Predicting tick-borne encephalitis using Google Trends

SULYOK, Mihály, RICHTER, Hardy, SULYOK, Zita, KAPITÁNY-FÖVÉNY, Máté and WALKER, Mark D. (2019). Predicting tick-borne encephalitis using Google Trends. Ticks and Tick-borne Diseases, 11 (1), p. 101306.

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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).

Item Type: Article
Additional Information: ** Article version: AM ** Embargo end date: 31-12-9999 ** From Elsevier via Jisc Publications Router ** Licence for AM version of this article: This article is under embargo with an end date yet to be finalised. **Journal IDs: issn 1877959X **History: issue date 22-09-2019; accepted 21-09-2019
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Page Range: p. 101306
SWORD Depositor: Helen Garner
Depositing User: Helen Garner
Date Deposited: 07 Oct 2019 12:26
Last Modified: 18 Mar 2021 03:17

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