Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt on which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance in balancing sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom.

翻译：从COVID-19大流行最初几个月起，病例严重程度及症状范围的变异性便已显现。从临床角度看，症状变异性可能表明感染导致疾病的不同路径/机制，不同路径可能需要不同的治疗方法。对于公共卫生和传播控制而言，社区病例的症状是采取PCR检测和隔离等行动的触发点。然而，症状解读面临挑战，例如如何在个体症状的敏感性与特异性之间取得平衡，同时最大化病例发现并管理检测等有限资源的需求。出于临床和传播控制两方面的考量，我们需要一种能够考虑不同症状表型可能性的方法，而非假设症状仅沿单一维度变化。为解决此问题，我们整合了来自英国常规检测、具有人群代表性的家庭调查以及参与式智能手机监测的四项大规模且多样化的数据集。