Mathematical Biosciences and Engineering, 2014, 11(6): 1337-1356. doi: 10.3934/mbe.2014.11.1337

Primary: 92D25, 92D30; Secondary: 34D20, 34C23, 91C99.

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Modeling the impact of twitter on influenza epidemics

1. Department of Mathematics and Computational Science, University of South Carolina Beaufort, Bluffton, SC 29909
2. Department of Communication, University of Connecticut, Storrs, CT 06269
3. Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309

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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