[1M1-OS-20a] 社会現象とAIと可視化(1/3)

[1M1-OS-20a-03] A note on the visualization of multiple factors related to the epidemics of infectious diseases

〇Kimihito Ito¹, Masahiko Itoh² (1. Hokkaido University, 2. Hokkaido Information University)

[[Online]]

Keywords:COVID-19, epidemiological data, genome data, visualization

The pandemic of COVID-19 has highlighted the role of data scientists who advise governments on the control measure against the infectious disease. Governments expect these data scientists to evaluate the effectiveness of control measures to find best strategy to reduce the impact of infections on the society. Visualization and mathematical modelling are strong tools to explain how proposed control measures are effective. In this paper, we summarize relationships between factors affecting to infections of COVID-19. State-of-art visualization techniques of data on these factors are reviewed. It will be beneficial to develop of visualization systems that allow data scientists not only to explore the relationship among multiple factors related to COVID-19 infections but also to interactively evaluate their models using the same system.