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Allocating resources for epidemic spreading on metapopulation networks

Author

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  • Zhu, Xuzhen
  • Liu, Yuxin
  • Wang, Shengfeng
  • Wang, Ruijie
  • Chen, Xiaolong
  • Wang, Wei

Abstract

A practical resource allocation strategy is the prerequisite for disease control during a pandemic affected by various external factors, such as the information about the epidemic state, the interregional population mobility, and the geographical factors. Understanding the influence of these factors on resource allocation and epidemic spreading is the premise for designing an optimal resource allocation strategy. To this end, we study the interaction of resource allocation and epidemic spreading in the scope of the metapopulation model by incorporating the factors of geographic proximity, the information of the epidemic state, the willingness of resource allocation, and the population mobility simultaneously. We develop a mathematical framework based on the Markovian chain approach to analyze the dynamical system and obtain the epidemic threshold concerning external factors. Combining extensive Monte Carlo simulations, we find that the disease can be controlled effectively when resources are allocated unbiased in terms of the geographical factor during a pandemic. Specifically, the spreading size is the lowest, and the epidemic threshold is the largest when resources are allocated unbiasedly between neighbor nodes and other nodes. In addition, when studying the effects of resource allocation on the epidemic threshold, we find the same results, i.e., information-aware resource allocation with unbiased in terms of the geographical factor will raise the epidemic threshold. At last, we study the effects of mobility rate on the dynamical property and find an appropriate small value of mobility rate that is propitious to control the disease through numerical analysis and simulations. Our findings will have a direct application in the development of strategies to suppress the spread of the disease and guide the behavior of individuals during a pandemic.

Suggested Citation

  • Zhu, Xuzhen & Liu, Yuxin & Wang, Shengfeng & Wang, Ruijie & Chen, Xiaolong & Wang, Wei, 2021. "Allocating resources for epidemic spreading on metapopulation networks," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    • Handle: RePEc:eee:apmaco:v:411:y:2021:i:c:s0096300321006159
    DOI: 10.1016/j.amc.2021.126531
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    References listed on IDEAS

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    Cited by:

    1. Chen, Peng & Qi, Mingze & Yan, Liang & Duan, Xiaojun, 2024. "Diffusion capacity analysis of complex network based on the cluster distribution," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    2. Wang, Ning-Ning & Qiu, Shui-Han & Zhong, Xiao Wen & Di, Zeng-Ru, 2023. "Epidemic thresholds identification of susceptible-infected-recovered model based on the Eigen Microstate," Applied Mathematics and Computation, Elsevier, vol. 449(C).
    3. Wang, Juquan & Han, Dun, 2023. "Epidemic spreading on metapopulation networks considering indirect contact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    4. Jiang, Jiehui & Ma, Jie, 2023. "Dynamic analysis of pandemic cross-regional transmission considering quarantine strategies in the context of limited medical resources," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    5. Zhang, Kebo & Hong, Xiao & Han, Yuexing & Wang, Bing, 2023. "Optimal discrete resource allocation on metapopulation networks for suppressing spatial spread of epidemic," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

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