Birrell Paul J, Zhang Xu-Sheng, Corbella Alice, van Leeuwen Edwin, Panagiotopoulos Nikolaos, Hoschler Katja, Elliot Alex J, McGee Maryia, Lusignan Simon de, Presanis Anne M, Baguelin Marc, Zambon Maria, Charlett André, Pebody Richard G, Angelis Daniela De
MRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR, UK.
National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
BMC Public Health. 2020 Apr 15;20(1):486. doi: 10.1186/s12889-020-8455-9.
Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested.
Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored.
The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3-4 of 2018. Estimates for R were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity.
This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.
自2009年甲型H1N1流感大流行以来，英国公共卫生部利用强化的大流行监测数据开发了一套实时统计模型，用于对未来大流行进行现况估计和预测。其追踪季节性流感以及根据2017年澳大利亚流感高发情况预测冬季医疗负担加重的能力尚未得到检验。
采用四种传播模型对2017/2018年英格兰季节性流感疫情进行预测：一种分层初级保健模型，使用全科医疗中流感样疾病会诊的每日、特定地区计数和病毒学拭子阳性率；一种毒株特异性（SS）模型，使用每周的全国全科医疗流感样疾病和病毒学数据；一种重症监护模型（ICU），使用重症监护病房流感入院报告；以及一个综合了所有数据源的综合模型。在2018年的前12周，将每个模型应用于最新数据，以提供疫情参数估计和短期流感预测。探讨了季前人群易感性数据的附加价值。
综合结果提供了有关疫情状况的有价值的现况估计。对初级和二级卫生服务负担的短期预测最初差异很大，直到在2018年第3 - 4周的活动高峰之后达成共识。在2018年第12周之前，四种模型中的三种模型的R估计值随时间保持一致，并且在SPC和SS模型之间的R估计值以及ICU和综合模型估计的ICU发病率方面存在一致性。估计和预测因假定的季前免疫力水平而异。
本研究成功地将一系列大流行模型应用于季节性流感。在季节早期进行预测仍然具有挑战性，但这是为规划提供信息的一项至关重要的活动。更好地了解预先存在的免疫力水平将很有价值。
