Decision support tool to define the optimal pool testing strategy for SARS-CoV-2

This work proposes a holistic decision support tool to be used by diverse stakeholders and decision-makers, integrating experimental data. Considering the characteristics of each region, the tool aids in defining the most suitable testing strategies per country or region: number of tests, time, cost, type of pool strategy based on prevalence rate interval, and the use of centralization vs. decentralization of testing strategies. The tool contributes to the field of decision support systems theoretically by introducing a novel model for testing decisions in extreme situations within a specific region, and practically, by demonstrating how laboratory data can be applied to real-life decision-making in that same region. This tool has been validated with two real case studies (Portugal and France) and has applied, as input data, real experimental results. The results suggested tree- and matrixbased strategies for low and high prevalence rates. A decentralized testing strategy improves the financial and time gains introduced by pool testing; however, centralizing the decisions shows a more straightforward implementation and resource management of the pool testing system with a low-performance loss. Therefore, there might be a solution to consider overcoming operational restrictions.