AI 中文总结
本研究提出结合流行病学与基因组数据的连续时间马尔可夫链家庭传播模型，解决家庭传播参数可识别性弱的问题，模拟显示该方法可集中高似然区域，提升传播推断效果。
AI 中文摘要
纳入家庭结构（家庭内部与家庭间传播率不同）的模型被广泛应用于传染病流行病学领域。这类模型可利用最终规模数据进行校准，而该数据忽略了传播顺序，因为其不影响最终暴发规模的分布。尤其，许多不同的传播历史会产生相同的最终流行病学结果，导致难以区分家庭内部（internal）与家庭间（external）传播，限制了参数的可识别性。在此，我们针对家庭传播动力学构建了连续时间马尔可夫链（continuous-time Markov chain）模型，其中模型状态空间被扩展为包含描述感染方向与顺序的传播图，同时利用理想化的病原体基因组数据来识别与观测结果相符的传播历史。我们开展了模拟研究，结果显示，与仅基于流行病学数据的模型相比，纳入遗传信息可显著集中高似然区域。具体而言，基因组数据减少了家庭内部与外部传播参数之间的依赖关系，消除了与其弱可识别性相关的特征脊。这些结果表明，经图解析的家庭模型可在保持分析与计算可处理性的同时，改进传播推断。
英文摘要
Models incorporating household structure, with different rates of transmission within and between households, are widely used in infectious disease epidemiology. These models can be calibrated using final-size data in which transmission ordering is ignored because it does not affect the distribution of final outbreak sizes. In particular, many distinct transmission histories produce identical final epidemiological outcomes, making it difficult to distinguish internal (within-household) from external (between-household) transmission and limiting parameter identifiability. Here, we develop a continuous-time Markov chain formulation for household transmission dynamics in which the model state space is expanded to include transmission graphs describing infection direction and order, with idealised pathogen genomic data used to identify the transmission histories compatible with observations. We conduct simulation studies which show that incorporating genetic information substantially concentrates the regions of high likelihood compared with models based on epidemiological data alone. In particular, genomic data reduces the dependence between internal and external transmission parameters, removing the characteristic ridge associated with their weak identifiability. These results demonstrate that graph-resolved household models enable improved transmission inference while maintaining analytical and computational tractability.
