The problem What can’t be seen can’t be managed 🔍

We believe testing is our best asset towards normality and this is two-fold:

• Infection testing to control active transmission chains;
• Antibody testing to visualise immunity landscape.

Only a better picture of who’s been infected and who’s imune will enable easing off lockdown and social distancing measures. Testing resouces are scarce and time is more precious than ever.

Our approach Let's be efficient about it! 🎯

Batch testing is a proven method first used in WWII when tests were expensive. We can think of it as if testing light bulbs linked in series: if a single light bulk is broken the whole batch won’t light up.

Highlights:

• Applying the same principle, researchers propose combining multiple patient samples in batches for testing. The question is: what’s a good batch size?;
• Mathematically we can demonstrate that the ideal batch size depends on the infection probability in a given population;
• Using a fixed batch size is usually sub-optimal and could let to a waste of already scarce resources.

As a social project we aim at providing a free, open-access web application for health agents to easily apply these principles and determine this ideal batch size.

Check out our live example on the Optimiser »

This hackathon A first milestone 💪

To promote an efficient use of resources we developed a mathematical model and implemented this in Excel as a proof of concept before designing a web-based working prototype. Main features of our web application:

• Compute infection probability given latest daily testing data (number of tests made and number of positive results);
• Estimate region-specific ideal batch size given the local input data (population and infection probability);
• Display and compare number of tests required for traditional testing and batch testing.

The underlying mathematical model is detailed under the Model »
Solution implemented using the following technologies: .NET, C#, AZURE and JavaScript.

Next steps for our project To infinity and beyond 🚀

1. Promote EU-wide visibility and awareness for use of batch testing and of our support tool;
2. Improve our tool and release better iterations feature-wise (during this pandemic);
3. Develop tools to assist in infection probability estimation and integrate with local databases from healthcare systems (during this pandemic);
4. Apply same principles to other disease testing and improve the efficient use of resources in the health sector (post-pandemic).