Two mathematical models of the COVID-19 dynamics are considered as the health system in some country consists in a network of regional hospital centers. The first macroscopic model for the virus dynamics at the level of the general population of the country is derived from a standard SIR model. The second local model refers to a single node of the health system network, i.e. it models the flows of patients with a smaller granularity at the level of a regional hospital care center for COVID-19 infected patients. Daily (low cost) data are easily collected at this level, and are worked out for a fast evaluation of the local health status thanks to control systems methods.

Precisely, the identifiability of the parameters of the hospital model is proven and thanks to the availability of clinical data, essential characteristics of the local health status are identified. Those parameters are meaningful not only to alert on some increase of the infection, but also to assess the efficiency of the therapy and health policy.

由于某些国家的卫生系统位于区域医院中心网络中，因此考虑了COVID-19动态的两个数学模型。在该国总人口水平上，第一个宏观的病毒动态宏观模型是从标准SIR模型得出的。第二个局部模型是指卫生系统网络的单个节点，即它在区域医院护理中心针对COVID-19感染患者的水平上，以较小的粒度对患者的流量进行建模。每天（低成本）数据都可以在此级别轻松收集，并且借助控制系统方法，可以对这些数据进行快速评估，以评估当地的健康状况。

准确地证明了医院模型参数的可识别性，并且由于可获得临床数据，因此可以识别出本地健康状况的基本特征。这些参数不仅对警告感染的增加有重要意义，而且对评估治疗效果和健康政策也很有意义。