Online Heuristic Approach for Efficient Allocation of Limited COVID-19 Testing Kits

ABSTRACT

Testing kit scarcity plays an important role in aggravating any epidemiological response against pandemics such as COVID-19, especially for resource-constrained countries. Better decision-making tools are essential to assist policymakers in containing the disease from spreading to a large extent despite limited resource availability. We propose a testing kit allocation framework that comprises three components estimation of time-varying prevalence rates using empirical Bayes model, testing kit allocation using multi-armed bandit algorithms, and pooled testing technique to extract the maximum utility from the available testing kits. We conduct simulation experiments based on real-world data and obtain results to demonstrate the enhanced efficiency in detecting COVID-19 cases. We conclude that Bayesian estimation of prevalence coupled with bandit-based allocation performs significantly well. We also identify scenarios under which pooled testing offers a strong advantage.