In this article we study the temporal evolution of the pandemic Sars-Cov-2 in Italy by means of dynamic population models. The time window of the available population data is between February 24, and March 25. After we upgrade the data until April 1. We perform the analysis with 4 different models and we think that the best candidate to correctly described the italian situation is a generalized Logistic equation. We use two coupled differential equations that model the evolution of the severe infected and the dead. This choice is due to the fact that in Italy the pharyngeal swabs are made only to severe infected, therefore we have no information about asymptomatic people. Moreover, an important observation is that the virus spreads between Regions with some delay. Indeed, we suggest that a different analysis, region by region, would be more sensible than one on the whole Italy. In particular the region Lombardy has a behaviour very fast compared to the other ones. We show the fit and forecast of the dead and total severe infected for Italy and five regions: Lombardy, Piedmont, Emilia-Romagna, Veneto and Tuscany. Finally we perform an analysis of the peak (intended, in our study, as the maximum of the daily total severe infected) and an estimation of how many lives have been saved by means of the LockDown.

在本文中，我们通过动态人口模型研究了大流行Sars-Cov-2在意大利的时间演变。现有人口数据的时间范围是2月24日至3月25日。在将数据升级到4月1日之后。我们使用4种不同的模型进行分析，我们认为正确描述意大利情况的最佳人选是广义的逻辑方程。我们使用两个耦合的微分方程对严重感染者和死亡者的进化进行建模。这种选择是由于以下事实：在意大利，咽拭子仅针对重度感染者，因此我们没有无症状人群的信息。此外，重要的观察结果是该病毒在区域之间传播有一定的延迟。实际上，我们建议对每个区域进行不同的分析，比整个意大利要明智的多。特别是伦巴第大区的行为与其他区域相比非常快。我们显示了意大利和五个地区（伦巴第，皮埃蒙特，艾米利亚—罗马涅，威尼托和托斯卡纳）的死者和总严重感染者的拟合度和预报。最后，我们对高峰进行了分析（在我们的研究中，预期为每日感染的严重总人数的最大值），并估计了通过LockDown挽救了多少生命。