Analysis and modelling of global online public interest in multiple other infectious diseases due to the COVID-19 pandemic.

Rationale: Previous research has demonstrated the applicability of Google Trends in predicting infectious diseases.

Aims and objectives: This study aimed to analyze public interest in other infectious diseases before and after the outbreak of COVID-19 via Google Trends data and to predict these trends via time series models.

Method: Google Trends data for 12 common infectious diseases were obtained in this study, covering the period from 1 February 2018 to 5 May 2023. The ARIMA, TimeGPT, XGBoost, and LSTM algorithms were then utilized to establish time series prediction models.

Results: Our study revealed a significant decrease in public interest in most infectious diseases at the beginning of the pandemic outbreak, followed by a rebound in the post-pandemic era, which is consistent with reported disease incidences. Furthermore, our prediction models demonstrated good accuracy, with TimeGPT showing unique advantages.

Conclusions: Our study highlights the potential application value of Google Trends and large pre-trained models for infectious disease prediction.