The problem we want to solve is one of the most important challenges we face right now, namely: How can we collaborate across countries and regions to ensure that all hospitals have access to an adequate amount of equipment and resources? Given the progress of covid-19 globally, we can expect more regional resource shortages to come.
Co-vidia is a two-pronged approach for ensuring that COVID-19 patients have access to the healthcare they need in a certain region or country. By first applying machine learning to forecast the amount of future ICU cases in each region, we can see where to expect shortages. We then apply mathematical decision optimization techniques to get suggestions for the optimal way to move patients between regions in order to avoid local shortages.
How we built it
The product is a web-app developed with open-source tools in Python deployed in Azure. For machine learning predictions, we used to XGBoost library, for optimization we used PuLP, for the webserver, we used Dash and the graphs were made in Plotly.
Challenges we ran into
Data cleaning was a hazzle (who knew how many odd characters there are in Spanish municipality names?!). Creating an intuitive UI in the webb Responsive CSS is a hassle when paired with Dash. Next time we will use the dash bootstrap components as a base instead of regular CSS.
Accomplishments that we're proud of
We aimed at creating a solution with a professional feeling and believe we have succeded. To get an intuitite product with machine learning and decision optimization all the way to production.
What we learned
What's next for Co-vidia?
Opening up the data platform in order to let users submit their own data to solve problems for their chosen geography.
Try It out