Predicting tick-borne encephalitis using Google Trends

Abstract

Data generated through public Internet searching offers a promising alternative source of information for monitoring and forecasting of infectious disease. Here future cases of tick-borne encephalitis (TBE) were predicted using traditional weekly case reports, both with and without Google Trends data (GTD). Data on the weekly number of acute, confirmed TBE cases in Germany were obtained from the Robert Koch Institute. Data relating to the volume of Internet searching on TBE was downloaded from the Google Trends website. Data were split into training and validation parts. A SARIMA (0,1,1) (1,1,1) [52] model was used to describe the weekly TBE case number time series. Google Trends Data was used as an external regressor in a second, as optimal identified SARIMA (4,1,1) (1,1,1) [52] model. Predictions for the number of future cases were made with both models and compared with the validation dataset. GTD showed a significant correlation with reported weekly case numbers of TBE (p < 0.0001). A comparison of forecasted values with reported ones resulted in an RMSE (residual mean squared error) of 0.71 for the model without Google search values, and an RMSE of 0.70 for the Google Trends values enhanced model. However, difference between predictive performances was not significant (Diebold Mariano test, p-value = 0.14).

Keywords: ARIMA; Forecasting; Tick-borne encephalitis; Web browser.