miguelramosfdz / 3rd-position-solution_for_the__african-covid-19__zindi_hackathon

Can we infer important COVID-19 public health risk factors from outdated data? In many countries census and other survey data may be incomplete or out of date.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.

3rd-Position-solution_for_the__African-COVID-19__zindi_hackathon

Can we infer important COVID-19 public health risk factors from outdated data? In many countries census and other survey data may be incomplete or out of date.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.