Forecasting the future number of pertussis cases using data from Google Trends.

ABSTRACT:

Background

Alternative methods could be used to enhance the monitoring and forecasting of re-emerging conditions such as pertussis. Here, whether data on the volume of Internet searching on pertussis could complement traditional modeling based solely on reported case numbers was assessed.

Methods

SARIMA models were fitted to describe reported weekly pertussis case numbers over a four-year period in Germany. Pertussis-related Google Trends data (GTD) was added as an external regressor. Predictions were made by the models, both with and without GTD, and compared with values within the validation dataset over a one-year and for a two-weeks period.

Results

Predictions of the traditional model using solely reported case numbers resulted in an RMSE (residual mean squared er

SUBMITTER: Nann D 

PROVIDER: S-EPMC8605298 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature