Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates
Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates
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DOI：
10.1007/s00285-007-0081-y
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发表时间：
2007-08-01
影响因子：
1.9
通讯作者：
Giles, Philip R.
中科院分区：
文献类型：
--
作者：
Boys, Richard J.;Giles, Philip R.
SEIR类型的随机房室模型通常用于从部分观测数据中推断流行病过程，其中只有去除时间可用。对于许多流行病，恒定清除率的假设是不合理的。我们开发的模型，其中这些速率是一个随时间变化的阶跃函数的方法。可逆跳MCMC算法描述，允许贝叶斯推断模型参数，特别是那些与阶跃函数。该方法被应用到两个数据集的天花和呼吸道疾病的爆发。分析强调了允许时间依赖性的重要性，通过对比去除时间的预测分布，并将其与观测数据进行比较。
Stochastic compartmental models of the SEIR type are often used to make inferences on epidemic processes from partially observed data in which only removal times are available. For many epidemics, the assumption of constant removal rates is not plausible. We develop methods for models in which these rates are a time-dependent step function. A reversible jump MCMC algorithm is described that permits Bayesian inferences to be made on model parameters, particularly those associated with the step function. The method is applied to two datasets on outbreaks of smallpox and a respiratory disease. The analyses highlight the importance of allowing for time dependence by contrasting the predictive distributions for the removal times and comparing them with the observed data.