(searched for: doi:10.1016/j.jbi.2021.103753)
Published: 3 February 2021
Transactions on Petri Nets and Other Models of Concurrency XV pp 163-173; doi:10.1007/978-3-030-68007-7_10
Conference: Italian Workshop on Visualization and Visual Analytics, 29 September 2020 - 29 September 2020
COVID-19 data analysis has become a prominent activity in the last year. The use of different models for predicting the spread of the disease, while providing very useful insights on the epidemics, are not inherently designed for interactive analysis, with time for a single computation ranging in the tens of seconds. In order to overcome this limitation, this paper proposes three techniques for progressive visualization of Susceptible-Infectious-Recovered (SIR) models data, that govern the trade-off between time and quality of the intermediate results. The techniques are quantitatively evaluated showing promising results.