Forecasting tuberculosis using diabetes-related google trends data.

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 8^th, 2014, to May 5^th, 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).

SUBMITTER: Frauenfeld L 

PROVIDER: S-EPMC7480530 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature