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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)

Abstract

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.

Keywords

Dengue fever Gaussian process ARMA HMC NOAA 

Notes

Acknowledgements

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.

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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

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