论文标题
离散的Gompertz和广义逻辑模型，用于早期监测古巴的COVID-19大流行
Discrete Gompertz and Generalized Logistic Models for early monitoring of the COVID-19 pandemic in Cuba
论文作者
论文摘要
在过去的几年中，使用现象学生长模型来预测传染病的早期动力学。这些模型假定时间是连续变量，而在本贡献中，Gompertz和广义逻辑模型的离散版本用于早期监测和短期预测，对该地区的流行病的传播。时间连续模型用一阶微分方程表示数学表示，而它们的离散版本则由一阶差异方程表示，涉及参数，这些参数应在预测之前估算。详细描述了估计此类参数的方法。古巴在COVID-19感染的实际数据用于说明这种方法。提出的方法是在最初的35天实施，能够很好地预测接下来的二十天的数据。在差异的每个步骤中都包括在附录中包含用于研究Gompertz模型的代码。
For the last few years there has been a resurgence in the use of phenomenological growth models for predicting the early dynamics of infectious diseases. These models assume that time is a continuous variable whereas in the present contribution, the discrete versions of Gompertz and Generalized Logistic models are used for early monitoring and short-term forecasting of the spread of an epidemic in a region. The time-continuous models are represented mathematically by first-order differential equations while their discrete versions are represented by first-order difference equations that involve parameters that should be estimated prior to forecasting. The methodology for estimating such parameters is described in detail. Real data of COVID-19 infection in Cuba is used to illustrate this methodology. The proposed methodology was implemented for the first thirty-five days, being able to predict with very good precision the data reported for the following twenty days. The codes implemented to study the Gompertz model in differences are included in an appendix with each step of the methodology identified.
