Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London
Likelihood-based estimation of continuous-time epidemic models from time-series data: application to measles transmission in London
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DOI：
10.1098/rsif.2007.1292
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发表时间：
2008-08-06
影响因子：
3.9
通讯作者：
Ferguson, Neil M.
中科院分区：
文献类型：
--
作者：
Cauchemez, Simon;Ferguson, Neil M.
我们提出了一种新的统计方法来分析流行病的时间序列数据。推断的一个主要困难是：(i)潜在传播过程被部分观察到，（ii）观察到的量在时间上进一步汇总。我们开发了一种数据增强策略来解决这些问题，并引入了一个模拟易感感染性去除（SIR）流行过程的扩散过程，但在分析上更易于处理。虽然基于离散时间模型的方法要求流行病和数据收集过程具有相似的时间尺度，但我们基于连续时间模型的方法不受这种限制。通过模拟数据，我们发现，当观测间隔小于疾病发生时间的2.5倍时，SIR模型的所有参数，包括发生时间都能被准确地估计出来。先前的离散时间TSIR模型假设生成时间等于观测间隔，因此无法估计生成时间。然而，我们无法从历史数据中准确估计麻疹的发生时间。这表明，假设数学流行病学标准类型的均匀混合（甚至考虑年龄结构）的简单模型错过了大群体流行病的关键特征。
We present a new statistical approach to analyse epidemic time-series data. A major difficulty for inference is that (i) the latent transmission process is partially observed and (ii) observed quantities are further aggregated temporally. We develop a data augmentation strategy to tackle these problems and introduce a diffusion process that mimicks the susceptible infectious removed (SIR) epidemic process, but that is more tractable analytically. While methods based on discrete-time models require epidemic and data collection processes to have similar time scales, our approach, based on a continuous-time model, is free of such constraint. Using simulated data, we found that all parameters of the SIR model, including the generation time, were estimated accurately if the observation interval was less than 2.5 times the generation time of the disease. Previous discrete-time TSIR models have been unable to estimate generation times, given that they assume the generation time is equal to the observation interval. However, we were unable to estimate the generation time of measles accurately from historical data. This indicates that simple models assuming homogenous mixing (even with age structure) of the type which are standard in mathematical epidemiology miss key features of epidemics in large populations.