Medical Research Council, Biostatistics Unit - University of Cambridge, School of Clinical Medicine, Cambridge, UK.
Centre for Infectious Disease Surveillance and Control, Public Health England, London, UK.
BMC Public Health. 2018 Jun 26;18(1):790. doi: 10.1186/s12889-018-5671-7.
Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and predict the key transmission features of seasonal and pandemic influenza.
We propose an epidemic model that links the underlying unobserved influenza transmission process to data on severe influenza cases. Within a Bayesian framework, we infer retrospectively the parameters of the epidemic model for each seasonal outbreak from 2012 to 2015, including: the effective reproduction number; the initial susceptibility; the probability of admission to intensive care given infection; and the effect of school closure on transmission. The model is also implemented in real time to assess whether early forecasting of the number of admissions to intensive care is possible.
Our model of admissions data allows reconstruction of the underlying transmission dynamics revealing: increased transmission during the season 2013/14 and a noticeable effect of the Christmas school holiday on disease spread during seasons 2012/13 and 2014/15. When information on the initial immunity of the population is available, forecasts of the number of admissions to intensive care can be substantially improved.
Readily available severe case data can be effectively used to estimate epidemiological characteristics and to predict the evolution of an epidemic, crucially allowing real-time monitoring of the transmission and severity of the outbreak.
流感仍然对卫生系统造成重大负担。有效的应对措施依赖于及时了解疫情的规模和演变。为了监测目的，英格兰严重流感病例的数据每周向英国公共卫生署报告。这些数据既易于获取，又有可能提供有价值的信息，以估计和预测季节性和大流行性流感的关键传播特征。
我们提出了一个将潜在的未观察到的流感传播过程与严重流感病例数据联系起来的传染病模型。在贝叶斯框架内，我们从 2012 年到 2015 年，对每个季节性疫情回溯推断出传染病模型的参数，包括：有效繁殖数；初始易感性；感染后入住重症监护病房的概率；以及学校关闭对传播的影响。该模型还实时实施，以评估是否可以早期预测重症监护病房的入院人数。
我们的入院数据模型允许重建潜在的传播动态，揭示：2013/14 季节传播增加；2012/13 年和 2014/15 年圣诞节学校假期对疾病传播有明显影响。当获得人群初始免疫力的信息时，可以大大改善对重症监护病房入院人数的预测。
现成的严重病例数据可有效用于估计流行病学特征，并预测疫情的演变，关键是可以实时监测疫情的传播和严重程度。
