Individual-based Information Dissemination in Multilayer Epidemic Modeling

¹ K–State Epicenter, Department of Electrical and Computer Engineering Kansas State University, Manhattan, KS 66506, USA
² Directorate for Social, Behavioral & Economic Sciences, National Science Foundation Arlington, VA 22230, USA
³ Department of Psychological Sciences, Kansas State University Manhattan, KS 66506, USA

^⋆ Corresponding author. E-mail: caterina@ksu.edu

Abstract

In epidemic modeling, the Susceptible-Alert-Infected-Susceptible (SAIS) model extends the SIS (Susceptible-Infected-Susceptible) model. In the SAIS model, “alert” individuals observe the health status of neighbors in their contact network, and as a result, they may adopt a set of cautious behaviors to reduce their infection rate. This alertness, when incorporated in the mathematical model, increases the range of effective/relative infection rates for which initial infections die out. Built upon the SAIS model, this work investigates how information dissemination further increases this range. Information dissemination is realized through an additional network (e.g., an online social network) sharing the contact network nodes (individuals) with different links. These “information links” provide the health status of one individual to all the individuals she is connected to in the information dissemination network. We propose an optimal information dissemination strategy with an index in quadratic form relative to the information dissemination network adjacency matrix and the dominant eigenvector of the contact network. Numerical tools to exactly solve steady state infection probabilities and influential thresholds are developed, providing an evaluative baseline for our information dissemination strategy. We show that monitoring the health status of a small but “central” subgroup of individuals and circulating their incidence information optimally enhances the resilience of the society against infectious diseases. Extensive numerical simulations on a survey–based contact network for a rural community in Kansas support these findings.