Search engines and social media can forecast disease outbreaks

Researchers found association between prevalence of COVID and search queries, posts Internet search engine queries and social media data can be early warning signals, creating a real-time surveillance system for disease forecasting, says a recent University of Waterloo study. Using the example of COVID-19, researchers found there was an association between the disease's prevalence and search engine queries and social media posts. "The general public tends to use internet searches and social media for health information, and especially so during global epidemics," said Dr. Yang (Rena) Yang, a postdoctoral research fellow in the School of Public Health Sciences at Waterloo. "These behaviour patterns can be used by public health authorities to develop a real-time surveillance system to flag when diseases are spiking or waning or respond quickly to emerging infectious diseases." The team extracted symptom keywords from Google Trends and Twitter data in Canada from January to March 2020. These keywords included cough, runny nose, sore throat, shortness of breath, fever, headache, body ache, and fatigue on Google Trends. On Twitter, researchers looked at COVID-19-related hashtags, such as pneumonia, cough, fever, running nose and breath. They then cross-checked the information against COVID-19 data from the COVID-19 Canada Open Data Working Group.