African data science competition platform Zindi has released the first of its machine learning solutions to help fight the COVID-19 pandemic. The solution, a model that can predict air pollution levels using satellite data, comes from Zindi’s virtual hackathon series #ZindiWeekendz that will run throughout April and May.
“In a time when many university students across Africa and the world cannot attend lectures or even leave their homes, #ZindiWeekendz aims to put those passionate hearts and sharp minds to good use building innovative machine learning solutions to the problems of COVID-19,” says Celina Lee, CEO of Zindi.
During #ZindiWeekendz, data scientists build practical solutions to some of the public health and economic challenges Africa and the world face as a result of the COVID-19 crisis.
Since the beginning of April, Zindi has hosted challenges to map the households in South Africa most vulnerable to COVID-19, to predict air quality from satellite imagery, and to build a model that can identify whether a person in an image is wearing a facemask. Every solution that comes out of these challenges is freely available for anyone wanting to put them to use, and Zindi will also be making its own efforts to get these models used by various organisations.
Zindi is grateful to Microsoft for sponsoring these challenges and contributing to the fight against COVID-19.
Future #ZindiWeekendz challenges will continue to explore ways that data science, machine learning, and artificial intelligence can improve social, economic, and health outcomes of COVID-19. Zindi will continue to work with governments, companies, and non-profit organisations to make sure these solutions end up in the hands of those who need them most. If you’re interested in helping Zindi make a difference, you can join the next #ZindiWeekendz hackathonor suggest a relevant data set.