Work

Machine learning-driven COVID-19 early triage and large-scale testing strategies based on the 2021 Costa Rican Actualidades survey

Year: 2025

Type: article

Abstract: The COVID-19 pandemic underscored the importance of mass testing in mitigating the spread of the virus. This study presents mass testing strategies developed through machine learning models, which predict the risk of COVID-19 contagion based on health determinants. Using the data from the 2021 “Actualidades” survey in Costa Rica, we trained models to classify individuals by contagion risk. After theorize four possible strategies, we evaluated these using Monte Carlo simulations, analyzing the distribution functions for the number of tests, positive cases detected, tests per person, and total costs. Additionally, we introduced the metrics, efficiency and stock capacity , to assess the performance of different strategies. Our classifier achieved an AUC-ROC of 0.80 and an AUC-PR of 0.59, considering a disease prevalence of 0.26. The fourth strategy, which integrates RT-qPCR, antigen, and RT-LAMP tests, emerged as a cost-effective approach for mass testing, offering insights into scalable and adaptable testing mechanisms for pandemic response.

Source: Mathematics in Medical and Life Sciences

DOI: 10.1080/29937574.2025.2494001

Authors: Carlos Pasquier, Maikol Solís, Vivian Vilchez, Santiago Núñez-Corrales

Institutions: Universidad de Costa Rica, University of Illinois Urbana-Champaign

Language: English

Topic: SARS-CoV-2 detection and testing

Subfield: Infectious Diseases

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: Universidad de Costa Rica, Vicerrectoría de Investigación, Creación e Innovación