In this study, we develop a deep learning model to forecast the transmission rate of COVID-19 globally, via a proposed G parameter, as a function of fused data features which encompass selected climate conditions, socioeconomic and restrictive governmental factors. A 2-step optimization process is adopted for the model's data fusion component which systematically performs the following: (Step I) determining the optimal climate feature which can achieve good precision score (> 70%) when predicting the spatial classes distribution of the G parameter on a global scale consisting of 251 countries, followed by (Step II) fusing the optimal climate feature with 11 selected socioeconomic-governmental factors to further improve the model's predictive capability. By far, the obtained results from the model's testing step indicate that land surface temperature day (LSTD) has the strongest correlation with the global G parameter over time by achieving an average precision score of 72%. When coupled with relevant socioeconomic-governmental factors, the model's average precision score improves to 77%. At the local scale analysis for selected countries, our proposed model can provide insights into the relationship between the fused data features and the respective local G parameter by achieving an average accuracy score of 79%.

在这项研究中，我们开发了一个深度学习模型，通过提出的 G 参数来预测 COVID-19 在全球的传播率，作为融合数据特征的函数，其中包括选定的气候条件、社会经济和限制性政府因素。模型的数据融合组件采用两步优化过程，系统地执行以下操作：（步骤 I）确定在预测 G 的空间类别分布时可以获得良好精度分数（> 70%）的最佳气候特征在由 251 个国家组成的全球范围内计算参数，然后（第二步）将最佳气候特征与 11 个选定的社会经济政府因素融合，以进一步提高模型的预测能力。到目前为止，模型测试步骤获得的结果表明，随着时间的推移，地表温度日 (LSTD) 与全球 G 参数的相关性最强，平均精度得分为 72%。当与相关的社会经济政府因素相结合时，该模型的平均精度得分提高到 77%。在选定国家/地区的本地规模分析中，我们提出的模型可以通过实现 79% 的平均准确度分数来深入了解融合数据特征与相应本地 G 参数之间的关系。