Tracking measles infection through non-linear state space models
Tracking measles infection through non-linear state space models
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DOI：
10.1111/j.1467-9876.2011.01001.x
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发表时间：
2012-01-01
影响因子：
1.6
通讯作者：
Ferrari, Matthew J.
中科院分区：
文献类型：
--
作者：
Chen, Shi;Fricks, John;Ferrari, Matthew J.
． 传染病负担的估计由于漏报病例的普遍趋势而复杂化。在报告率未知的情况下，传统方法依赖于不明确利用监测数据或传播和感染的时间动态的核算方法。状态空间模型是各种方法的框架，允许动态模型与部分或不完全观察到的监视数据相拟合。状态空间模型是一种很有吸引力的负担估计方法，因为它们以底层动态模型的形式结合了专家知识，但明确使用监测数据来估计参数值，预测模型中未观察到的元素，并为估计提供标准误差。
. Estimating the burden of infectious disease is complicated by the general tendency for underreporting of cases. When the reporting rate is unknown, conventional methods have relied on accounting methods that do not make explicit use of surveillance data or the temporal dynamics of transmission and infection. State space models are a framework for various methods that allow dynamic models to be fitted with partially or imperfectly observed surveillance data. State space models are an appealing approach to burden estimation as they combine expert knowledge in the form of an underlying dynamic model but make explicit use of surveillance data to estimate parameter values, to predict unobserved elements of the model and to provide standard errors for estimates.