Objective: To evaluate whether a digital surveillance model using Google Trends is feasible for obtaining accurate data on coronavirus disease 2019 and whether accurate predictions can be made regarding new cases.
Methods: Data on total and daily new cases in each US state were collected from January 22, 2020, to April 6, 2020. Information regarding 10 keywords was collected from Google Trends, and correlation analyses were performed for individual states as well as for the United States overall.
Results: Among the 10 keywords analyzed from Google Trends, face mask, Lysol, and COVID stimulus check had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82, and 0.79, respectively. Lag and lead Pearson correlations were assessed for every state and all 10 keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states.
Conclusion: This study documents the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as coronavirus disease 2019. This information could enable better preparation and planning of health care systems.
Copyright © 2020 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.