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

• المؤلفون الرئيسيون: Chew, Alvin Wei Ze, Wang, Ying, Zhang, Limao
• مؤلفون آخرون: School of Civil and Environmental Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Civil engineering; Climate Conditions; Socioeconomic-Governmental Nexus
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/159902
• المؤسسة: Nanyang Technological University

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 a...