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.