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Interacting Epidemics and Coinfection on Contact Networks

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  • M E J Newman
  • Carrie R Ferrario

Abstract

The spread of certain diseases can be promoted, in some cases substantially, by prior infection with another disease. One example is that of HIV, whose immunosuppressant effects significantly increase the chances of infection with other pathogens. Such coinfection processes, when combined with nontrivial structure in the contact networks over which diseases spread, can lead to complex patterns of epidemiological behavior. Here we consider a mathematical model of two diseases spreading through a single population, where infection with one disease is dependent on prior infection with the other. We solve exactly for the sizes of the outbreaks of both diseases in the limit of large population size, along with the complete phase diagram of the system. Among other things, we use our model to demonstrate how diseases can be controlled not only by reducing the rate of their spread, but also by reducing the spread of other infections upon which they depend.

Suggested Citation

  • M E J Newman & Carrie R Ferrario, 2013. "Interacting Epidemics and Coinfection on Contact Networks," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0071321
    DOI: 10.1371/journal.pone.0071321
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    Cited by:

    1. Nie, Yanyi & Zhong, Xiaoni & Lin, Tao & Wang, Wei, 2022. "Homophily in competing behavior spreading among the heterogeneous population with higher-order interactions," Applied Mathematics and Computation, Elsevier, vol. 432(C).
    2. Xiao, Di & Wang, Jun, 2021. "Attitude interaction for financial price behaviours by contact system with small-world network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    3. Jia, Nan & Ding, Li & Liu, Yu-Jing & Hu, Ping, 2018. "Global stability and optimal control of epidemic spreading on multiplex networks with nonlinear mutual interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 93-105.
    4. Meng Liu & Daqing Li & Pengju Qin & Chaoran Liu & Huijuan Wang & Feilong Wang, 2015. "Epidemics in Interconnected Small-World Networks," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-9, March.
    5. Ma, Jing & Li, Dandan & Tian, Zihao, 2016. "Rumor spreading in online social networks by considering the bipolar social reinforcement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 108-115.
    6. Nie, Yanyi & Zhong, Xiaoni & Lin, Tao & Wang, Wei, 2023. "Pathogen diversity in meta-population networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    7. Nie, Yanyi & Li, Wenyao & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Markovian approach to tackle competing pathogens in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    8. Marialisa Scatá & Aurelio La Corte, 2023. "A Complex Insight for Quality of Service Based on Spreading Dynamics and Multilayer Networks in a 6G Scenario," Mathematics, MDPI, vol. 11(2), pages 1-20, January.
    9. Li, WenYao & Xue, Xiaoyu & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Competing spreading dynamics in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    10. Marcella Tambuscio & Diego F. M. Oliveira & Giovanni Luca Ciampaglia & Giancarlo Ruffo, 2018. "Network segregation in a model of misinformation and fact-checking," Journal of Computational Social Science, Springer, vol. 1(2), pages 261-275, September.

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