Predicting COVID-19 Hot Spots Using Google Search Trends
The Role of Web-Based Activity Detection Tools
Recent research indicates that Google search trends can be instrumental in identifying potential COVID-19 hot spots. These web-based detection tools are crucial for the early identification of infectious diseases, allowing health care systems to prepare proactively and mitigate the negative impacts of unforeseen outbreaks. In the ongoing pandemic, the ability to predict COVID-19 hot spots can significantly enhance health care planning efforts.
Leveraging Google Trends for Disease Forecasting
Google Trends has emerged as a valuable resource for assessing correlations and developing forecasting models for various infectious diseases, including influenza, Middle East respiratory syndrome (MERS), and Zika virus. A research team led by Dr. Shyam J. Kurian investigated the relationship between public search queries on Google and the number of COVID-19 cases across different states in the U.S. Their study analyzed correlations between new patient data from January 22, 2020, to April 6, 2020, and ten specific keywords.
Key Findings from the Study
The findings of this research were published in the Mayo Clinic Proceedings. The ten keywords examined included:
– COVID symptoms
– Coronavirus symptoms
– Sore throat + shortness of breath + fatigue + cough
– Coronavirus testing centre
– Loss of smell
– Lysol
– Antibody
– Face mask
– Coronavirus vaccine
– COVID stimulus check
Among these keywords, “face mask,” “Lysol,” and “COVID stimulus check” showed the strongest correlations when assessed at a national level. Notably, the researchers found strong correlations up to sixteen days prior to the initial reported cases in certain states. This underscores the effectiveness of using internet search terms for syndromic surveillance in predicting COVID-19 hot spots.
Implications for Health Care Resource Allocation
These insights can significantly aid health care systems in optimizing resource allocation for testing, personal protective equipment, medications, and other essential needs in response to the pandemic.
Reference
Shyam J Kurian, Atiq Ur Rehman Bhatti, Mohammed Ali Alvi, Henry H Ting, Curtis Storlie, Patrick M Wilson, Nilay D Shah, Hongfang Liu, Mohamad Bydon. Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis. Mayo Clin Proc. 2020 Nov;95(11):2370-2381. doi: 10.1016/j.mayocp.2020.08.022. Epub 2020 Aug 20.
Image by Simon Steinberger from Pixabay.