Issue link: https://geospatial.trimble.com/en/resources/i/1415423
HealthMap brings together disparate data sources, including online news aggregators, eyewitness reports, expert-curated discussions and validated official reports, to achieve a comprehensive view of the current global state of infectious diseases GeoAI to map psychological connect to health ere is a psychological connect to health and social media data analysis too. Li Yunxia, Account Director, Platform Center, SuperMap, explains when a major epidemic comes, the impact of panic on the society may exceed the disease itself. "To this end, it is necessary to track and evaluate the spatial spread of social emotions by analyzing massive social media data. For instance, when facing an epidemic, public behavior might be irrational, highly infectious, and conformable. It is required to build a knowledge base of epidemic-related emotions and to dig out the dynamic evolu- tion of public opinion in time, space and semantics aspects from social media." By using Internet search or social media data as a source, public topic categories related to the epidemic can be obtained by topic models and Machine Learning methods, which helps us to characterize the changes in public sentiments. ese out- comes then contribute to the reveal of the temporal, spatial and semantic distribution characteristics and evolution paerns of public topic views under the COVID-19, he adds. WHEN BIG G FAILED Way back in 2008, Google had tried its hand at Flu Trends by aggregating Google Search queries with the help of AI, before quietly burying it in 2015. The idea behind Google Flu Trends was that by monitoring millions of users' health tracking behaviors online, the large number of Google search queries gathered can be analyzed to reveal if there is the presence of flu-like illness in a population. Google Flu Trends compared these findings to a historic baseline level of influenza activity for its corresponding region and then reports the activity level as either minimal, low, moderate, high, or intense. The initial reports stated that the predictions were 97% accurate when compared with CDC data. However, then subsequent reports asserted that the predictions were sometimes very inaccurate, especially around 2011-2013, when it consistently overestimated flu prevalence. For one interval in the 2012-2013 flu season, it predicted twice as many doctors' visits as the CDC recorded. Insights tool uses AI to scan over 100,000 articles per day in 65 languages, searching for official and unofficial news of infectious dis- ease outbreaks. e platform performs these Big Data analytics every 15 minutes around the clock. A team of subject maer experts then reviews the outputs before publishing on the Insights dashboard. e Insights platform then assesses the risk of spread to locations of interest around the world using data on local and global air travel paerns that draw from over 4 billion flight itineraries per year. www.geospatialworld.net | May-June 2020 38 TECHNOLOGY TRACK