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Metapopulation epidemic models with a universal mobility pattern on interconnected networks

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  • Huang, Jinyu
  • Chen, Chao

Abstract

The global pandemic of the coronavirus disease 2019 (COVID-19) exemplifies the influence of human mobility on epidemic spreading. A framework called the movement-interaction-return (MIR) model is a model to study the impact of human mobility on epidemic spreading. In this paper, we investigate epidemic spreading in interconnected metapopulation networks. Specifically, we incorporate the human mobility pattern called the radiation model into the MIR model. As a result, the proposed model is more realistic in comparison to the original MIR model. We use the tensorial framework to develop Markovian equations that describe the dynamics of the proposed model on interconnected metapopulation networks. Then we derive the corresponding epidemic thresholds by converting tensors into matrices. Comprehensive numerical simulations confirm our analysis.

Suggested Citation

  • Huang, Jinyu & Chen, Chao, 2022. "Metapopulation epidemic models with a universal mobility pattern on interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    • Handle: RePEc:eee:phsmap:v:591:y:2022:i:c:s0378437121009183
    DOI: 10.1016/j.physa.2021.126692
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    References listed on IDEAS

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    1. Luca Pappalardo & Filippo Simini & Salvatore Rinzivillo & Dino Pedreschi & Fosca Giannotti & Albert-László Barabási, 2015. "Returners and explorers dichotomy in human mobility," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    2. Nagatani, Takashi & Ichinose, Genki & Tainaka, Kei-ichi, 2019. "Epidemic spreading of random walkers in metapopulation model on an alternating graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 350-360.
    3. Gong, Yong-Wang & Song, Yu-Rong & Jiang, Guo-Ping, 2014. "Epidemic spreading in metapopulation networks with heterogeneous infection rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 208-218.
    4. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    5. Huang, Jinyu & Huang, Jiaxuan, 2019. "Epidemic behaviors in weighted networks with core–periphery structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
    6. Filippo Simini & Marta C. González & Amos Maritan & Albert-László Barabási, 2012. "A universal model for mobility and migration patterns," Nature, Nature, vol. 484(7392), pages 96-100, April.
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