Identification of Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets

Identification of Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets

Learn about innovative research using social media data to identify potential Lyme disease cases in this 44-minute seminar presented by Elda Laison from the University of Montreal. Explore how self-reported tweets from around the world are being analyzed to detect and track Lyme disease occurrences as part of the Next Generation Seminar Series hosted by the Fields Institute. Gain insights into the intersection of public health, data science, and social media analytics in this cutting-edge approach to disease surveillance and epidemiology.