Estimating severity from multiple data sources using Bayesian evidence synthesis

在我们为可能发生的COVID-19大流行做准备时，了解疫情的严重程度，即导致住院或死亡等严重事件的感染比例，对于监测和预测流行病医疗保健服务的负担至关重要。这种负担是通过需要全科医生提供初级保健、住院、呼吸支持和/或重症监护的感染者人数来衡量的。严重程度是通过感染严重程度和病例严重风险来量化的，即感染（无论有无症状）或症状性感染（临床病例）导致严重事件的概率。从单一数据集直接观察这些数量具有挑战性，因为不可能在人群中发现和随访每一例病例，特别是在疫情早期，病例计数可能会遗漏许多无症状或轻度病例。使问题更加复杂的是，对于仍在住院的病人，我们还没有时间观察他们是否会康复。我们建议结合来自多个数据集的信息，包括个人和汇总计数，以估计严重程度，同时考虑到缺失病例的挑战和尚未观察结果。

As we prepare for a possible COVID-19 pandemic, understanding the severity of theepidemic, i.e. the proportion of infections that result in a severe event such ashospitalisation or death, is crucial to monitoring and predicting the burden of the epidemicon healthcare services. This burden is measured by the number of people infected whorequire primary care from general practitioners, hospital admission, respiratory supportand/or admission to intensive care. Severity is quantified by infection-severity and caseseverityrisks, namely the probability that an infection (whether with or without symptoms)or a symptomatic infection (clinical case) leads to a severe event. These quantities arechallenging to observe directly from a single dataset, as it is not possible to detect andfollow-up every case in a population, particularly early in the epidemic when case countsmay miss many asymptomatic or mild cases. The problem is compounded by the fact thatfor patients still ill in hospital, we have not yet had time to observe whether they willrecover or not. We propose combining information from multiple datasets, both onindividuals and aggregated counts, to estimate severity while accounting for thechallenges of missing cases and not yet observing outcomes.