论文标题
Lifshitz缩放和大流行场景下的扩散过程
Diffusive process under Lifshitz scaling and pandemic scenarios
论文作者
论文摘要
我们在这里建议通过基于动态扩散系数在Lifshitz缩放下的修改后扩散方程进行连续有效模型来对Covid-19的活动和累积案例数据进行建模。提出的模型足够丰富，可以捕获复杂病毒扩散的不同方面，因为人类最近面临。该模型是连续的，它势必会通过分析和/或数字求解。因此，我们研究了两个可能的模型，其中某些特定轮廓捕获了与可能的污染类型相关的扩散系数。这里得出的活动案例曲线能够成功地描述德国和西班牙的大流行行为。此外，我们还预测了巴西Covid-19的演变的一些方案。此外，我们描绘了Covid-19的累积病例曲线，再现了圣保罗和巴西圣保罗市和圣保罗·多斯·坎波斯之间大流行的传播。这些方案还揭示了锁定措施如何使污染曲线变平。我们可以找到扩散系数的最佳曲线，以更好地符合大流行的真实数据。
We here propose to model active and cumulative cases data from COVID-19 by a continuous effective model based on a modified diffusion equation under Lifshitz scaling with a dynamic diffusion coefficient. The proposed model is rich enough to capture different aspects of a complex virus diffusion as humanity has been recently facing. The model being continuous it is bound to be solved analytically and/or numerically. So, we investigate two possible models where the diffusion coefficient associated with possible types of contamination are captured by some specific profiles. The active cases curves here derived were able to successfully describe the pandemic behavior of Germany and Spain. Moreover, we also predict some scenarios for the evolution of COVID-19 in Brazil. Furthermore, we depicted the cumulative cases curves of COVID-19, reproducing the spreading of the pandemic between the cities of São Paulo and São José dos Campos, Brazil. The scenarios also unveil how the lockdown measures can flatten the contamination curves. We can find the best profile of the diffusion coefficient that better fit the real data of pandemic.
