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Individual-based Information Dissemination in MultilayerEpidemic Modeling

Published online by Cambridge University Press:  24 April 2014

F.D. Sahneh ,
F.N. Chowdhury ,
G. Brase  and
C.M. Scoglio
F.D. Sahneh
Affiliation:
K–State Epicenter, Department of Electrical and Computer Engineering Kansas State University, Manhattan, KS 66506, USA
F.N. Chowdhury
Affiliation:
Directorate for Social, Behavioral & Economic Sciences, National Science Foundation Arlington, VA 22230, USA
G. Brase
Affiliation:
Department of Psychological Sciences, Kansas State University Manhattan, KS 66506, USA
C.M. Scoglio*
Affiliation:
K–State Epicenter, Department of Electrical and Computer Engineering Kansas State University, Manhattan, KS 66506, USA
*
⋆Corresponding author. E-mail: caterina@ksu.edu
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Abstract

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

Keywords

Epidemic modelinfectious diseaseSAISinformation dissemination

Information

Type
Research Article
Information
Mathematical Modelling of Natural Phenomena , Volume 9 , Issue 2: Epidemics models on networks , 2014 , pp. 136 - 152
Copyright
© EDP Sciences, 2014

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