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

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    [1] "http://www.statisticbrain.com/twitter-statistics." target="_blank">http://www.statisticbrain.com/twitter-statistics.
    [2] HEALTHINF, SciTePress, (2012), 61-70.
    [3] Epidemiol. Infect., 139 (2011), 68-79.
    [4] Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, (2011), 1568-1576.
    [5] Vaccine, 28 (2010), 2370-2384.
    [6] BMC Medicine, 7 (2009), 45.
    [7] J. Am. Pharm. Assoc., 50 (2010), 745-751.
    [8] (2009).
    [9] PLoS One, 5 (2010), e14118.
    [10] J. Dyn. Differ. Equ., 20 (2008), 31-53.
    [11] Proceedings of the First Workshop on Social Media Analytics, (2012), 115-122.
    [12] The Nation's Health, 39 (2009), 1-10.
    [13] Online J. Issues Nurs., 17 (2012), manuscript 4.
    [14] Nature, 437 (2005), 209-214.
    [15] Nature, 442 (2006), 448-452.
    [16] J. R. Soc. Interface, 7 (2010), 1247-1256.
    [17] PNAS, 106 (2009), 6872-6877.
    [18] Nature, 457 (2009), 1012-1014.
    [19] CMAJ, 181 (2009), 673-680.
    [20] PNAS, 105 (2008), 4639-4644.
    [21] J. Dent. Res., 90(9) (2011), 1047-1051.
    [22] The Fifth International Symposium on Parallel Architectures, Algorithms and Programming, (2012), 100-105.
    [23] Health Information Management Journal, 40 (2011), 33-35.
    [24] in Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, New York, ACM Press, 2007, 56-65.
    [25] Marcel Dekker, Inc., New York and Basel, 1989.
    [26] Regional Conference Series in Applied Mathematics, SIAM, Philadelphia, 1976.
    [27] Vaccine, 28 (2010), 4875-4879.
    [28] Infect. Cont. Hosp. Ep., 28 (2007), 1071-1076.
    [29] Computational and Mathematical Methods in Medicine, (2011), Art. ID 527610, 17 pp.
    [30] Comput. Math. Methods Med., 8 (2007), 153-164.
    [31] Int. J. Biomath., 1 (2008), 65-74.
    [32] Am. J. of Epidemiol., 159 (2004), 623-633.
    [33] Vaccine, 28 (2009), 98-109.
    [34] PLoS One, 5 (2011), e9018.
    [35] BMC Infect. Dis., 9 (2009), 117.
    [36] Pew Research Center's Project for Excellence in Journalism, (2012). Available from: http://stateofthemedia.org/2012/mobile-devices-and-news-consumption-some-good-signs-for-journalism/what-facebook-and-twitter-mean-for-news/.
    [37] Online Marketing Blog, (2008). Available from: http://www.toprankblog.com/2008/05/top-10-twitter-uses/.
    [38] Math. Biosci., 238 (2012), 80-89.
    [39] J. Theoret. Biol., 260 (2009), 31-40.
    [40] PLoS Comput. Biol., 6 (2010), e1000793.
    [41] Am. J. Infect. Control, 38 (2010), 182-188.
    [42] PLoS One, 6 (2011), e19467.
    [43] Pew Internet and American Life Project, (2012). Available from: http://www.pewinternet.com/Reports/2012/Twitter-Use-2012.
    [44] ISRN Biomathematics, 2012 (2012), 1-10.
    [45] BMC Public Health, 11 (2011).
    [46] in Understanding the Dynamics of Emerging and Re-Emerging Infectious Diseases Using Mathematical Models (eds. S. Mushayabasa and C. P. Bhunu), 2012, 157-177.
    [47] Brit. J. Sports Med., 46 (2012), 258-263.
    [48] in Electronic Healthcare, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 69, Springer Berlin Heidelberg, 2010, 18-26.
    [49] 2010.
    [50] Math. Biosci., 180 (2002), 29-48.
    [51] Int. J. of Media Cultural Polit., 7 (2012), 333-348.
    [52] Dermatol. Clin., 27 (2009), 133-136.
    [53] Proceedings of the 13th World Congress on Public Health, (2012).
    [54] PLoS Med., 3 (2006), e361.
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