Testing asymptomatic people for SARS-CoV-2 aims to reduce COVID-19 transmission. Screening programmes’ effectiveness depends upon testing strategy, sample handling logistics, test sensitivity and individual behaviour, in addition to dynamics of viral transmission. The interaction between these factors is not fully characterized. We investigated the interaction between these factors to determine how to optimize reduction of transmission. We estimate that under idealistic assumptions 70% of transmission may be averted, but under realistic assumptions only 7% may be averted. We show that programmes that overwhelm laboratory capacity or reduce isolation of those with minor symptoms have increased transmission compared with those that do not: programmes need to be designed to avoid these issues, or they will be ineffective or even counter-productive. Our model allows optimal selection of whom to test, quantifies the balance between accuracy and timeliness, and quantifies potential impacts of behavioural interventions. We anticipate our model can be used to understand optimal screening strategies for other infectious diseases with substantially different dynamics.

对无症状人群进行 S​​ARS-CoV-2 检测旨在减少 COVID-19 的传播。除了病毒传播的动态之外，筛查计划的有效性还取决于检测策略、样本处理物流、检测敏感性和个人行为。这些因素之间的相互作用尚未完全表征。我们调查了这些因素之间的相互作用，以确定如何优化减少传播。我们估计，在理想假设下，70% 的传播可能会被避免，但在现实假设下，只有 7% 可能会被避免。我们表明，与那些没有的项目相比，那些压倒实验室能力或减少对有轻微症状的人的隔离的项目增加了传播：项目需要设计来避免这些问题，否则它们将无效甚至适得其反。我们的模型允许最佳选择测试对象，量化准确性和及时性之间的平衡，并量化行为干预的潜在影响。我们预计我们的模型可用于了解具有显着不同动态的其他传染病的最佳筛选策略。