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Model of epidemic control based on quarantine and message delivery

Author

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  • Wang, Xingyuan
  • Zhao, Tianfang
  • Qin, Xiaomeng

Abstract

The model provides two novel strategies for the preventive control of epidemic diseases. One approach is related to the different isolating rates in latent period and invasion period. Experiments show that the increasing of isolating rates in invasion period, as long as over 0.5, contributes little to the preventing of epidemic; the improvement of isolation rate in latent period is key to control the disease spreading. Another is a specific mechanism of message delivering and forwarding. Information quality and information accumulating process are also considered there. Macroscopically, diseases are easy to control as long as the immune messages reach a certain quality. Individually, the accumulating messages bring people with certain immunity to the disease. Also, the model is performed on the classic complex networks like scale-free network and small-world network, and location-based social networks. Results show that the proposed measures demonstrate superior performance and significantly reduce the negative impact of epidemic disease.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:458:y:2016:i:c:p:168-178
    DOI: 10.1016/j.physa.2016.04.009
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    1. Zhang, Yue & Zhang, Qingling & Yan, Xing-Gang, 2014. "Complex dynamics in a singular Leslie–Gower predator–prey bioeconomic model with time delay and stochastic fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 180-191.
    2. Eric M. Leroy & Brice Kumulungui & Xavier Pourrut & Pierre Rouquet & Alexandre Hassanin & Philippe Yaba & André Délicat & Janusz T. Paweska & Jean-Paul Gonzalez & Robert Swanepoel, 2005. "Fruit bats as reservoirs of Ebola virus," Nature, Nature, vol. 438(7068), pages 575-576, December.
    3. A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 13(3), pages 547-560, February.
    4. Lagorio, C. & Migueles, M.V. & Braunstein, L.A. & López, E. & Macri, P.A., 2009. "Effects of epidemic threshold definition on disease spread statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 755-763.
    5. Satsuma, J & Willox, R & Ramani, A & Grammaticos, B & Carstea, A.S, 2004. "Extending the SIR epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 369-375.
    6. Ababou, M. & Vandewalle, N. & Moussa, N. & El Bouziani, M. & Ludewig, F., 2013. "Spreading of periodic diseases and synchronization phenomena on networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2526-2531.
    7. 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.
    8. Afassinou, Komi, 2014. "Analysis of the impact of education rate on the rumor spreading mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 43-52.
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