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Infection propagator approach to compute epidemic thresholds on temporal networks: impact of immunity and of limited temporal resolution

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  • Eugenio Valdano
  • Chiara Poletto
  • Vittoria Colizza

Abstract

The epidemic threshold of a spreading process indicates the condition for the occurrence of the wide spreading regime, thus representing a predictor of the network vulnerability to the epidemic. Such threshold depends on the natural history of the disease and on the pattern of contacts of the network with its time variation. Based on the theoretical framework introduced in [E. Valdano, L. Ferreri, C. Poletto, V. Colizza, Phys. Rev. X 5, 21005 (2015)] for a susceptible-infectious-susceptible model, we formulate here an infection propagator approach to compute the epidemic threshold accounting for more realistic effects regarding a varying force of infection per contact, the presence of immunity, and a limited time resolution of the temporal network. We apply the approach to two temporal network models and an empirical dataset of school contacts. We find that permanent or temporary immunity do not affect the estimation of the epidemic threshold through the infection propagator approach. Comparisons with numerical results show the good agreement of the analytical predictions. Aggregating the temporal network rapidly deteriorates the predictions, except for slow diseases once the heterogeneity of the links is preserved. Weight-topology correlations are found to be the critical factor to be preserved to improve accuracy in the prediction. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Eugenio Valdano & Chiara Poletto & Vittoria Colizza, 2015. "Infection propagator approach to compute epidemic thresholds on temporal networks: impact of immunity and of limited temporal resolution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(12), pages 1-11, December.
  • Handle: RePEc:spr:eurphb:v:88:y:2015:i:12:p:1-11:10.1140/epjb/e2015-60620-5
    DOI: 10.1140/epjb/e2015-60620-5
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    References listed on IDEAS

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    1. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
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    Cited by:

    1. Eugenio Valdano & Davide Colombi & Chiara Poletto & Vittoria Colizza, 2023. "Epidemic graph diagrams as analytics for epidemic control in the data-rich era," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Han, Dun & Shao, Qi & Li, Dandan & Sun, Mei, 2020. "How the individuals’ risk aversion affect the epidemic spreading," Applied Mathematics and Computation, Elsevier, vol. 369(C).

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