论文标题
前100天：建模Covid-19大流行的演变
The first 100 days: modeling the evolution of the COVID-19 pandemic
论文作者
论文摘要
提出了一个简单的分析模型，用于建模2020年共同-19大流行的演变。该模型基于用于描述流行病的广泛使用的易感性摄取（SIR）种群模型的数值解。我们考虑了原始Kermack-Mckendrick模型的扩展版本，该版本包括由于外部强加的条件而引起的参数$β$（有效接触率）的衰减值，我们将其称为强制SIR（FSIR）模型。我们向代表FSIR模型的微分方程引入了近似的分析解决方案，该方程在100天内为许多国家提供了非常合理的实际数据（从指数增长开始，在中国）。所提出的模型包含3个可调节参数，这些参数可通过拟合实际数据（截至2020年4月28日）获得。我们分析这些结果以推断涉及参数的物理含义。我们使用该模型对每个国家的感染总数以及感染次数达到该总数的99％的日期进行预测。我们还将模型的关键发现与最近报道的有关该疾病的高传染性和快速传播的结果进行了比较。
A simple analytical model for modeling the evolution of the 2020 COVID-19 pandemic is presented. The model is based on the numerical solution of the widely used Susceptible-Infectious-Removed (SIR) populations model for describing epidemics. We consider an expanded version of the original Kermack-McKendrick model, which includes a decaying value of the parameter $β$ (the effective contact rate) due to externally imposed conditions, to which we refer as the forced-SIR (FSIR) model. We introduce an approximate analytical solution to the differential equations that represent the FSIR model which gives very reasonable fits to real data for a number of countries over a period of 100 days (from the first onset of exponential increase, in China). The proposed model contains 3 adjustable parameters which are obtained by fitting actual data (up to April 28, 2020). We analyze these results to infer the physical meaning of the parameters involved. We use the model to make predictions about the total expected number of infections in each country as well as the date when the number of infections will have reached 99% of this total. We also compare key findings of the model with recently reported results on the high contagiousness and rapid spread of the disease.
