Spatial epidemiological analysis of Lyme disease in southern Ontario utilizing Google Trends searches

Abstract

Lyme disease is of growing concern in Ontario with endemic areas increasing in size. Differential diagnosis of Lyme disease patients should include their exposure status assuming knowledge of high-risk areas. The goal of this study was a spatial analysis of Lyme disease in southern Ontario for the years 2015–2019 with a focus on the association between Lyme disease prevalence and Internet search frequencies recorded by Google Trends. A choropleth map visualized the raw prevalence of Lyme disease across the 28 public health units of southern Ontario. A disease cluster comprising five public health units was identified in eastern Ontario using the flexible scan statistic (standard morbidity ratio = 4.9, p = 0.01). Poisson regression modeling revealed an association between Lyme disease prevalence and the search term “Lyme disease” in Google Trends (p = 0.032). Lyme disease prevalence was correlated with Google Trend searches, with an increase in relative risk by a factor of 1.19 (CI95%: 1.03, 1.39) for every 1% increase in search activity. Knowledge of the existence and location of high-risk or exposure areas for Lyme disease is important to properly diagnose patients. Exploiting the association between Lyme disease and Internet search activity by the population at risk can also further disease surveillance.