Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009)
Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009)
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DOI：
10.1186/1475-925x-10-15
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发表时间：
2011-02-16
影响因子：
3.9
通讯作者：
Nishiura, Hiroshi
中科院分区：
文献类型：
--
作者：
Nishiura, Hiroshi
背景：流行病的实时预测，特别是那些基于可能性的方法，是研究不足。本研究旨在开发一种简单的方法，可用于实时的流行prediction.Methods：离散时间随机模型，占人口统计随机性和条件测量，被开发和应用作为一个案例研究，每周发病率的大流行性流感（H1N1-2009）在日本。通过采用分支过程近似法并假设每个报告间隔内病例呈线性增长，仅使用两个参数就可预测流行曲线。预测的不确定性界限计算使用链的条件后代distributions.Results：在流行高峰出现之前，预测的质量在很大程度上取决于获得有效的参数估计。每周发病率和最终的流行规模的预测大大提高了在和之后的流行高峰期与所有观察到的数据点落在不确定性bounds.Conclusions：实时预测使用离散时间随机模型，其简单的计算的不确定性界限是成功的。由于模型结构简单，所提出的模型有可能额外考虑到各种类型的异质性，时间依赖性的传播动力学和流行病学的细节。当数据作为疾病监测的一部分出现时，应探讨这种复杂性对预测的影响。
Background: Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting.Methods: A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009) in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions.Results: The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds.Conclusions: Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.