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. [ABSTRACT FROM AUTHOR]