Modeling the Transmission Mitigation Impact of Testing for Infectious Diseases.

Casey Middleton, Daniel B Larremore
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Abstract

A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale-and a model that incorporates test specifications and usage, within-host pathogen dynamics, and human behaviors to estimate TE. Using TE to guide recommendations, we show that today's rapid diagnostics should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus (RSV), but delayed by up to 2d to control omicron-era SARS-CoV-2. Furthermore, while rapid tests are superior to RT-qPCR for control of founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer turnaround times. Finally, we illustrate the model's flexibility by quantifying tradeoffs in the use of post-diagnosis testing to shorten isolation times.

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传染病检测的传播缓解影响建模。
任何专注于传染病检测和及时诊断的项目的一个根本问题是其在减少社区传播方面的有效性。不幸的是,在实践中很难直接估计这种有效性,这提高了可以从第一性原理预测它的数学建模的价值。在这里,我们介绍了检测有效性(TE),它被定义为在人群规模上通过检测和诊断后隔离减少传播的比例,并开发了一个数学模型,根据检测的相互作用、宿主-病原体动力学和任意复杂的检测行为来估计它。虽然我们的模型适用于病原体,但我们通过分析三种呼吸道病原体,即甲型流感、呼吸道合胞病毒(RSV),以及疫苗接种前和疫苗接种后的严重急性呼吸系统综合征冠状病毒2型,量化暴露后、症状后和常规检测场景中的TE,证明了其灵活性。我们发现,TE因策略和病原体而异,最佳测试取决于可用测试的数量和使用时间。这项工作量化了何时以及如何进行检测的权衡,提供了一个灵活的框架来指导当前和未来诊断检测的使用和开发,以控制传染病的传播。
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