COVIs: Supporting Temporal Visual Analysis of Covid-19 Events Usable in Data-Driven Journalism

Abstract

Caused by a newly discovered coronavirus, COVID-19 is an infectious disease easily transmitted between people through close contacts that had exponential global growth in 2020 and became, in a very short time, a major health, and economic global issue. Real-world data concerning the spread of the disease was quickly made available by different global institutions and resulted in many works involving data visualizations and prediction models. In this paper, (1) we discuss the problem, data aspects, and challenges of COVID-19 data analysis; (2) We propose a Visual Analytics approach (called COVis) combining different temporal aspects of COVID-19 data with the output of a predictive model. This combination supports the estimation of the spread of the disease in different scenarios and allows correlating and monitoring the virus development in relation to different government response events; (3) We evaluate the approach with two domain experts to support the understanding of how our system can facilitate journalistic investigation tasks and (4) we discuss future works and a possible generalization of our solution.