Bridging the gap between domain models and computational models: a case study of COVID-19

Novel coronavirus pneumonia (COVID-19) has broken out and spread rapidly in many countries and regions around the world. Since the outbreak, many researchers have proposed propagation models of COVID-19, among which the mainstream computational epidemiology model requires the establishment of a corresponding artificial society model for computational experiments. However, such models tightly coupled domain knowledge about epidemics with computational models and have low reusability. On this basis, we take COVID-19 as our research object and propose a hierarchical modeling framework for epidemic transmission, which describes how to decouple and dock domain models and computational models. This framework consists of three levels: individual capability model and virus model at the individual level, organizational structure and interaction mechanisms between individuals at the organizational level, and intervention model and environmental model design at the social level. The experimental results show that this is an effective hierarchical framework modeling approach for studying transmission mechanisms.