Can we infer important COVID-19 public health risk factors from outdated data? In many countries census and other survey data may be incomplete or out of date. This challenge is to develop a proof-of-concept for how machine learning can help governments more accurately map COVID-19 risk in 2020 using old data, without requiring a new costly, risky, and time-consuming on-the-ground survey.
The 2011 census gives us valuable information for determining who might be most vulnerable to COVID-19 in South Africa. However, the data is nearly 10 years old, and we expect that some key indicators will have changed in that time. Building an up-to-date map showing where the most vulnerable are located will be a key step in responding to the disease. A mapping effort like this requires bringing together many different inputs and tools. For this competition, we’re starting small. Can we infer important risk factors from more readily available data?
The task is to predict the percentage of households that fall into a particularly vulnerable bracket - large households who must leave their homes to fetch water - using 2011 South African census data. Solving this challenge will show that with machine learning it is possible to use easy-to-measure stats to identify areas most at risk even in years when census data is not collected.
The Zindi community is joining the fight against COVID-19! #ZindiWeekendz are virtual weekend hackathons hosted by Zindi. This series of #ZindiWeekendz throughout April and May 2020 focuses specifically on COVID-19.
In a time of lockdowns, remote work, and general uncertainty, #ZindiWeekendz offer data scientists the opportunity to continue to develop their skills while contributing to practical, open-source AI solutions to help in the battle against COVID-19.
All winning solutions will be shared as a public good on GitHub. We are committed to supporting partners implement these solutions and encourage anyone who is interested to reach out to us at email@example.com.