Whom to Test? Active Sampling Strategies for Managing COVID-19

TL;DR

This paper introduces adaptive active sampling strategies inspired by machine learning to optimize COVID-19 testing, effectively identifying infected individuals and reducing death rates through dynamic, data-driven methods.

Contribution

It presents a novel adaptive sampling algorithm that integrates contact tracing and location data for efficient COVID-19 testing management.

Findings

The algorithm rapidly traces infected individuals in simulations.

Smart-testing significantly reduces COVID-19 death rates.

The method adapts in real-time to changing data.

Abstract

This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared to current methods such as testing symptomatic individuals with or without contact tracing.