The performance of mass testing strategies for COVID-19: a case study for Costa Rica

Introduction: In this article, we derive the behavior of four different mass testing strategies, grounded in guidelines and public health policies issued by the Costa Rican public healthcare system. Objective: To formally develop the changes of each studied mass testing strategy under different contexts related to people’s risk, costs of testing, and accessibility to alternative testing technologies. Methods: We take over a pre-classifier applied to individuals capable of partitioning suspected individuals into low-risk and high-risk groups. We consider the impact of three testing technologies: RT-qPCR, antigen-based testing, and saliva-based testing (RT-LAMP). When available, we introduced a category of essential workers. Results: Numerical simulation results confirm that strategies using only RT-qPCR tests cannot achieve sufficient stock capacity to provide efficient detection regardless of prevalence, sensitivity, or specificity. Strategies that harness the power of pooling and RT-LAMP either maximize stock capacity, detection efficiency, or both. Conclusions: Investing in data quality and classification accuracy can improve the odds of achieving pandemic control and mitigation. Future work will be focused on, based on our findings, constructing representative synthetic data through agent-based modeling and studying the properties of specific pre-classifiers under various scenarios.