In this liveProject, you’ll use machine learning to construct a contact tracing network for COVID-19 using location recordings from smart phone data. You’ll read the location of infected individuals, and generate a contact network of individuals who have been within two meters. Once you’ve established this tracing system, you’ll implement a distributed algorithm that can compute the average infection rate for each connected component of the contact network.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for Python data scientists interested in applying big data analytics to public healthcare. To begin this liveProject you will need to be familiar with the following:
- Intermediate Python (declaring variables, loops, branches, debugging, importing modules)
- Basics of Matplotlib
- Basics of NumPy
- Basics of GeoPy
- Basics of NetworkX
you will learn
In this liveProject, you’ll learn how to represent contact networks, and how a network representation lends naturally to efficient algorithms for processing data during pandemics. network. These distributed algorithms are an excellent choice for privacy-preserving machine learning that can be implemented without sharing any local raw data.
- Reading in location recordings from a CSV file
- Determining geodesic distances between locations that are specified by a latitude and a longitude
- Implement a simple algorithm that computes a summary statistic of networked data