The COVID-19 pandemic, in its rampage across the world, has escaped reason, upended countless lives, and only grown as a subject of obscurity. Among mixed messaging and lacking centralized intelligence, growing quantities of public data provide an opportunity to illuminate the inner workings of the crisis.

In the classic fashion of an inventive mind, Cloud Brigade’s Chris Miller saw a challenge and technical solution within heaps of unapplied data. Isolating the disparity between the stories circulating in communities and the data presented by major organizations, Chris decided to take matters into his own hands and thought “How can we use Machine Learning to provide insights that will help public policy makers and officials save lives and keep people healthy during the COVID-19 pandemic?” By applying computational systems and a variety of models from linear regression to Kmeans clustering algorithms, jumbled data revealed astounding insights. Though, as with all forays into statistical analysis, correlation does not imply causation. Chris remarks that the project was purely explorative and therefore abstains from skewing perspectives in favor of particular popular points of view.

With the successful completion of Phase 1 of the COVID-19 Data Exploration dashboard, Chris’ team has created a resource for policy makers and the public alike. In the future, the team is looking forward to building on Machine Learning systems and implementing a Recurrent Neural Network to complement real-time and historic data with predictive insights.