Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data

PLoS Comput Biol. 2019 Nov 21;15(11):e1007518. doi: 10.1371/journal.pcbi.1007518. eCollection 2019 Nov.


Dengue and influenza-like illness (ILI) are two of the leading causes of viral infection in the world and it is estimated that more than half the world's population is at risk for developing these infections. It is therefore important to develop accurate methods for forecasting dengue and ILI incidences. Since data from multiple sources (such as dengue and ILI case counts, electronic health records and frequency of multiple internet search terms from Google Trends) can improve forecasts, standard time series analysis methods are inadequate to estimate all the parameter values from the limited amount of data available if we use multiple sources. In this paper, we use a computationally efficient implementation of the known variable selection method that we call the Autoregressive Likelihood Ratio (ARLR) method. This method combines sparse representation of time series data, electronic health records data (for ILI) and Google Trends data to forecast dengue and ILI incidences. This sparse representation method uses an algorithm that maximizes an appropriate likelihood ratio at every step. Using numerical experiments, we demonstrate that our method recovers the underlying sparse model much more accurately than the lasso method. We apply our method to dengue case count data from five countries/states: Brazil, Mexico, Singapore, Taiwan, and Thailand and to ILI case count data from the United States. Numerical experiments show that our method outperforms existing time series forecasting methods in forecasting the dengue and ILI case counts. In particular, our method gives a 18 percent forecast error reduction over a leading method that also uses data from multiple sources. It also performs better than other methods in predicting the peak value of the case count and the peak time.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Brazil / epidemiology
  • Dengue / epidemiology*
  • Electronic Health Records
  • Forecasting / methods*
  • Humans
  • Incidence
  • Influenza, Human / epidemiology*
  • Internet / trends
  • Mexico / epidemiology
  • Models, Statistical
  • Population Surveillance / methods
  • Singapore / epidemiology
  • Taiwan / epidemiology
  • Thailand / epidemiology
  • Time Factors
  • United States / epidemiology

Grant support

PR was supported by the Kishore Vaigyanik Protsahan Yojana Fellowship, Government of India. SKM was supported by a Tata Trusts Grant. MM’s research was partially supported by DTRA CNIMS (contract HDTRA1-11-D-0016-0001), NSF DIBBS Grant ACI-1443054, NSF EAGER Grant CMMI-1745207, and NSF BIG DATA Grant IIS-1633028. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.