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

mContain was developed (and sparsely deployed) by MD2K center at University of Memphis in the early stages of COVID-19 pandemic to help reduce community transmission in Shelby County and Memphis metropolitan area. The application counts and displays the number of daily proximity encounters with other app users. To reduce the chances of entering crowded places, users can see the level of crowding at busy places on a map. If a user and their COVID-19 test provider both agree to share the results of their test, the app can notify other users about possible exposures to COVID-19. The smartphone application collects location and Bluetooth data and sends it to cloud for near real time processing and decisions to be sent back for visualization and interface with the user. The backend algorithmic infrastructure responsible for real time crowd estimation and contact tracing from streaming batch data use open-source cloud analytics platform Cerebral-Cortex. This project concerns about presenting the authors contributions in the algorithmic development, design and implementation of mContain application as part of the entire collaborative project. We describe the mcontain algorithmic infrastructure and major computational challenges encountered when developing and deploying this application for real-life usage. Details of the app can be found in https://mcontain.md2k.org/