Heterogeneous epidemic modelling within an enclosed space and corresponding Bayesian estimation

Since March 11th, 2020, COVID-19 has been a global pandemic for more than one years due to a long and infectious incubation period. This paper establishes a heterogeneous epidemic model that divides the incubation period into infectious and non-infectious and employs the Bayesian framework to model the ‘Diamond Princess’ enclosed space incident. The heterogeneity includes two different identities, two transmission methods, two different-size rooms, and six transmission stages. This model is also applicable to similar mixed structures, including closed schools, hospitals, and communities. As the COVID-19 pandemic continues, our mathematical modeling can provide management insights to the governments and policymakers on how the COVID-19 disease has spread and what prevention strategies still need to be taken. •A completely data-driven linear heterogeneous epidemic model for a relatively enclosed space composed of subspaces up to three people is developed.•The heterogeneity of the model includes the following five aspects: two different identities, two infection sources, two transmission methods, two different-size rooms, and six transmission stages.•A probabilistic algorithm to calculate the change in the subspace distribution of people is constructed.•A simple method is introduced to calculate the basic reproduction number R0 for different infection sources.•An improved approximate Bayesian updating computation method based on entirely non-informative priors that simultaneously infers any number of unknown parameters is proposed.