Skip to main content Accessibility help
×
Home

Individual-based Information Dissemination in Multilayer Epidemic Modeling

  • F.D. Sahneh (a1), F.N. Chowdhury (a2), G. Brase (a3) and C.M. Scoglio (a1)

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.

Copyright

Corresponding author

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

References

Hide All
[1] 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, 12991308.
[2] 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, 228251.
[3] 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, 63066311.
[4] Ferguson, N.. Capturing human behaviour. Nature, 446 (2007), No. 7137, 733.
[5] 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), 12471256.
[6] Funk, S., Gilad, E., Watkins, C., Jansen, V.. The spread of awareness and its impact on epidemic outbreaks. PNAS, 106 (2009), No. 16, 68726877.
[7] W. R. Gilks, S. Richardson, D. J. Spiegelhalter. Markov chain Monte Carlo in practice. Vol. 2, CRC press, 1996.
[8] Givan, O., Schwartz, N., Cygelberg, A., Stone, L.. Predicting epidemic thresholds on complex networks: Limitations of mean-field approaches. J. Theor. Biol., 288 (2011), 2128.
[9] M. J. Keeling, P. Rohani. Modeling infectious diseases in humans and animals. Princeton Univ. Press, 2008.
[10] B. Kosko. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall, Inc., 1991.
[11] B. Lemmens, R. Nussbaum. Nonlinear Perron-Frobenius Theory. Vol. 189, Cambridge University Press, 2012.
[12] 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.
[13] 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, 5981.
[14] Perra, N., Balcan, D., Gonasalves, B., Vespignani, A.. Towards a characterization of behavior-disease models. PLoS ONE, 6 (2011), No. 8, e23084.
[15] P. Poletti. Human behavior in epidemic modelling, Ph.D. thesis, University of Trento, 2010.
[16] Reluga, T.. Game theory of social distancing in response to an epidemic. PLoS Comput. Biol., 6 (2010), No. 5, e1000793.
[17] F. D. Sahneh, C. Scoglio. Epidemic spread in human networks. in: IEEE Decis. Contr. P., (2011), 3008–3013.
[18] 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.
[19] F. D. Sahneh, C. Scoglio. Optimal information dissemination in epidemic networks. in: IEEE Decis. Contr. P., (2012), 1657–1662.
[20] 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, 16091620.
[21] R. Sapolsky. Why Zebras Dont` Get Ulcers. An Updated Guide to Stress, Stress-Related Diseases and Coping. New York: WH Freeman and Company, 1998.
[22] 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.
[23] 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, 10211042.
[24] 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.
[25] Youssef, M., Scoglio, C.. Mitigation of epidemics in contact networks through optimal contact adaptation. Math. Biosci. Eng., 10 (2013), No. 4, 12271251.

Keywords

Individual-based Information Dissemination in Multilayer Epidemic Modeling

  • F.D. Sahneh (a1), F.N. Chowdhury (a2), G. Brase (a3) and C.M. Scoglio (a1)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed