The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking preventive and control measures to reduce human health damage. The Grey Modeling (1,1) is a popular approach used to construct a predictive model with a small-sized data set. In this study, a hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm (PSO) was applied to create short-term estimates of the total number of confirmed COVID-19 cases for three countries, Germany, Turkey, and the USA. A rolling mechanism that updates data in equal dimensions was applied to improve the forecasting accuracy of the models. The PSO algorithm was used to optimize the Grey Modeling parameters (1,1) to provide more robust and efficient solutions with minimum errors. To compare the accuracy of the predictive models, a nonlinear autoregressive neural network (NARNN) was also developed. According to the analysis results, Grey Rolling Modeling (1,1) optimized by PSO algorithm performs better than the classical Grey Modeling (1,1), Grey Rolling Modelling (1,1), and NARNN models for predicting the total number of confirmed COVID-19 cases. The present study can provide an important basis for countries to allocate health resources and formulate epidemic prevention policies effectively.

预测 2019 年冠状病毒病 (COVID-19) 的传播对于采取预防和控制措施以减少对人类健康的损害至关重要。灰色建模 (1,1) 是一种流行的方法，用于构建具有小型数据集的预测模型。在这项研究中，应用了一种基于灰色预测和通过粒子群优化算法 (PSO) 优化的滚动机制的混合模型，对德国、土耳其和土耳其这三个国家确诊的 COVID-19 病例总数进行了短期估计。美国。应用等维度更新数据的滚动机制来提高模型的预测精度。PSO 算法用于优化灰色建模参数 (1,1)，以提供更稳健、更高效且误差最小的解决方案。为了比较预测模型的准确性，还开发了非线性自回归神经网络 (NARNN)。根据分析结果，PSO算法优化的Gray Rolling Modeling(1,1)在预测总确认数方面优于经典的Gray Modeling(1,1)、Gray Rolling Modeling(1,1)和NARNN模型2019冠状病毒病病例。本研究可为各国有效配置卫生资源和制定防疫政策提供重要依据。