Abstract
Mathematical models of disease spreading are a key factor in ensuring that we are prepared to deal with the next epidemic. They allow us to predict how an infection will spread throughout a population, thereby allowing us to make intelligent choices when attempting to contain a disease. Whether due to a lack of empirical data, a lack of computational power, a lack of biological understanding, or some combination thereof, traditional models must make sweeping, unrealistic assumptions about the behavior of a population during an epidemic.
We present the results of granular epidemic simulations using a rich social network constructed from real-world interactions, demonstrating the effects of ten potential vaccination strategies. We confirm estimates by the WHO and the CDC regarding the virulence of measles-like diseases, and we show how representing a population as a temporal graph and applying existing graph metrics can lead to more effective interventions.
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References
Borradaile, G., Migler, T., Wilfong, G.: Density decompositions of networks. In: Cornelius, S., Coronges, K., Gonçalves, B., Sinatra, R., Vespignani, A. (eds.) Complex Networks IX, pp. 15–26. Springer, Cham (2018)
CDC (Centers for Disease Control and Prevention). Measles|cases and outbreaks—CDC. Accessed 16 May 2019. https://web.archive.org/web/20190516082526/https://www.cdc.gov/measles/cases-outbreaks.html
CDC (Centers for Disease Control and Prevention). Measles|vaccination—CDC. Accessed 16 May 2019. https://web.archive.org/web/20190516074529/https://www.cdc.gov/measles/vaccination.html
CDC (Centers for Disease Control and Prevention). Pinkbook—measles—epidemiology of vaccine-preventable disease—cdc. Accessed 14 May 2019. https://web.archive.org/web/20190514200016/https://www.cdc.gov/vaccines/pubs/pinkbook/meas.html
Fenner, F., Henderson, D.A., Arita, I., Jezek, Z., Ladnyi, I.D.: Smallpox and its eradication. Number 6 in History of International Public Health. World Health Organization, Geneva (1988)
FrÃas-MartÃnez, E., Williamson, G., FrÃas-MartÃnez, V.: An agent-based model of epidemic spread using human mobility and social network information. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp. 57–64, October 2011
Gates, B.: The next epidemic—lessons from Ebola. N. Engl. J. Med. 372(15), 1381–1384 (2015). PMID: 25853741
Gemmetto, V., Barrat, A., Cattuto, C.: Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infect. Dis. 14(1), 695 (2014)
Granell, C., Mucha, P.J.: Epidemic spreading in localized environments with recurrent mobility patterns. Phys. Rev. E 97(5), 5 (2018)
Kephart, J.O., White, S.R.: Directed-graph epidemiological models of computer viruses. In: Proceedings. 1991 IEEE Computer Society Symposium on Research in Security and Privacy, pp. 343–359, May 1991
Rota, P.A., Moss, W.J., Takeda, M., de Swart, R.L., Thompson, K.M., Goodson, J.L.: Measles. Nat. Rev. Dis. Primers 2, 16049 (2016). EP – 07
Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)
Stopczynski, A., Pentland, A.‘Sandy’., Lehmann, S.: How physical proximity shapes complex social networks. Sci. Rep. 8(1), 17722 (2018)
Stopczynski, A., Sekara, V., Sapiezynski, P., Cuttone, A., Madsen, M.M., Larsen, J.E., Lehmann, S.: Measuring large-scale social networks with high resolution. PLoS ONE 9(4), 1–24 (2014)
WHO (World Health Organization). Measles vaccines: WHO position paper—April 2017. Wkly. Epidemiol. Rec. 92(17), 205–228 2017
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Siu, C., Migler, T. (2020). Vaccination Strategies on a Robust Contact Network. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_26
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DOI: https://doi.org/10.1007/978-3-030-36687-2_26
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