Summary
During the peak of the COVID-19 pandemic, &v-highlight;limited availability of test kits&:v-highlight; made large-scale testing nearly impossible. Parag Shah, CEO of Mechsoft Digital Technologies, developed a probabilistic pool testing algorithm that minimized the number of tests required without compromising accuracy. This innovative approach &v-highlight;reduced testing requirements by up to 85%,&:v-highlight; earning top-rank recognition at the PAN-IIT Innovation Challenge.
Overview
The COVID-19 pandemic placed immense pressure on &link-/industry-specific-solutions/custom-healthcare-software-development-company; healthcare &:link-/industry-specific-solutions/custom-healthcare-software-development-company; systems worldwide. With millions requiring a COVID-19 test and only a limited number of kits available, governments needed smarter ways to manage testing resources. Pool testing, where multiple samples are tested together, offered a potential solution. But optimizing the pool size and configuration based on infection rates required complex probabilistic reasoning.
Business challenge
The primary challenge was balancing &v-highlight;limited testing capacity&:v-highlight; with the need to screen large populations quickly and accurately.
&v-highlight;Key challenges included: &:v-highlight;
- Designing an algorithm that adapts &v-highlight;dynamically to changing infection rates.&:v-highlight;
- Ensuring minimal false negatives while maximizing test efficiency.
- Managing &v-highlight;probability density variations&:v-highlight; as infection concentrations shifted geographically and temporally.
Governments and health organizations sought a model that could maximize test coverage while minimizing kit usage, especially in resource-constrained regions.
The Solution: Probabilistic Pool Testing Algorithm
Parag Shah applied his expertise in complex problem solving and mathematical modeling to design a &v-highlight;probability-based pool testing algorithm&:v-highlight; that addressed these challenges effectively.
&v-highlight;Core Innovations : &:v-highlight;
- &v-highlight;Dynamic Pool Size Adjustment:&:v-highlight; The algorithm automatically recalculated optimal pool sizes based on current positivity rates and population density.
- &v-highlight;Probabilistic Modeling:&:v-highlight; Leveraged Bayesian and &v-highlight;density-based models&:v-highlight; to minimize redundant testing cycles.
- &v-highlight;Efficiency-Driven Design:&:v-highlight; Reduced the number of required tests without increasing false negatives or testing time.
- &v-highlight;Scalable Implementation:&:v-highlight; This model is adaptable accross different regions, testing infrastructures, and outbreak stages.
This algorithm emerged as the best solution at the PAN-IIT Innovation Challenge, standing out among multiple submissions across India.
Results/Impact
- 85% reduction in the number of COVID-19 tests required.
- Enabled faster testing of large populations using limited resources.
- Provided a scalable and adaptable model for government healthcare systems.
- Demonstrated how AI-inspired probabilistic modelling can solve real-world crises.