论文标题
Logistic方程和COVID-19
Logistic equation and COVID-19
论文作者
论文摘要
广义逻辑方程用于解释几个国家的COVID-19-19-19S流行数据：奥地利，瑞士，荷兰，意大利，土耳其和韩国。计算模型系数：感染人的增长率和预期数量，以及广义逻辑方程中的指数索引。结果表明，逻辑曲线（在简单或广义的逻辑方程式的框架内）平均可以很好地描述被感染者数量的依赖性，其确定系数超过0.8。同时，每天被感染人数的依赖性具有非常不平衡的特征，并且可以通过逻辑曲线非常大致描述。为了描述它，有必要考虑到模型系数的依赖性或案例总数。例如，增长率的变化可以达到60％。系数的可变性光谱在几天的周期时具有特征峰，这对应于观察到的串行间隔。提出了随机逻辑方程的使用，以估计冠状病毒发病率的可能峰的数量。
The generalized logistic equation is used to interpret the COVID-19 epidemic data in several countries: Austria, Switzerland, the Netherlands, Italy, Turkey and South Korea. The model coefficients are calculated: the growth rate and the expected number of infected people, as well as the exponent indexes in the generalized logistic equation. It is shown that the dependence of the number of the infected people on time is well described on average by the logistic curve (within the framework of a simple or generalized logistic equation) with a determination coefficient exceeding 0.8. At the same time, the dependence of the number of the infected people per day on time has a very uneven character and can be described very roughly by the logistic curve. To describe it, it is necessary to take into account the dependence of the model coefficients on time or on the total number of cases. Variations, for example, of the growth rate can reach 60%. The variability spectra of the coefficients have characteristic peaks at periods of several days, which corresponds to the observed serial intervals. The use of the stochastic logistic equation is proposed to estimate the number of probable peaks in the coronavirus incidence.
