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Influenza epidemic spread simulation for Poland — a large scale, individual based model study

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  • Rakowski, Franciszek
  • Gruziel, Magdalena
  • Bieniasz-Krzywiec, Łukasz
  • Radomski, Jan P.

Abstract

In this work a construction of an agent based model for studying the effects of influenza epidemic in large scale (38 million individuals) stochastic simulations, together with the resulting various scenarios of disease spread in Poland are reported. Simple transportation rules were employed to mimic individuals’ travels in dynamic route-changing schemes, allowing for the infection spread during a journey. Parameter space was checked for stable behaviour, especially towards the effective infection transmission rate variability. Although the model reported here is based on quite simple assumptions, it allowed to observe two different types of epidemic scenarios: characteristic for urban and rural areas. This differentiates it from the results obtained in the analogous studies for the UK or US, where settlement and daily commuting patterns are both substantially different and more diverse. The resulting epidemic scenarios from these ABM simulations were compared with simple, differential equations based, SIR models — both types of the results displaying strong similarities. The pDYN software platform developed here is currently used in the next stage of the project employed to study various epidemic mitigation strategies.

Suggested Citation

  • Rakowski, Franciszek & Gruziel, Magdalena & Bieniasz-Krzywiec, Łukasz & Radomski, Jan P., 2010. "Influenza epidemic spread simulation for Poland — a large scale, individual based model study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3149-3165.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:16:p:3149-3165
    DOI: 10.1016/j.physa.2010.04.029
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    Cited by:

    1. Ge, Yuanzheng & Song, Zhichao & Qiu, Xiaogang & Song, Hongbin & Wang, Yong, 2013. "Modular and hierarchical structure of social contact networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4619-4628.
    2. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    3. Wang, Xingyuan & Zhao, Tianfang & Qin, Xiaomeng, 2016. "Model of epidemic control based on quarantine and message delivery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 168-178.
    4. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.

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