Adapool: Multi-armed bandits for adaptive virology screening on cyber-physical digital-microfluidic biochips

Abstract

Cyber-physical digital microfluidics is a versatile lab-on-chip technology that offers key advantages in reconfigurability, manufacturability, and sensor integration. Critical applications such as point-of-care testing (POCT) are expected to benefit the most from this technology, thus motivating a great body of literature that addresses performance, cost, and reliability using design-automation methodologies. Despite this effort, today's solutions are unable to support the most critical application in the modern era; that is, cost-effective POCT for rapid virology screening. This application poses new design challenges related to the testing capacity and adaptability to the infection distribution within target populations. To support this application, we present a reinforcement-learning method that enables a cyber-physical digital-microfluidic platform to learn from its testing results. The proposed method, named AdaPool, uses multi-armed bandits to infer the dynamics of viral infection and hence adapt the microfluidic system to an effective testing strategy. Simulation results illustrate the effectiveness of the proposed method at different infection conditions.