Branching process models for surveillance of infectious diseases controlled by mass vaccination
Branching process models for surveillance of infectious diseases controlled by mass vaccination
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DOI：
10.1093/biostatistics/4.2.279
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发表时间：
2003-04-01
期刊：
影响因子：
2.1
通讯作者：
Gay, NJ
中科院分区：
文献类型：
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作者：
Farrington, CP;Kanaan, MN;Gay, NJ
大规模疫苗接种计划旨在维持低于统一的感染的有效繁殖数R。我们描述了使用监视数据监视R值的方法。模型基于分支过程，其中r用后代平均值识别。我们使用有关暴发大小和暴发持续时间的数据来得出后代平均值的无条件可能性。我们还讨论了由大都会杂货采样实施的贝叶斯方法。我们通过仿真研究模型对易感的耗竭和病例不足的有效性。使用有关麻疹的监视数据来说明这些方法。
Mass vaccination programmes aim to maintain the effective reproduction number R of an infection below unity. We describe methods for monitoring the value of R using surveillance data. The models are based on branching processes in which R is identified with the offspring mean. We derive unconditional likelihoods for the offspring mean using data on outbreak size and outbreak duration. We also discuss Bayesian methods, implemented by Metropolis-Hastings sampling. We investigate by simulation the validity of the models with respect to depletion of susceptibles and under-ascertainment of cases. The methods are illustrated using surveillance data on measles in the USA.