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.

中文翻译：

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基于 2021 年哥斯达黎加 Actualidades 调查的机器学习驱动的 COVID-19 早期分类和大规模测试策略

COVID-19 大流行凸显了大规模检测对于减轻病毒传播的重要性。这项研究提出了通过机器学习模型开发的大规模测试策略，该策略根据健康决定因素预测 COVID-19 传染的风险。利用 2021 年哥斯达黎加“Actualidades”调查的数据，我们训练了模型，根据传染风险对个人进行分类。在对四种可能的策略进行理论分析后，我们使用蒙特卡罗模拟对这些策略进行了评估，分析了测试数量、检测到的阳性病例、每人测试和总成本的分布函数。此外，我们还引入了效率和库存能力等指标来评估不同策略的绩效。考虑到疾病患病率为 0.26，我们的分类器的 AUC-ROC 为 0.80，AUC-PR 为 0.59。第四种策略集成了 RT-qPCR、抗原和 RT-LAMP 测试，成为一种经济有效的大规模测试方法，为大流行应对提供了可扩展且适应性强的测试机制。