Optimization of Multi Two-Level Batch Testing

Abstract

As the COVID-19 pandemic continues to spread, rapid testing could help curb asymptomatic transmission. Thus, it is paramount that the most efficient testing method be identified and implemented, as to reduce the strain on the medical community. This project introduces a novel batch testing method called multi two-level batch testing, which was hypothesized to increase the efficiency of batch testing in terms of minimizing the number of tests performed for a given population. While Dorfman’s two-level and Li’s multi-level batch testing methods already exist, this method offers a novel strategy distinct from existing methods. A Java simulation was created to iteratively compute the number of tests required for each testing method at various percentages of population infection rate, batch sizes, and other parameters specific to each method. Based on this simulation, it can be shown that the multi two-level procedure is more efficient than both the two-level and the multi-level procedures at an infection rate of 0.01, which is the anticipated rate at the University of Florida during the Spring 2021 semester. Additionally, at infection rates between 0.05 and 0.30, the multi two-level batch testing method slightly outperforms multi-level. When the infection rate exceeds 0.30, all methods are unviable and begin to require more tests than necessary to test each person in the population individually. If laboratories implement multi two-level batch testing, they may reduce costs and labor. Additionally, the novel batch testing procedure can be applied to other diseases and future pandemics.