Forecasting influenza in Hong Kong with Google search queries and statistical model fusion.

ABSTRACT:

Background

The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources.

Methods

Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates

SUBMITTER: Xu Q 

PROVIDER: S-EPMC5413039 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature