Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-22T18:43:31.540Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected]
Get access

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
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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cousens, S., Kanki, B., Toure, S., Diallo, I., Curtis, V.. Reactivity and repeatability of hygiene behaviour: structured observations from burkina faso. Soc. Sci. Med., 43 (1996), No. 9, 1299–1308. CrossRefGoogle Scholar
Del Valle, S., Hethcote, H., Hyman, J., Castillo-Chavez, C.. Effects of behavioral changes in a smallpox attack model. Math. Biosci., 195 (2005), No. 2, 228–251. CrossRefGoogle Scholar
Fenichel, E. P., Castillo-Chavez, C., Ceddia, M., Chowell, G., Parra, P. A. G., Hickling, G. J., Holloway, G., Horan, R., Morin, B., Perrings, C., et al. Adaptive human behavior in epidemiological models. PNAS, 108 (2011), No. 15, 6306–6311. CrossRefGoogle ScholarPubMed
Ferguson, N.. Capturing human behaviour. Nature, 446 (2007), No. 7137, 733. CrossRefGoogle ScholarPubMed
Funk, S., Salath, M., Jansen, V. A. A.. Modelling the influence of human behaviour on the spread of infectious diseases: a review. J. R. Soc. Interface, 7 (2010), 1247–1256. CrossRefGoogle ScholarPubMed
Funk, S., Gilad, E., Watkins, C., Jansen, V.. The spread of awareness and its impact on epidemic outbreaks. PNAS, 106 (2009), No. 16, 6872–6877. CrossRefGoogle Scholar
W. R. Gilks, S. Richardson, D. J. Spiegelhalter. Markov chain Monte Carlo in practice. Vol. 2, CRC press, 1996.
Givan, O., Schwartz, N., Cygelberg, A., Stone, L.. Predicting epidemic thresholds on complex networks: Limitations of mean-field approaches. J. Theor. Biol., 288 (2011), 21–28. CrossRefGoogle ScholarPubMed
M. J. Keeling, P. Rohani. Modeling infectious diseases in humans and animals. Princeton Univ. Press, 2008.
B. Kosko. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall, Inc., 1991.
B. Lemmens, R. Nussbaum. Nonlinear Perron-Frobenius Theory. Vol. 189, Cambridge University Press, 2012.
Machado, L., Wyatt, N., Devine, A., Knight, B.. Action planning in the presence of distracting stimuli: An investigation into the time course of distractor effects. J. Exp. Psychol. Hum. Percept. Perform., 33 (2007), No. 5, 1045. CrossRefGoogle Scholar
Miller, S., Yardley, L., Little, P.. Development of an intervention to reduce transmission of respiratory infections and pandemic flu: Measuring and predicting hand-washing intentions. Psych. Health Med., 17 (2012), No. 1, 59–81. CrossRefGoogle Scholar
Perra, N., Balcan, D., Gonasalves, B., Vespignani, A.. Towards a characterization of behavior-disease models. PLoS ONE, 6 (2011), No. 8, e23084. CrossRefGoogle ScholarPubMed
P. Poletti. Human behavior in epidemic modelling, Ph.D. thesis, University of Trento, 2010.
Reluga, T.. Game theory of social distancing in response to an epidemic. PLoS Comput. Biol., 6 (2010), No. 5, e1000793. CrossRefGoogle Scholar
F. D. Sahneh, C. Scoglio. Epidemic spread in human networks. in: IEEE Decis. Contr. P., (2011), 3008–3013.
Sahneh, F. D., Chowdhury, F. N., Scoglio, C. M.. On the existence of a threshold for preventive behavioral responses to suppress epidemic spreading. Sci. Rep., 2 (2012), 632. CrossRefGoogle ScholarPubMed
F. D. Sahneh, C. Scoglio. Optimal information dissemination in epidemic networks. in: IEEE Decis. Contr. P., (2012), 1657–1662.
Sahneh, F. D., Scoglio, C., Van Mieghem, P.. Generalized epidemic mean-field model for spreading processes over multilayer complex networks. IEEE/ACM Trans. Networking, 21 (2013), No. 5, 1609–1620. CrossRefGoogle Scholar
R. Sapolsky. Why Zebras Dont` Get Ulcers. An Updated Guide to Stress, Stress-Related Diseases and Coping. New York: WH Freeman and Company, 1998.
Scoglio, C., Schumm, W., Schumm, P., Easton, T., Chowdhury, S. R., Sydney, A., Youssef, M.. Efficient mitigation strategies for epidemics in rural regions. PLoS ONE, 5 (2010), No. 7, e11569. CrossRefGoogle ScholarPubMed
Taylor, M., Simon, P. L., Green, D. M., House, T., Kiss, I. Z.. From markovian to pairwise epidemic models and the performance of moment closure approximations. J. Math. Biol., 64 (2012), No. 6, 1021–1042. CrossRefGoogle ScholarPubMed
Tracht, S. M., Del Valle, S. Y., Hyman, J. M.. Mathematical modeling of the effectiveness of facemasks in reducing the spread of novel influenza A (H1N1). PLoS ONE, 5 (2010), No. 2, e9018. CrossRefGoogle Scholar
Youssef, M., Scoglio, C.. Mitigation of epidemics in contact networks through optimal contact adaptation. Math. Biosci. Eng., 10 (2013), No. 4, 1227–1251. CrossRefGoogle ScholarPubMed