Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart

Abstract

Background Considering the widespread of coronavirus disease 2019 (COVID-19) pandemic in the world, it is important to understand the spatiotemporal development of the pandemic. In this study, we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries. Methods Using COVID-19 case and death data from February to December 2020 offered by Johns Hopkins University, we generated time-associated balloon charts with multiple epidemiological indicators including crude case fatality rate (CFR), morbidity, mortality and the total number of cases, to compare the progression of the pandemic within a specific period across regions and countries, integrating seven related dimensions together. The area chart is used to supplement the display of the balloon chart in daily new COVID-19 case changes in UN geographic regions over time. Javascript and Vega-Lite were chosen for programming and mapping COVID-19 data in browsers for visualization. Results From February 1st to December 20th 2020, the COVID-19 pandemic spread across UN subregions in the chronological order. It was first reported in East Asia, and then became noticeable in Europe (South, West and North), North America, East Europe and West Asia, Central and South America, Southern Africa, Caribbean, South Asia, North Africa, Southeast Asia and Oceania, causing several waves of epidemics in different regions. Since October, the balloons of Europe, North America and West Asia have been rising rapidly, reaching a dramatically high morbidity level ranging from 200 to 500/10 000 by December, suggesting an emerging winter wave of COVID-19 which was much bigger than the previous ones. By late December 2020, some European and American countries displayed a leading mortality as high as or over 100/100 000, represented by Belgium, Czechia, Spain, France, Italy, UK, Hungary, Bulgaria, Peru, USA, Argentina, Brazil, Chile and Mexico. The mortality of Iran was the highest in Asia (over 60/100 000), and that of South Africa topped in Africa (40/100 000). In the last 15 days, the CFRs of most countries were at low levels of less than 5%, while Mexico had exceptional high CFR close to 10%. Conclusions We creatively used visualization integrating 7-dimensional epidemiologic and spatiotemporal indicators to assess the progression of COVID-19 pandemic in terms of transmissibility and severity. Such methodology allows public health workers and policy makers to understand the epidemics comparatively and flexibly.

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

“…Task Accuracy User Experience Task Efficiency N/A [14,16,18,22,24,[27][28][29][30][32][33][34][35][37][38][39][41][42][43][44][45][46][47]49,[52][53][54][55][56][57][58][59]61,64,65,[68][69][70]72,73,[75][76][77][78][79][81][82][83][84][85][86][87]…”

“…With regard to datasets, 72.81% (83/114) of the studies indicated which ones they used. However, owing to the extent and variety of the studies reviewed, there were only eight duplicate sources: Korea Centers for Disease Control & Prevention (KCDC) [30,44,71,106,107], Johns Hopkins University datasets [15,23,28,42,46,47,53,57,75,81,[84][85][86]95,97,98,125], WHO COVID-19 Daily Report [30,42,43,76,102], European Centre of Disease Prevention and Control (ECDC) [57,86,100,108,118], Centre of Disease Prevention and Control (CDC) on COVID-19 [24,30,31,55,95,102], the National Health Commission [42,43,52,55,87], the US Census Bureau [31,46,49,…”

Espaço E Covid-19:

“…KIM et al, 2021;SCARPONE et al, 2020;KWOK et al, 2021;BUJA et al, 2020;VAZ, 2021). Como exemplo, tem-se a pesquisa de Lenzen et al (2020) BHERWANI et al, 2021;ZHANG et al, 2020;SANGIORGIO;PARISI, 2020;GIANQUINTIERI et al, 2020;POURGHASEMI et al, 2020;SHAW et al, 2021;KARAIVANOV, 2020;PAPASTEFANOPOULOS;VICTOR OKHUESE, 2020;LINARDATOS;KOTSIANTIS, 2020;CHENG et al, 2020;CHINAZZI et al, 2020;KONICEK et al, 2020;JIANG et al, 2020;PANG et al, 2021;ZHOU et al, 2020).…”

Spatial Analysis to Identify Hotspots of Covid-19 Incidence in Jambi Municipality Indonesia

“…Many studies on the spatial aspects of the Covid-19 case have been carried out around the world (Ballesteros et al, 2021;Edriani et al, 2021;Hu et al, 2021;Lak et al, 2021;Mohammad Ebrahimi et al, 2021;Pang et al, 2021;Purwanto et al, 2021;Raymundo et al, 2021;Saavedra et al, 2021). Not much has been done to study the spatial aspects of the Indonesian Covid-19 case (Dhewantara et al, 2022;Heldayani et al, 2021;Iriany et al, 2021;Ratnasari & Dewi, 2021).…”