To better prepare for the emergence and re-emergence of coronavirus epidemics, we seek to leverage on the availability of common existing digital infrastructure such as the increasingly ubiquitous Wi-Fi networks that can be readily activated to assist in large-scale contact tracing.

This project implements an experimental data warehouse of Wi-Fi sessions for contact tracing and disease outbreak investigation on a large local university campus in Singapore.

This project focuses on analysing Wifi log sessions provided by the IT department of a large local university in Singapore. For more information about the data and data processing routines please see the data README.md file.

docs

This folder contains project documentation.

analysis

Relevant analysis are included here. Each analyst is alocated a sub-directory.

src

Source-code to support project implementation, data wrangling, visualization and analysis. More info below.

bin

Contains executables and routines for managing different parts of the project.

The code is split in different parts as following:

src/agens

Contains the data-warehouse. It includes prepopulated tables and more detailed information about the data. The data-warehouse can be started with the Dockerfile provided.

src/airflow

Provides the ETL tasks. The tasks are defined as Airflow DAGs. Python dependencies to assist with the tasks can be added to requirements.txt. The dependencies are installed when the container is created. To install new dependencies when the container is up, it is possible to do it interactively with docker exec, but make sure to document it in the requirements file.

src/viz

Contains the analytics application which consist of front- and backend servers. The frontend application provides three different functions: crowd sensing, contact tracing and disease simulator (pictured at the top). The application can be started with as a Docker container using the Dockerfile provided.

It is possible to set up a development environment via Docker containers and environment variables.

> docker-compose up -d

This will spin-off a fresh instance of the data warehouse. Configuration parameters should be tweaked in the environment file .env which should be based on .env-dev. After successful container initialization, you will find the database available in $AGENS_PORT, the Airflow UI in $AIRFLOW_PORT and the frontend application in $WEBSERVER_PORT. Both Airflow and the web-application can be accessed from the browser.

docker-compose.yml

This spins up the required Docker containers for this project.

.env-dev

Environment variables. For local deployment, the recommendation is to copy this file to .env and tweak as required while maintaining an authoritative environment file.