Analyzing COVID‐19 Using Multisource Data: An Integrated Approach of Visualization, Spatial Regression, and Machine Learning. (4th August 2021) - Wu, Chao; Zhou, Mengjie; Liu, Pengyu; Yang, Mengjie
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 gradient boosting (XGBoost) is used to identify the potential factors and the corresponding importance. The visualization results clearly reflect the spatial‐temporal patterns of COVID‐19. The quantified results reveal that the impact of population outflow from Wuhan is the most important factor and indicate statistically significant spatial heterogeneity. Our results provide insights into how multisource big geodata can be employed within the framework of integrating visualization and analytical methods to characterize COVID‐19 trends. In addition, this work can help understand the influential factors for controlling and preventing epidemics, which is important for policy design and effective decision‐making for controlling COVID‐19. The results reveal that one of the most effective ways to control COVID‐19 include controlling the source of infection, cutting off the transmission route, and protecting vulnerable groups. Plain Language Summary: The outbreak of the 2019 novel coronavirus disease (COVID‐19) has negative effects on the global human health, well‐being, production, life, social functioning, and international relations. This article proposed an integrated framework to analyze the COVID‐19 at the city‐level of China using multi‐source data to fight against the COVID‐19, which can provide useful insights in offering rapid visualization of spatial‐temporal epidemic information and identifying the potential factors. Specifically, a new type of vitalization method, called point grid map, is integrated with calendar‐based visualization to show the spatial‐temporal variations of COVID‐19. The combination of the mixed geographically weighted regression and extreme gradient boosting (XGBoost) is used to identify the potential factors and the corresponding importance. This article emphasizes the power of visualization and analytical method. The results reveal that the most effective way to control and defeat the COVID‐19 is to control the source of infection, cut off the transmission route, and protect the vulnerable groups. We believe that the study contents and findings are relevant to the scope of your journal will be of interest to its readership. Key Points: A point grid map is integrated with calendar‐based visualization to represent the spatial‐temporal variations of COVID‐2019 in China Mixed geographically weighted regression and extreme gradient boost are employed to identify the factors important to the spread of COVID‐19 The framework integrating visualization, spatial regression, and machine learning can be applied to spatial epidemiology studies elsewhere