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Modeling and analysis of a H1N1 model with relapse and effect of Twitter

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  • Huo, Hai-Feng
  • Jing, Shuang-Lin
  • Wang, Xun-Yang
  • Xiang, Hong

Abstract

Twitter can play an important role in the control of influenza epidemics. We introduce a quantitative approach to evaluate the effects of Twitter on the modeling of the spread of influenza epidemics in this paper. Statistically significant correlations between the number of the percentage of tweets that are self-reporting flu and data of influenza-like illness reported cases are found from Pearson correlation and cross-correlation analyses, during the 2009 H1N1 flu outbreak in England. A new H1N1 model with relapse which involves impact of Twitter are also proposed. Stability of all the equilibria of our model are obtained. The occurrence of backward and forward bifurcation are also established. The best-fit parameter values in our model are identified by gray wolf optimizer and nonlinear least square method from the above data. For determining key parameters during the outbreak of the disease with Twitter impact, the uncertainty and sensitivity analyses are explored by using a Latin hypercube sampling (LHS) method and evaluating the partial rank correlation coefficients (PRCCs). Our results show that Twitter reports have important implications for the control of infectious diseases and Twitter can serve as a good indicator of influenza epidemics.

Suggested Citation

  • Huo, Hai-Feng & Jing, Shuang-Lin & Wang, Xun-Yang & Xiang, Hong, 2020. "Modeling and analysis of a H1N1 model with relapse and effect of Twitter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    • Handle: RePEc:eee:phsmap:v:560:y:2020:i:c:s037843712030594x
    DOI: 10.1016/j.physa.2020.125136
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    References listed on IDEAS

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    1. Cai, Yongli & Jiao, Jianjun & Gui, Zhanji & Liu, Yuting & Wang, Weiming, 2018. "Environmental variability in a stochastic epidemic model," Applied Mathematics and Computation, Elsevier, vol. 329(C), pages 210-226.
    2. Wang, Weiming & Cai, Yongli & Ding, Zuqin & Gui, Zhanji, 2018. "A stochastic differential equation SIS epidemic model incorporating Ornstein–Uhlenbeck process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 921-936.
    3. Tailei Zhang & Kai Wang & Xueliang Zhang, 2015. "Modeling and Analyzing the Transmission Dynamics of HBV Epidemic in Xinjiang, China," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-14, September.
    4. Huo, Hai-Feng & Yang, Peng & Xiang, Hong, 2018. "Stability and bifurcation for an SEIS epidemic model with the impact of media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 702-720.
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