Modeling Influenza by Modulating Flu Awareness

  • Michael C. SmithEmail author
  • David A. Broniatowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9708)


It is important for public health officials to follow both the incidence of disease and the public’s perception of it, especially in the Internet-connected age. In the specific context of influenza, disease surveillance through social media has proven effective, but public awareness of influenza and its effects are not well understood. We build upon the existing Epstein model of coupled contagion with the aim of including modern media mechanisms for awareness transmission. Our agent-based model captures the unique effects of news media and social media on disease dynamics, and suggests potential areas for policy intervention to modulate the spread of the flu.


Agent-based modeling Influenza Awareness Coupled contagion Surveillance 



The authors would like to acknowledge Dr. Mark Dredze for allowing us access to the HealthTweets awareness trends, and Dr. Joshua Epstein for helpful feedback.


  1. 1.
    Smith, M., Broniatowski, D., Paul, M., Dredze, M.: Tracking public awareness of influenza through Twitter. In: 3rd International Conference on Digital Disease Detection (DDD), Florence, Italy, May 2015. [rapid fire talk]Google Scholar
  2. 2.
    Broniatowski, D.A., Paul, M.J., Dredze, M.: National and local influenza surveillance through twitter: An analysis of the 2012-2013 influenza epidemic. PLoS ONE 8(12), e83672 (2013). CrossRefGoogle Scholar
  3. 3.
    Brownstein, J.S., Freifeld, C.C., Madoff, L.C.: Digital disease detection harnessing the web for public health surveillance. N. Engl. J. Med. 360(21), 2153–2157 (2009). pMID: 19423867. CrossRefGoogle Scholar
  4. 4.
    Corley, C.D., Cook, D.J., Mikler, A.R., Singh, K.P.: Text and structural data mining of influenza mentions in Web and social media. Int. J. Environ. Res. Public Health 7(2), 596–615 (2010)CrossRefGoogle Scholar
  5. 5.
    Diekmann, O., Heesterbeek, J.A.P., Metz, J.A.J.: On the definition and the computation of the basic reproduction ratio r0 in models for infectious diseases in heterogeneous populations. J. Math. Biology 28(4), 365–382 (1990). MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Dredze, M., Cheng, R., Paul, M., Broniatowski, D.: A platform for public health surveillance using twitter. In: AAAI Workshop on the World Wide Web and Public Health Intelligence (2014)Google Scholar
  7. 7.
    Dugas, A.F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Lgusa, T., Rothman, R.E.: Influenza forecasting with google flu trends. PLoS ONE 8(2), e56176 (2013). CrossRefGoogle Scholar
  8. 8.
    Epstein, J.M., Parker, J., Cummings, D., Hammond, R.A.: Coupledcontagion dynamics of fear and disease: Mathematical and computational explorations. PLoS ONE 3(12), e3955 (2008). CrossRefGoogle Scholar
  9. 9.
    Hatfield, E., Cacioppo, J.T., Rapson, R.L.: Emotional contagion. Cambridge University Press, New York (1994)Google Scholar
  10. 10.
    Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. Proc. Royal Soc. London Math. Phys. Eng. Sci. 115(772), 700–721 (1927). CrossRefzbMATHGoogle Scholar
  11. 11.
    Kremer, M.: Integrating behavioral choice into epidemiological models of the aids epidemic. Technical report, National Bureau of Economic Research (1996)Google Scholar
  12. 12.
    Lee, K., Agrawal, A., Choudhary, A.: Real-time disease surveillance using twitter data: Demonstration on flu and cancer. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1474–1477. KDD 2013, NY, USA (2013).
  13. 13.
    Saroop, A., Karnik, A.: Crawlers for social networks amp; structural analysis of twitter. In: 2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application (IMSAA), pp. 1–8, December 2011Google Scholar
  14. 14.
    Smith, M., Broniatowski, D.A., Paul, M.J., Dredze, M.: Towards real-time measurement of public epidemic awareness: Monitoring influenza awareness through twitter. In: AAAI Spring Symposium on Observational Studies through Social Media and Other Human-Generated Content (2016)Google Scholar
  15. 15.
    Wilensky, U.: Netlogo., Centerfor Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999).
  16. 16.
    Yan, Q., Tang, S., Gabriele, S., Wu, J.: Media coverage and hospital notifications: Correlation analysis and optimal media impact duration to manage a pandemic. J. Theor. Biology 390, 1–13 (2016). MathSciNetCrossRefGoogle Scholar
  17. 17.
    Yang, C; Wilensky, U.: Netlogo epidem basic model., Center for ConnectedLearning and Computer-Based Modeling, Northwestern University, Evanston, IL (2011).

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The George Washington UniversityWashington DCUSA

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