2016/02/15 by Stefania Ottaviano, Francesco De Pellegrini, Ottaviano, Stefania +5
Mathematics · Physics and Astronomy · Psychology · #COVID-19 epidemiological studies #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Mental Health Research Topics #Optimization and Control (math.OC) #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1602.04679
openalex publication_date 2016/02/15 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
The design of an efficient curing policy, able to stem an epidemic process at\nan affordable cost, has to account for the structure of the population contact\nnetwork supporting the contagious process. Thus, we tackle the problem of\nallocating recovery resources among the population, at the lowest cost possible\nto prevent the epidemic from persisting indefinitely in the network.\nSpecifically, we analyze a susceptible-infected-susceptible epidemic process\nspreading over a weighted graph, by means of a first-order mean-field\napproximation. First, we describe the influence of the contact network on the\ndynamics of the epidemics among a heterogeneous population, that is possibly\ndivided into communities. For the case of a community network, our\ninvestigation relies on the graph-theoretical notion of equitable partition; we\nshow that the epidemic threshold, a key measure of the network robustness\nagainst epidemic spreading, can be determined using a lower-dimensional\ndynamical system. Exploiting the computation of the epidemic threshold, we\ndetermine a cost-optimal curing policy by solving a convex minimization\nproblem, which possesses a reduced dimension in the case of a community\nnetwork. Lastly, we consider a two-level optimal curing problem, for which an\nalgorithm is designed with a polynomial time complexity in the network size.\n