论文标题

COVID-19的随机隔室模型

A Stochastic Compartmental Model for COVID-19

论文作者

Sonnino, Giorgio, Mora, Fernando, Nardone, Pasquale

论文摘要

我们提出了两个冠状病毒大流行的随机模型。已正式计算了模型的统计特性,尤其是相关函数和概率密度函数。我们的模型概括了先前在专业期刊上提出和发表的模型,它考虑了采用锁定措施以及医院和医疗机构的关键作用的模型。为了完成这项工作,我们分析了两种情况:在存在锁定措施的情况下,SIS模型(易感=>感染=>易感),以及与医院的作用(始终在锁定措施的情况下)集成的SIS模型。我们表明,在纯SIS模型的情况下,一旦取消了锁定措施,冠状病毒将再次开始生长。但是,在第二种情况下,超出医院能力的一定门槛,冠状病毒不仅受到控制,而且其传播的能力往往会及时降低。因此,锁定措施与医院和卫生研究所的作用的综合作用能够遏制并抑制SARS-COV-2流行病的传播。可以在给定种群中有限数量的疫苗的大量分配的时间内使用此结果。举例来说,我们分析了美国和法国的数据,在统计力学估算了噪声的强度。特别是,对于美国而言,我们已经分析了两个可能的假设:美国仍然受到第一波感染和美国的影响,在第二次(或第三次)SARS-COV-2感染中。理论预测与真实数据之间的一致性证实了我们方法的有效性。

We propose two stochastic models for the Coronavirus pandemic. The statistical properties of the models, in particular the correlation functions and the probability density function, have duly been computed. Our models, which generalises a model previously proposed and published in a specialised journal, take into account the adoption of the lockdown measures as well as the crucial role of the hospitals and Health Care Institutes. To accomplish this work we have analysed two scenarios: the SIS-model (Susceptible => Infectious => Susceptible) in presence of the lockdown measures and the SIS-model integrated with the action of the hospitals (always in presence of the lockdown measures). We show that in the case of the pure SIS-model, once the lockdown measures are removed, the Coronavirus will start growing again. However, in the second scenario, beyond a certain threshold of the hospital capacities, the Coronavirus is not only kept under control, but its capacity to spread tends to diminish in time. Therefore, the combined effect of the lockdown measures with the action of the hospitals and health Institutes is able to contain and dampen the spread of the SARS-CoV-2 epidemic. This result can be used during a period of time when the massive distribution of delivery of a limited number of vaccines in a given population is not yet feasible. By way of example, we analysed the data for USA and France where the intensities of the noise have been estimated by Statistical Mechanics. In particular, for USA we have analysed two possible hypotheses: USA is still subject to the first wave of infection by and USA is in the second (or third) wave of SARS-CoV-2 infection.The agreement between theoretical predictions and real data confirms the validity of our approach.

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