Collaborative Research: CIF: Medium: Group testing for Real-Time Polymerase Chain Reactions: From Primer Selection to Amplification Curve Analysis

Group testing is a screening technique that relies on careful combinatorial mixing and testing of batches of samples. By using group testing instead of individual testing, for most problem settings of practical interest, one is guaranteed significant savings in the number of tests performed and consequently, significant reductions in reporting delays and experimental costs. Group testing is especially desirable when monitoring the spread of infectious diseases such as Covid-19, which requires frequent examinations of massive populations. Although many ad-hoc approaches to group testing for infectious diseases have been put forward, little work has addressed the problem of end-to-end group-testing protocol design, which includes the selection of genetic regions for viral/bacterial identification, mathematical modeling and analysis of the test results and the development of guiding protocols for communal testing strategies. The overarching goals of the project are to determine which group-testing methods can actually mitigate the spread of Covid-19 and other diseases and to what extent, to estimate the reduction in the number of infected individuals achievable through the use of pooled real-time polymerase chain reaction (RT-PCR) tests, and to aid in the employment of Mobile Testing Units that can reach geographically remote regions. Other broader societal impacts include increased readiness for fighting future pandemics and training a new cohort of young researchers on interdisciplinary topics involving machine learning, coding theory and bioinformatics.

The project aims to develop specialized machine-learning, combinatorial and information-theoretic methods for (a) identifying genomic regions with predictably low-mutation rates that may be used as amplification primers for gold-standard real-time polymerase chain reactions (RT-PCR) and determining best mixing strategies based on the likelihood of infection; (b) developing adequate models for amplification curves generated by RT-PCR and corresponding test-errors; (c) formulating experimental-protocol-specific non-adaptive and adaptive semiquantitative group testing schemes that account for nonbinary test outcomes; (d) addressing the testing issues associated with high-viral load subjects and heavy-hitter communities; and (e) integrating the mathematical techniques developed into an agent-based model for disease spreading and control in order to assess the potential impact of group testing and recommend effective test-quarantine-retest strategies.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.