School of Mathematical Sciences, The University of Adelaide, Adelaide, Australia.
PLoS One. 2013 Aug 30;8(8):e73420. doi: 10.1371/journal.pone.0073420. eCollection 2013.
The clinical serial interval of an infectious disease is the time between date of symptom onset in an index case and the date of symptom onset in one of its secondary cases. It is a quantity which is commonly collected during a pandemic and is of fundamental importance to public health policy and mathematical modelling. In this paper we present a novel method for calculating the serial interval distribution for a Markovian model of household transmission dynamics. This allows the use of Bayesian MCMC methods, with explicit evaluation of the likelihood, to fit to serial interval data and infer parameters of the underlying model. We use simulated and real data to verify the accuracy of our methodology and illustrate the importance of accounting for household size. The output of our approach can be used to produce posterior distributions of population level epidemic characteristics.
传染病的临床序列间隔是指在一个索引病例的症状发作日期和其继发病例的症状发作日期之间的时间。它是一种在大流行期间通常会收集的数量，对公共卫生政策和数学建模具有重要意义。在本文中，我们提出了一种计算家庭传播动力学马尔可夫模型的序列间隔分布的新方法。这允许使用贝叶斯 MCMC 方法，通过显式评估似然值，来拟合序列间隔数据并推断基础模型的参数。我们使用模拟和真实数据来验证我们方法的准确性，并说明考虑家庭规模的重要性。我们方法的输出可用于生成人群水平传染病特征的后验分布。
