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Background <p>Extensive population testing played a crucial role in mitigating the COVID-19 pandemic. However, scaling up testing capacity requires a considerable workforce and infrastructure. Furthermore, sampling and testing delays can hinder timely interventions. We therefore sought to improve pre-test triage through an ensemble model based on self-reported information.</p> Methods <p>We trained an XGBoost classifier to predict individual risk of COVID-19 infection for higher education students in Leuven (Belgium) from real-world social and health data related to 38,180 test results. The model could recommend isolation, testing, or release of individuals at high, moderate, or low risk of infection, respectively, based on two parametrizable probability thresholds. We then studied the epidemiological impact of the ensemble triage tool in silico, by simulating its implementation in our context to control an epidemic over time.</p> Results <p>The predictive model achieved a ROC AUC of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(77.5\%\)</EquationSource> </InlineEquation>, but its performance varied across rolling retraining windows. The epidemiological simulations highlight the potential of the ensemble-enhanced triage system to control a surge of infections in the student population of Leuven. Given a rapid implementation at the onset of an infection surge, it could reduce the effective reproduction number below 1.0 while reducing the testing requirements by <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(47\%\)</EquationSource> </InlineEquation>. The predictions of the ensemble model were strongly influenced by the number of contacts which individuals reported, the reason for testing, and the onset of symptoms.</p> Conclusions <p>Our study suggests that pre-test triage guided by ensemble models could play an important role in allocating testing resources efficiently. Given timely implementation and isolation compliance within the population, it could also help rapidly control a surge of infections. Future research could validate this approach for other pathogens, in other settings, and with deep learning models.</p>

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Predictive triage for testing may improve control of a COVID-19 epidemic while reducing testing requirements

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Jonathan Thibaut,
  • Caspar Geenen,
  • Edouard Hosten,
  • Pieter Libin,
  • Katrien Van Dyck,
  • Emmanuel André

摘要

Background

Extensive population testing played a crucial role in mitigating the COVID-19 pandemic. However, scaling up testing capacity requires a considerable workforce and infrastructure. Furthermore, sampling and testing delays can hinder timely interventions. We therefore sought to improve pre-test triage through an ensemble model based on self-reported information.

Methods

We trained an XGBoost classifier to predict individual risk of COVID-19 infection for higher education students in Leuven (Belgium) from real-world social and health data related to 38,180 test results. The model could recommend isolation, testing, or release of individuals at high, moderate, or low risk of infection, respectively, based on two parametrizable probability thresholds. We then studied the epidemiological impact of the ensemble triage tool in silico, by simulating its implementation in our context to control an epidemic over time.

Results

The predictive model achieved a ROC AUC of \(77.5\%\) , but its performance varied across rolling retraining windows. The epidemiological simulations highlight the potential of the ensemble-enhanced triage system to control a surge of infections in the student population of Leuven. Given a rapid implementation at the onset of an infection surge, it could reduce the effective reproduction number below 1.0 while reducing the testing requirements by \(47\%\) . The predictions of the ensemble model were strongly influenced by the number of contacts which individuals reported, the reason for testing, and the onset of symptoms.

Conclusions

Our study suggests that pre-test triage guided by ensemble models could play an important role in allocating testing resources efficiently. Given timely implementation and isolation compliance within the population, it could also help rapidly control a surge of infections. Future research could validate this approach for other pathogens, in other settings, and with deep learning models.