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Towards a Characterization of Behavior-Disease Models

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  • Nicola Perra
  • Duygu Balcan
  • Bruno Gonçalves
  • Alessandro Vespignani

Abstract

The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.

Suggested Citation

  • Nicola Perra & Duygu Balcan & Bruno Gonçalves & Alessandro Vespignani, 2011. "Towards a Characterization of Behavior-Disease Models," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-15, August.
  • Handle: RePEc:plo:pone00:0023084
    DOI: 10.1371/journal.pone.0023084
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    References listed on IDEAS

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    1. Neil Ferguson, 2007. "Capturing human behaviour," Nature, Nature, vol. 446(7137), pages 733-733, April.
    2. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
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    Cited by:

    1. Wang, Xinyu & Jia, Danyang & Gao, Shupeng & Xia, Chengyi & Li, Xuelong & Wang, Zhen, 2020. "Vaccination behavior by coupling the epidemic spreading with the human decision under the game theory," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    2. Estrada, Ernesto & Bartesaghi, Paolo, 2022. "From networked SIS model to the Gompertz function," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    3. Shi, Tianyu & Long, Ting & Pan, Yaohui & Zhang, Wensi & Dong, Chao & Yin, Qiuju, 2019. "Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Zhu, Peican & Wang, Xing & Zhi, Qiang & Ma, Jiezhong & Guo, Yangming, 2018. "Analysis of epidemic spreading process in multi-communities," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 231-237.
    5. Heinlein, Bastian & De Domenico, Manlio, 2023. "Unraveling the role of adapting risk perception during the COVID-19 pandemic in Europe," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    6. Anderson, Kerri-Ann & Creanza, Nicole, 2023. "A cultural evolutionary model of the interaction between parental beliefs and behaviors, with applications to vaccine hesitancy," Theoretical Population Biology, Elsevier, vol. 152(C), pages 23-38.

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