  COVID-19 has resulted in a public health global crisis. The pandemic control
necessitates epidemic models that capture the trends and impacts on infectious
individuals. Many exciting models can implement this but they lack practical
interpretability. This study combines the epidemiological and network theories
and proposes a framework with causal interpretability in response to this
issue. This framework consists of an extended epidemic model in interconnected
networks and a dynamic structure that has major human mobility. The networked
causal analysis focuses on the stochastic processing mechanism. It highlights
the social infectivity as the intervention estimator between the observable
effect (the number of daily new cases) and unobservable causes (the number of
infectious persons). According to an experiment on the dataset for Tokyo
metropolitan areas, the computational results indicate the propagation features
of the symptomatic and asymptomatic infectious persons. These new
spatiotemporal findings can be beneficial for policy decision making.
