ABSTRACT
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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In the post-epidemic era, China's urban communities are at the forefront of implementing the whole chain of accurate epidemic prevention and control. However, the uncertainty of COVID-19, the loopholes in community management and people's overly optimistic judgment of the epidemic have led to the frequent rebound of the epidemic and serious consequences. Existing studies have not yet formed a panoramic framework of community anti-epidemic work under the concept of resilience. Therefore, this article first summarizes the current research progress of resilient communities from three perspectives, including ideas and perspectives, theories and frameworks and methods and means, and summarizes the gap of the current research. Then, an innovative idea on the epidemic resilience of urban communities in China is put forward: (1) the evolution mechanism of community anti-epidemic resilience is described through the change law of dynamic networks; (2) the anti-epidemic resilience of urban communities is evaluated or predicted through the measurement criteria; (3) a simulation platform based on Multi-Agent and dynamic Bayesian networks simulates the interactive relationship between "epidemic disturbance-cost constraint--epidemic resilience"; (4) the anti-epidemic strategies are output intelligently to provide community managers with decision-making opinions on community epidemic prevention and control.