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mContain was developed (and sparsely deployed) by MD2K center at Universityof Memphis in the early stages of COVID-19 pandemic to help reduce communitytransmission in Shelby County and Memphis metropolitan area. The applicationcounts and displays the number of daily proximity encounters with other appusers. To reduce the chances of entering crowded places, users can see thelevel of crowding at busy places on a map. If a user and their COVID-19 testprovider both agree to share the results of their test, the app can notifyother users about possible exposures to COVID-19. The smartphone applicationcollects location and Bluetooth data and sends it to cloud for near real timeprocessing and decisions to be sent back for visualization and interface withthe user. The backend algorithmic infrastructure responsible for real timecrowd estimation and contact tracing from streaming batch data use open-sourcecloud analytics platform Cerebral-Cortex. This project concerns aboutpresenting the authors contributions in the algorithmic development, design andimplementation of mContain application as part of the entire collaborativeproject. We describe the mcontain algorithmic infrastructure and majorcomputational challenges encountered when developing and deploying thisapplication for real-life usage. Details of the app can be found inhttps://mcontain.md2k.org/

Scalable Algorithmic Infrastructure for Computation of Social Crowding and Viral Disease Encounters -- mContain Case Study