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

ABSTRACT: 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

SUBMITTER: Rangarajan P 

PROVIDER: S-EPMC6894887 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature