Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis.

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%.