Forecasting tuberculosis using diabetes-related google trends data.

Forecasting tuberculosis using diabetes-related google trends data. Pathog Glob Health. 2020 May 26;:1-6 Authors: Frauenfeld L, Nann D, Sulyok Z, Feng YS, Sulyok M Abstract Online activity-based data can be used to aid infectious disease forecasting. Our aim was to exploit the converging nature of the tuberculosis (TB) and diabetes epidemics to forecast TB case numbers. Thus, we extended TB prediction models based on traditional data with diabetes-related Google searches. We obtained data on the weekly case numbers of TB in Germany from June 8th, 2014, to May 5th, 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 traditi...
Source: Pathogens and Global Health - Category: International Medicine & Public Health Tags: Pathog Glob Health Source Type: research