A framework for combined epidemiological-genomic inference to improve estimation of household model parameters

Abstract

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.