Analyzing COVID-19 Using Multisource Data: An Integrated Approach of Visualization, Spatial Regression, and Machine Learning.

ABSTRACT: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, was first identified in Wuhan, China, in December 2019. As the number of COVID-19 infections and deaths worldwide continues to increase rapidly, the prevention and control of COVID-19 remains urgent. This article aims to analyze COVID-19 from a geographical perspective, and this information can provide useful insights for rapid visualization of spatial-temporal epidemic information and identification of the factors important to the spread of COVID-19. A new type of vitalization method, called the point grid map, is integrated with calendar-based visualization to show the spatial-temporal variations in COVID-19. The combination of mixed geographically weighted regression (mixed GWR) and extreme g

SUBMITTER: Wu C 

PROVIDER: S-EPMC8335962 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature