Comparative visualization of epidemiological data during various stages of a pandemic

TL;DR

This paper develops comparative visualization methods for epidemiological data during different pandemic stages, enabling better understanding of global trends and divergent behaviors over time.

Contribution

It introduces stage-adapted visualizations for pandemic data, focusing on time lag, doubling times, and wave detection to enhance comparative analysis.

Findings

Effective visualization of pandemic stages across countries

Identification of common and divergent epidemiological patterns

Tools for monitoring pandemic progression in real-time

Abstract

After COVID-19 was first reported in China at the end of 2019, it took only a few months for this local crisis to turn into a global pandemic with unprecedented disruptions of everyday life. However, at any moment in time the situation in different parts of the world is far from uniform and each country follows its own epidemiological trajectory. In order to keep track of the course of the pandemic in many different places at the same time, it is vital to develop comparative visualizations that facilitate the recognition of common trends and divergent behaviors. Similarly, it is important to always focus on the information that is most relevant at any given point in time. In this study we look at exactly one year of daily numbers of new cases and deaths and present data visualizations that compare many different countries and are adapted to the overall stage of the pandemic. During the early stage when cases and deaths still rise we focus on the time lag relative to the current epicenter of the pandemic and the doubling times. Later we monitor the rise and fall of the daily numbers via wave detection plots. The transition between these two stages takes place when the daily numbers stop rising for the first time.