Dynamic poisson autoregression for influenza-like-illness case count prediction

Zheng Wang, Prithwish Chakraborty, Sumiko R. Mekaru, John S. Brownstein, Jieping Ye, Naren Ramakrishnan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Influenza-like-illness (ILI) is among of the most common diseases worldwide, and reliable forecasting of the same can have significant public health benefits. Recently, new forms of disease surveillance based upon digital data sources have been proposed and are continuing to attract attention over traditional surveillance methods. In this paper, we focus on short-term ILI case count prediction and develop a dynamic Poisson autoregressive model with exogenous inputs variables (DPARX) for flu forecasting. In this model, we allow the autoregressive model to change over time. In order to control the variation in the model, we construct a model similarity graph to specify the relationship between pairs of models at two time points and embed prior knowledge in terms of the structure of the graph. We formulate ILI case count forecasting as a convex optimization problem, whose objective balances the autoregressive loss and the model similarity regularization induced by the structure of the similarity graph. We then propose an efficient algorithm to solve this problem by block coordinate descent. We apply our model and the corresponding learning method on historical ILI records for 15 countries around the world using a variety of syndromic surveillance data sources. Our approach provides consistently better forecasting results than state-of-the-art models available for short-term ILI case count forecasting.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1285-1294
Number of pages10
Volume2015-August
ISBN (Print)9781450336642
DOIs
StatePublished - Aug 10 2015
Externally publishedYes
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period8/10/158/13/15

Fingerprint

Convex optimization
Public health

Keywords

  • Autoregressive models
  • Flu forecasting
  • Time series methods

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Wang, Z., Chakraborty, P., Mekaru, S. R., Brownstein, J. S., Ye, J., & Ramakrishnan, N. (2015). Dynamic poisson autoregression for influenza-like-illness case count prediction. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 1285-1294). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783291

Dynamic poisson autoregression for influenza-like-illness case count prediction. / Wang, Zheng; Chakraborty, Prithwish; Mekaru, Sumiko R.; Brownstein, John S.; Ye, Jieping; Ramakrishnan, Naren.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August Association for Computing Machinery, 2015. p. 1285-1294.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, Z, Chakraborty, P, Mekaru, SR, Brownstein, JS, Ye, J & Ramakrishnan, N 2015, Dynamic poisson autoregression for influenza-like-illness case count prediction. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. 2015-August, Association for Computing Machinery, pp. 1285-1294, 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015, Sydney, Australia, 8/10/15. https://doi.org/10.1145/2783258.2783291
Wang Z, Chakraborty P, Mekaru SR, Brownstein JS, Ye J, Ramakrishnan N. Dynamic poisson autoregression for influenza-like-illness case count prediction. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August. Association for Computing Machinery. 2015. p. 1285-1294 https://doi.org/10.1145/2783258.2783291
Wang, Zheng ; Chakraborty, Prithwish ; Mekaru, Sumiko R. ; Brownstein, John S. ; Ye, Jieping ; Ramakrishnan, Naren. / Dynamic poisson autoregression for influenza-like-illness case count prediction. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 2015-August Association for Computing Machinery, 2015. pp. 1285-1294
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