COVIs: Supporting Temporal Visual Analysis of Covid-19 Events Usable in Data-Driven Journalism Roger A. Leite * TU Wien Victor Schetinger † TU Wien Davide Ceneda ‡ TU Wien Bernardo Henz § IFFar Silvia Miksch ¶ TU Wien Figure 1: COVis’s coordinated multiple view environment: (A) Control Panel: allows the user to change the graphs scale (linear / log), metric (absolute / per million cohabitants), and dimensions (cases / deaths). (B) Line charts: present four different line charts that are coordinated to support exploration of multiple narratives. Respectively, the charts display the relation between: (B1) time x cases/deaths, (B2) time x tests, (B3) total cases/deaths x last week cases/deaths, and (B4) time x cases/deaths projection length. (C) Events Panel: displays information and source references concerning main events occurred in certain time periods. (D) Events Time Chart: chart presenting the policy changes of a country over time. (E) Country Cards: show information concerning the analysed group of countries, allowing the exclusion and inclusion of different countries into the analysis. ABSTRACT Caused by a newly discovered coronavirus, COVID-19 is an in- fectious 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 ap- proach 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. Index Terms: COVID-19—Visual Analytics—Data Visualization— Prediction ModelEventsTime-oriented Analysis; * e-mail: roger.leite@tuwien.ac.at † e-mail: victor.schetinger@tuwien.ac.at ‡ e-mail: davide.ceneda@tuwien.ac.at § e-mail: bernardo.henz@iffarroupilha.edu.br ¶ e-mail: silvia.miksch@tuwien.ac.at 1 I NTRODUCTION The recent COVID-19 epidemic has triggered a global crisis that tests our modern civilization’s potential for coordination and cooper- ation. While scientists, epidemiologists, and health specialists have a central role in understanding the disease itself, its social, political and economical aspects can be much harder to analyze [5]. Every country responded to the crisis in a different way and in its own time. Data about contagion rates and their growth has been the main source for public discussion on the subject. However, relating such data to global and local events within each country is a complex puzzle and many specialists still rely primarily on their Microsoft Excel [25] skills to perform data-driven analysis (see Section 6). Journalists, social scientists and economists may be the ones most suited for the task of piecing this puzzle together, but they need the proper tools to visualize the bigger picture in a sea of data and news that evolves on a daily basis. One of the most important aspects to be understood is the relation between events – political or otherwise – and the spread of the virus in each local context. The Oxford University [11] provides a dataset of public measures for each individual country, along with an index estimating the level of closure of a determinate country. This data can be combined with the global rates of infection and predictive models to construct a rich picture of the development of the crisis. In this paper, we propose a Visual Analytics (VA) approach (called COVis) to aid the temporal analysis and exploration of narratives under an investigative framework, focusing on the identification of significant events and time frames. This is a process of storytelling, for which we provide means to be grounded as much as possible in available data and evidence, so that communicators need not rely solely on speculation, exaggeration, and interpretation. 56 2020 IEEE Visualization Conference (VIS) © IEEE 2021. This article is free to access and download, along with rights for full text and data mining, re-use and analysis. DOI 10.1109/VIS47514.2020.00018 2020 IEEE Visualization Conference (VIS) | 978-1-7281-8014-4/20/$31.00 ©2020 IEEE | DOI: 10.1109/VIS47514.2020.00018