Can we infer important COVID-19 public health risk factors from outdated data? In many countries census and other survey data may be incomplete or out of date.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 error metric for this competition is the Root Mean Squared Error.
For every row in the dataset, submission files should contain 2 columns: ward and target_pct_vunerable.
Your submission file should look like this:
Ward target_pct_vunerable RKX72I7 43.97 HTMSKQH 7.34 NGPVLJR 1.45
This is a learning competition. Aside from knowledge, there are no prizes for this competition.
You will receive 25 points for your first submission and 50 points for your first non-sample submission. You can read more about Zindi points here.
As this is a knowledge competition it will not close.
We reserve the right to update the contest timeline if necessary.
To help make this a more useful Knowledge competition, we have compiled some tutorials and other resources for learning the techniques associated with prediction. We’ll add to this list over the course of the competition, so if you have a good tutorial, a handy video or GitHub repo, or just a pro tip, post it in the discussion forums and we’ll share it here.
Read about the winner's approaches in this blog post.
Here are the top 3 solutions to this challenge.
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This challenge is open to all and not restricted to any country.
Teams and collaboration
You may participate in this competition as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the highest number of submissions among team members at team formation.
Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be disqualified.
Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).
Datasets and packages
The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.
You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.
If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.
You may use pretrained models as long as they are openly available to everyone.
The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.
You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.
Your solution must not infringe the rights of any third party.
Submissions and winning
You may make a maximum of 10 submissions per day.
Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.
Note that there is no public/private leaderboard split for this challenge. Read more about public and private leaderboards in this post.
You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.
Zindi also reserves the right to disqualify you and/or your submissions from any competition if we believe that you violated the rules or violated the spirit of the competition or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.