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An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

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Efficient and targeted COVID-19 border testing via reinforcement learning.

Hamsa Bastani¹, Kimon Drakopoulos², Vishal Gupta³

¹Department of Operations, Information and Decisions, Wharton School, University of Pennsylvania, Philadelphia, PA, USA.

|September 22, 2021

Summary

This summary is machine-generated.

A new reinforcement learning system, Eva, improved COVID-19 detection in travelers by identifying 1.85 times more infected individuals than random testing. This AI system optimized border control by using real-time data, outperforming traditional epidemiological metrics.

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Area of Science:

Artificial Intelligence
Public Health
Epidemiology

Background:

Countries utilized ad hoc border controls during the COVID-19 pandemic, including quarantines and entry restrictions based on population-level epidemiological metrics.
Existing protocols often relied on broad metrics like cases, deaths, or testing positivity rates, which may not accurately reflect traveler-specific risks.

Purpose of the Study:

To design and evaluate a reinforcement learning system (Eva) for real-time COVID-19 screening of international travelers.
To assess Eva's effectiveness in identifying asymptomatic SARS-CoV-2 infected travelers and informing border policies.
To compare Eva's performance against random testing and policies based solely on epidemiological metrics.

Main Methods:

Development and deployment of Eva, a reinforcement learning system, across Greek borders in the summer of 2020.
Eva utilized traveler demographics and historical testing data to allocate limited testing resources.
Performance comparison against modeled counterfactual scenarios, including random surveillance and metric-based testing policies.

Main Results:

Eva identified 1.85 times more asymptomatic, infected travelers than random surveillance testing, with higher rates during peak travel.
Eva detected 1.25-1.45 times more infected travelers than policies relying solely on epidemiological metrics.
Population-level epidemiological metrics showed limited predictive value and significant country-specific variations for traveler prevalence in 2020.

Conclusions:

Reinforcement learning systems like Eva can significantly enhance the detection of infected travelers compared to traditional methods.
Real-time data and AI-driven resource allocation offer a more effective approach to border health security than country-agnostic policies.
The study highlights concerns regarding the reliance on population-level epidemiological metrics for international travel policies.