Real-time modelling of a pandemic influenza outbreak
Real-time modelling of a pandemic influenza outbreak
复制标题
DOI：
10.3310/hta21580
复制
发表时间：
2017-10-01
影响因子：
3.6
通讯作者：
De Angelis, Daniela
中科院分区：
文献类型：
--
作者：
Birrell, Paul J.;Pebody, Richard G.;De Angelis, Daniela
背景：实时建模是英国应对大流行性流感爆发的公共卫生反应的重要组成部分。已经开发了基于真实流行病监测数据的流行病重建模型，但该模型需要增强，以提供空间分类的流行病估计，同时确保实时实施的可行性。目标：通过以下方式推进最先进的实时流行病建模：（1）开发现有流行病模型来捕获传播的空间变化，（2）设计有效的计算算法以提供及时的统计分析，以及（3）将上述内容纳入免费软件中。方法：马尔可夫链蒙特卡罗（MCMC）抽样用于使用 2009 年大流行数据从两种候选建模方法中得出贝叶斯统计推断：(1) 平行区域 (PR) 方法，将大流行分为发生在空间不相交区域的非相互作用流行病； (2) 元区域 (MR) 方法，将国家视为单一元群体，其远程接触率由通勤人口普查数据提供。模型辨别是通过后验均值偏差统计以及更多实际考虑来执行的。在实时环境中，研究了使用顺序蒙特卡罗 (SMC) 算法进行实时分析，作为 MCMC 的替代方案，使用旨在严格测试这两种算法的模拟数据。在估计质量和计算负担方面，将 SMC 导出的分析与“黄金标准”MCMC 导出的推论进行比较。结果：PR 方法可以更好、更及时地拟合流行病数据。对大流行感兴趣数量的估计在不同方法中是一致的，在 PR 方法中，在不同地区之间也是一致的（例如，Rc 始终估计为 1.76-1.80，在整个夏季学校假期期间下降了 43-50%）。开发了一种 SMC 方法，该方法需要进行一些调整，以应对大流行干预造成的数据突然“冲击”。这种半自动 SMC 算法在估计精度和及时提供方面均优于 MCMC。英国公共卫生部 (PHE) 已开发并安装了实现所有研究结果的软件，关键工作人员接受了使用培训。 局限性：PR 模型缺乏预测大流行早期阶段感染传播的预测能力，而 MR 模型可能因其依赖通勤数据来描述传播路线而受到限制。随着严重流行病对资源需求的增加，来自一般医疗和住院治疗的数据可能变得不可靠或有偏见。开发的SMC算法是半自动化的；结论：根据目标，本研究发现及时、空间分解、实时的大流行推断是可行的，并且已经开发了一个根据大流行防备计划假设数据的系统，以便快速实施。未来的工作建议；调查大流行干预措施（例如疫苗接种和学校停课）影响的建模研究；利用替代数据源（例如互联网搜索）来增强传统监视；以及正确处理血清学数据中的测试敏感性和特异性，将这种不确定性传播到实时建模中。
Background: Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible.Objectives: To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software.Methods: Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with 'gold-standard' MCMC-derived inferences in terms of estimation quality and computational burden.Results: The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. Rc is consistently estimated to be 1.76-1.80, dropping by 43-50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden 'shock' in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use.Limitations: The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance.Conclusions: Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation.Future work recommendations; Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling.