Wisnieski, L.; Gruszynski, K.; Faulkner, V.; Shock, B. Challenges and Opportunities in One Health: Google Trends Search Data. Pathogens2023, 12, 1332.
Wisnieski, L.; Gruszynski, K.; Faulkner, V.; Shock, B. Challenges and Opportunities in One Health: Google Trends Search Data. Pathogens 2023, 12, 1332.
Wisnieski, L.; Gruszynski, K.; Faulkner, V.; Shock, B. Challenges and Opportunities in One Health: Google Trends Search Data. Pathogens2023, 12, 1332.
Wisnieski, L.; Gruszynski, K.; Faulkner, V.; Shock, B. Challenges and Opportunities in One Health: Google Trends Search Data. Pathogens 2023, 12, 1332.
Abstract
Google Trends data can be informative for infectious disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we tested the ability of Google Trends search data to predict monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010-2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms as Lyme disease. We built mixed negative binomial models based on a training dataset (2010-2016) and tested the models on a test dataset (2017-2021). A model was built for each search term and monthly lags of search terms were included as predictors. The highest performing models had high predictive ability, indicated by low Root Mean Squared Errors (RMSEs) and close association between observed and predicted case counts. The highest performing model was for the search term “Summer Flu”, which indicates low specificity of some of the terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data.
Keywords
Google Trends; disease prediction; Lyme disease; Lyme; Big Data; One Health; negative binomial; mixed models; zoonotic disease; tick-borne disease
Subject
Public Health and Healthcare, Public, Environmental and Occupational Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
