A global COVID-19 observatory, monitoring the pandemics through text mining and visualization

Massri, M.Besher; Costa, Joao Pita; Bauer, Andrej; Grobelnik, Marko; Brank, Janez; Stopar, Luka

doi:10.31449/inf.v46i1.3375

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…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,…”

Section: Papers Standard Methodologymentioning

confidence: 99%

“…According to our review, more than half of the 114 studies (67/114) used 2D or 3D line charts as depictions of time series to represent temporal data related to a pan-demic. They were not only used to depict the number of cases per week, month, or year [16,21,22,24,25,30,36,50,53,54,56,59,60,62,65,67,68,[71][72][73]75,79,[84][85][86][87]97,98,100,103,104,106,109,113,114,120,121,127] or cumulative cases [22,27,52,55,65,70,71,[98][99][100]105,…”

Section: Q3: Which Techniques Are the Most Frequently Used To Visuali...mentioning

confidence: 99%

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Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

Kim,

Cánovas-Segura,

Campos

et al. 2024

Technologies

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In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. This study aims to address this gap by conducting a scoping review following the PRISMA-ScR guidelines, examining the literature from 2000 to 2024 on spatial–temporal visualization techniques when applied to epidemics, across five databases: PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library until 24 January 2024. Among 1312 papers reviewed, 114 were selected, emphasizing aggregate measures, web platform tools, and geospatial data representation, particularly favoring choropleth maps and extended charts. Visualization techniques were predominantly utilized for real-time data presentation, trend analysis, and predictions. Evaluation methods, categorized into standard methodology, user experience, task efficiency, and accuracy, were observed. Although various open-access datasets were available, only a few were commonly used, mainly those related to COVID-19. This study sheds light on the current trends in visualizing epidemiological data over the past 24 years, highlighting the gaps in standardized evaluation methodologies and the limited exploration of individual epidemiological data and diseases acquired in hospitals during epidemics.

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Section: Papers Standard Methodologymentioning

confidence: 99%

Section: Q3: Which Techniques Are the Most Frequently Used To Visuali...mentioning

confidence: 99%

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

Kim,

Cánovas-Segura,

Campos

et al. 2024

Technologies

View full text Add to dashboard Cite

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A Study of Prediction Accuracy of English Test Performance Using Data Mining and Analysis

Duan¹

2023

AETiC

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This paper focused on the effect of data mining in predicting students' English test scores. With the progress of data mining analysis, there are more applications in teaching, and data mining to achieve the prediction of students’ test scores is important to support the educational work. In this paper, the C4.5 decision tree algorithm was improved by combining Taylor's series, and then the data of students' English tests in 2019-2020 were collected for experiments. The results showed that the scores of “Comprehensive English” and “Specialized English” had a great influence on the score of CET-4, and the improved C4.5 algorithm was more efficient than the original one, maintained a fast computation speed even when the data volume was large, and had an accuracy of more than 85%. The results demonstrate the accuracy of the improved C4.5 algorithm for predicting students’ English test scores. The improved C4.5 algorithm can be extended and used in reality.

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A global COVID-19 observatory, monitoring the pandemics through text mining and visualization

Cited by 2 publications

References 5 publications

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review

A Study of Prediction Accuracy of English Test Performance Using Data Mining and Analysis

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