Multi-scale modeling for the transmission of XXXX and the evaluation of interventions toward it.

@entity1 @entity3082 @entity683 @entity281 @entity221
Mathematical modeling of @entity683 is important for analyzing the main cause of the @entity3082 and finding effective interventions towards it. The @entity3082 is a dynamic process. In this process, daily @entity281 are caused by @entity1 's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily @entity221 attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of @entity3082 spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of @entity683 virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an @entity3082 . Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of @entity3082 significantly and system behavior analysis showed social network degree is a critical factor determining the size of an @entity3082 . Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.
