Abstract
COVID-19 is the latest pandemic of many that have affected the world over the past few decades. Most countries have taken multiple control measures to fight against the coronavirus, in which vaccination is one of the essential methods. Some related studies have been processed to analyse the relationship between vaccination and mortality rate, further reflecting vaccines' impacts on the epidemic. However, their data sources are relatively individual, and few studies have considered the potential relationships among the factors involved. Here, a method based on pure data analysis is proposed to initially address the gap in the possible connections between pandemic-related information and vaccine data from a more comprehensive and detailed perspective, and better analyse the impact of related factors as a supplement to research in the epidemiology area, and a novel metaphor graph method is also introduced to represent results as well. We collected data from the top six countries with the highest confirmed cases globally as of 22 March 2022, and the datasets cover data such as epidemic case, vaccine, nucleic acid testing, population, and related events. The experimental results suggest that potential relationships exist in most circumstances, and evidence indicates that mortality and infection rates decline with vaccination in some countries during the pandemic; the downward trend becomes more pronounced as vaccination rates increase; highly effective vaccines have contributed to a reduction in mortality. This work could potentially serve as a supplementary tool in relevant areas to offer more evidence for existing research from a data analytics angle.
Original language | English |
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Pages (from-to) | 1231-1241 |
Number of pages | 11 |
Journal | Journal of Nonlinear and Convex Analysis |
Volume | 24 |
Issue number | 6 |
Publication status | Published - 2023 |
Externally published | Yes |
Keywords
- COVID-19
- graph visualisation
- relationship analytics
- vaccines
- visual analysis