Statistical Models of Dengue Fever

  • Hamilton LinkEmail author
  • Samuel N. Richter
  • Vitus J. Leung
  • Randy C. Brost
  • Cynthia A. Phillips
  • Andrea Staid
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)


We use Bayesian data analysis to predict dengue fever outbreaks and quantify the link between outbreaks and meteorological precursors tied to the breeding conditions of vector mosquitos. We use Hamiltonian Monte Carlo sampling to estimate a seasonal Gaussian process modeling infection rate, and aperiodic basis coefficients for the rate of an “outbreak level” of infection beyond seasonal trends across two separate regions. We use this outbreak level to estimate an autoregressive moving average (ARMA) model from which we extrapolate a forecast. We show that the resulting model has useful forecasting power in the 6–8 week range. The forecasts are not significantly more accurate with the inclusion of meteorological covariates than with infection trends alone.


Dengue fever Gaussian process ARMA HMC NOAA 



This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.


  1. 1.
    Taekegn, A., et al.: Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best. Trop. Med. Int. Health 7(10), 851–857 (2002)CrossRefGoogle Scholar
  2. 2.
    Ahmad, T., et al.: Characterizing dengue spread and severity using internet media sources. In: Proceedings of ACM DEV 2013, New York. ACM (2013)Google Scholar
  3. 3.
    Buczak, A.L., Baugher, B., Moniz, L.J., Bagley, T., Babin, S.M., Guven, E.: Ensemble method for dengue prediction. PLoS one 13(1), January 2018CrossRefGoogle Scholar
  4. 4.
    Buczak, A.L., Koshute, P.T., Babin, S.M., Feighner, B.H., Lewis, S.H.: A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data. BMC Med. Inform. Decis. Mak. 12, 124 (2012)CrossRefGoogle Scholar
  5. 5.
    Gubler, D.J.: Desk Encyclopedia of human and medical virology, Chapter Dengue Viruses, pp. 372–382. Academic Press, Boston (2010)Google Scholar
  6. 6.
    Hales, S., de Wet, N., Maindonald, J., Woodward, A.: Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet 360, 830–834 (2002)CrossRefGoogle Scholar
  7. 7.
    Johnson, L.R., et al.: Phenomenological forecasting of disease incidence using heteroskedastic gaussian processes: a dengue case study, August 2017Google Scholar
  8. 8.
    Kirian, M.L., Weintraub, J.M.: Prediction of gastrointestinal disease with over-the-counter diarrheal remedy sales records in the San Francisco Bay Area. BMC Med. Inform. Decis. Mak. 10(1), 39 (2010)CrossRefGoogle Scholar
  9. 9.
    Lauer, S.A., et al.: Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010–2014. In: Proceedings of the National Academy of Sciences (PNAS), February 2018CrossRefGoogle Scholar
  10. 10.
    Masui, H., Kakitani, I., Ujiyama, S., Hashidate, K., Shiono, M., Kudo, K.: Assessing potential countermeasures against the dengue epidemic in non-tropical urban cities. Theor. Biol. Med. Model. 13, 12 (2016)CrossRefGoogle Scholar
  11. 11.
    NOAA. Combating dengue with infectious disease forecasting. Technical report, National Oceanic and Atmospheric Administration, DOC, 5 June 2015. Retrieved from Dengue Forecasting
  12. 12.
    Ray, E.L., Sakrejda, K., Lauer, S.A., Johansson, M.A., Reich, N.G.: Infectious disease prediction with kernel conditional density estimation. Stat. Med. 36(30), 4908–4929 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Rehman, N.A., Kalyanaraman, S., Ahmad, T., Pervaiz, F., Saif, U., Subramanian, L.: Fine-grained dengue forecasting using telephone triage services. Sci. Adv. 2(7), e1501215 (2016)CrossRefGoogle Scholar
  14. 14.
    Sathler, C.: Predictive modeling of dengue fever epidemics: A Neural Network Approach, December 2017Google Scholar
  15. 15.
    Shortridge, J.E., Guikema, S.D.: Public health and pipe breaks in water distribution systems: analysis with internet search volume as a proxy. Water Res. 53, 26–34 (2014)CrossRefGoogle Scholar
  16. 16.
    Simmons, C.P., Farrar, J.J., Nguyen, V.V., Wills, B.: Dengue. N Engl. J Med 366(15), 1423–1432 (2012)CrossRefGoogle Scholar
  17. 17.
    Soebiyanto, R.P., Adimi, F., Kiang, R.K.: Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS One 5(3), e9450 (2010)CrossRefGoogle Scholar
  18. 18.
    WHO. Dengue: Guidelines for diagnosis, treatment, prevention and control. Technical report, WHO/TDR (2009)Google Scholar
  19. 19.
    WHO. Dengue and severe dengue fact sheet. Retrieved from World Health Organization, 29 July 2016
  20. 20.
    Yamana, T.K., Kandula, S., Shaman, J.: Superensemble forecasts of dengue outbreaks. J. R. Soc. 13, (20160410) (2016)CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Hamilton Link
    • 1
    Email author
  • Samuel N. Richter
    • 2
  • Vitus J. Leung
    • 1
  • Randy C. Brost
    • 1
  • Cynthia A. Phillips
    • 1
  • Andrea Staid
    • 1
  1. 1.Sandia National LaboratoriesAlbuquerqueUSA
  2. 2.Missouri University of Science and TechnologyRollaUSA

Personalised recommendations