Modeling the impact of twitter on influenza epidemics

  • Received: 01 December 2013 Accepted: 29 June 2018 Published: 01 September 2014
  • MSC : Primary: 92D25, 92D30; Secondary: 34D20, 34C23, 91C99.

  • Influenza remains a serious public-health problem worldwide. Therising popularity and scale of social networking sites such asTwitter may play an important role in detecting, affecting, andpredicting influenza epidemics. In this paper, we develop a simplemathematical model including the dynamics of ``tweets'' --- short,140-character Twitter messages that may enhance the awareness ofdisease, change individual's behavior, and reduce the transmissionof disease among a population during an influenza season. We analyzethe model by deriving the basic reproductive number and proving thestability of the steady states. A Hopf bifurcation occurs when athreshold curve is crossed, which suggests the possibility ofmultiple outbreaks of influenza. We also perform numericalsimulations, conduct sensitivity test on a few parameters related totweets, and compare modeling predictions with surveillance data ofinfluenza-like illness reported cases and the percentage of tweetsself-reporting flu during the 2009 H1N1 flu outbreak in England andWales. These results show that social media programs like Twittermay serve as a good indicator of seasonal influenza epidemics andinfluence the emergence and spread of the disease.

    Citation: Kasia A. Pawelek, Anne Oeldorf-Hirsch, Libin Rong. Modeling the impact of twitter on influenza epidemics[J]. Mathematical Biosciences and Engineering, 2014, 11(6): 1337-1356. doi: 10.3934/mbe.2014.11.1337

    Related Papers:

  • Influenza remains a serious public-health problem worldwide. Therising popularity and scale of social networking sites such asTwitter may play an important role in detecting, affecting, andpredicting influenza epidemics. In this paper, we develop a simplemathematical model including the dynamics of ``tweets'' --- short,140-character Twitter messages that may enhance the awareness ofdisease, change individual's behavior, and reduce the transmissionof disease among a population during an influenza season. We analyzethe model by deriving the basic reproductive number and proving thestability of the steady states. A Hopf bifurcation occurs when athreshold curve is crossed, which suggests the possibility ofmultiple outbreaks of influenza. We also perform numericalsimulations, conduct sensitivity test on a few parameters related totweets, and compare modeling predictions with surveillance data ofinfluenza-like illness reported cases and the percentage of tweetsself-reporting flu during the 2009 H1N1 flu outbreak in England andWales. These results show that social media programs like Twittermay serve as a good indicator of seasonal influenza epidemics andinfluence the emergence and spread of the disease.


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