Forecasting tuberculosis using diabetes-related google trends data
Leonie Frauenfeld, Dominik Nann, Zita Sulyok, You‐Shan Feng, Mihály Sulyok
Abstract
, 2019. Internet search data were obtained from a Google Trends (GTD) search for 'diabetes' to the corresponding interval. A seasonal autoregressive moving average (SARIMA) model (0,1,1) (1,0,0) [52] was selected to describe the weekly TB case numbers with and without GTD as an external regressor. We cross-validated the SARIMA models to obtain the root mean squared errors (RMSE). We repeated this procedure with autoregressive feed-forward neural network (NNAR) models using 5-fold cross-validation. To simulate a data-poor surveillance setting, we also tested traditional and GTD-extended models against a hold-out dataset using a decreased 52-week-long period with missing values for training. Cross-validation resulted in an RMSE of 20.83 for the traditional model and 18.56 for the GTD-extended model. Cross-validation of the NNAR models showed a mean RMSE of 19.49 for the traditional model and 18.99 for the GTD-extended model. When we tested the models trained on a decreased dataset with missing values, the GTD-extended models achieved significantly better prediction than the traditional models (p < 0.001). The GTD-extended models outperformed the traditional models in all assessed model evaluation parameters. Using online activity-based data regarding diabetes can improve TB forecasting, but further validation is warranted.