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Author Spotlight: Controlled Human Exposure Model for Tick Research and Lyme Disease Studies

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Predicting tick-borne encephalitis using Google Trends.

Mihály Sulyok¹, Hardy Richter², Zita Sulyok³

¹Department of Pathology and Neuropathology, Eberhard Karls University of Tübingen Liebermeisterstrasse 8, 72076, Tübingen, Germany.

Ticks and Tick-Borne Diseases

|October 19, 2019

Summary

This summary is machine-generated.

Google Trends data (GTD) can help predict future tick-borne encephalitis (TBE) cases. While GTD showed a significant correlation, its inclusion in forecasting models did not significantly improve prediction accuracy compared to traditional methods.

Keywords:

ARIMA Forecasting Tick-borne encephalitis Web browser

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Area of Science:

Epidemiology
Public Health
Data Science

Background:

Infectious disease surveillance traditionally relies on case reports.
Publicly available internet search data offers a novel data source for disease monitoring.
Tick-borne encephalitis (TBE) is a significant public health concern in endemic regions.

Purpose of the Study:

To assess the utility of Google Trends data (GTD) in forecasting future tick-borne encephalitis (TBE) cases in Germany.
To compare the predictive performance of a forecasting model incorporating GTD with a traditional model.
To evaluate the correlation between internet search volume for TBE and reported case numbers.

Main Methods:

Acquired weekly TBE case data from the Robert Koch Institute and TBE-related search volume data from Google Trends.
Developed a SARIMA (0,1,1) (1,1,1) [52] model for TBE case time series.
Integrated GTD as an external regressor into an optimized SARIMA (4,1,1) (1,1,1) [52] model.
Split data into training and validation sets for model evaluation.

Main Results:

Google Trends data demonstrated a statistically significant correlation with reported weekly TBE cases (p < 0.0001).
The model enhanced with GTD achieved a slightly lower RMSE (0.70) compared to the model without GTD (0.71).
The difference in predictive performance between the two models was not statistically significant (Diebold Mariano test, p = 0.14).

Conclusions:

Internet search data, such as GTD, is a valuable and significantly correlated data source for TBE surveillance.
While GTD shows promise, its current integration did not yield a significant improvement in forecasting accuracy for TBE cases.
Further research may explore advanced methods to better leverage internet search trends for infectious disease forecasting.