heuristic that gives priority to the treatment of nodes that have both a high dissemination rate of the infection to many healthy nodes, and low reinfection probability after recovery. Experiments on random and a real-world network show that the dynamic problem is significantly different from vaccination, since the latter strategies can lead to disastrous results, and that the proposed heuristic is an effective strategy under various initial infection conditions.