The problem AI Driven Covid Risk Analyzer solves

It’s impossible to contain covid-19 without knowing who’s infected, until a safe and effective vaccine is widely available. If we combine machine learning with test pooling, large populations can be tested weekly or even daily, for as low as Rs220 to Rs370 per person. Infrequent testing (monthly seems to be the default in many proposals) or haphazard screening allow active cases to spread the virus for weeks before it’s caught. And the price is still high at around Rs1000 per test. Pooled testing, guided by machine-learning algorithms, can fundamentally change this calculus. In pooled testing, many people’s samples are combined into one. If no virus is detected in the combined sample, that means no one in the pool is infected. The entire pool can be cleared with just one test. But there’s a catch; if anyone in the pool is infected, the test will be positive and more testing will be required to figure out who has the virus. Machine learning can give us the precise individual-level estimates we need to make pooling work, by identifying those likely to test positive and keeping them out of large pools.

Challenges I ran into

Due to limitations on the IBM Cloud Trial plan we were not able to include a large amount of website data. So, we limited it to only use Covid hotspot locations of southern districts of Kerala (Thiruvananthapuram, Kollam, Pathanamthitta, Alappuzha, Kottayam and Idukki).