  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/
