The outbreak of coronavirus disease 2019 (COVID-19) has caused a global disaster, seriously endangering human health and the stability of social order. The purpose of this study is to construct a nonlinear combinational dynamic transmission rate model with automatic selection based on forecasting effective measure (FEM) and support vector regression (SVR) to overcome the shortcomings of the difficulty in accurately estimating the basic infection number $R_0$ and the low accuracy of single model predictions. We apply the model to analyze and predict the COVID-19 outbreak in different countries. First, the discrete values of the dynamic transmission rate are calculated. Second, the prediction abilities of all single models are comprehensively considered, and the best sliding window period is derived. Then, based on FEM, the optimal sub-model is selected, and the prediction results are nonlinearly combined. Finally, a nonlinear combinational dynamic transmission rate model is developed to analyze and predict the COVID-19 epidemic in the United States, Canada, Germany, Italy, France, Spain, South Korea, and Iran in the global pandemic. The experimental results show an the out-of-sample forecasting average error rate lower than 10.07% was achieved by our model, the prediction of COVID-19 epidemic inflection points in most countries shows good agreement with the real data. In addition, our model has good anti-noise ability and stability when dealing with data fluctuations.

中文翻译：

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非线性组合动态传播率模型及其在全球COVID-19疫情预测分析中的应用

2019冠状病毒病（COVID-19）的爆发已造成全球性灾难，严重危害人类健康和社会秩序的稳定。本研究的目的是构建基于预测有效测度（FEM）和支持向量回归（SVR）的具有自动选择的非线性组合动态传播率模型，以克服难以准确估计基本感染人数的缺点。${\mathrm{电阻}}_0$以及单一模型预测的低准确率。我们应用该模型来分析和预测不同国家的 COVID-19 爆发。首先，计算动态传输速率的离散值。其次，综合考虑所有单个模型的预测能力，推导出最佳滑动窗口期。然后基于有限元法选择最优子模型，对预测结果进行非线性组合。最后，开发了非线性组合动态传播率模型来分析和预测全球大流行中美国、加拿大、德国、意大利、法国、西班牙、韩国和伊朗的 COVID-19 流行。实验结果表明，我们的模型实现了低于10.07%的样本外预测平均错误率，大多数国家对 COVID-19 流行拐点的预测与真实数据吻合良好。此外，我们的模型在处理数据波动时具有良好的抗噪能力和稳定性。