Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis

Zheng, Tianlong; Zhang, Chunli; Shi, Yueting; Chen, Debao; Liu, Sheng

doi:10.26599/bdma.2022.9020010

Cited by 5 publications

(7 citation statements)

References 50 publications

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“…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]…”

Section: Papers Standard Methodologymentioning

confidence: 99%

“…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%

“…The studies that used a map-style representation (79/114) adopted different strategies: static geographic 2D or 3D maps [14,15,19,20,22,31,33,36,38,[42][43][44]50,75,81,83,88,93,102,104,107,109,117,118,123], dynamic 2D maps [20][21][22]25,61,65,80,85,89,90,94,97], or choropleth maps [17,23,24,27,30,32,34,35,37,40,46,48,49,52,…”

Section: Q2: Which Techniques Are Most Frequently Used To Visualize E...mentioning

confidence: 99%

“…X [15,53,59,81,91,93] X 3.5. Q5: Do the Existing Visualizations Use Individual-Level Data or Aggregated Data?…”

Section: Q4: Which Combinations Of Spatial-temporal Visualizations Ar...mentioning

confidence: 99%

“…,[42][43][44]50,75,81,83,88,93,102,104,107,109,117,118,123], dynamic 2D maps[20][21][22]25,61,65,80,85,89,90,94,97], or choropleth maps[17,23,24,27,30,32,34,35,37,40,46,48,49,52,55,56,58,60,62,64,68,71,[76][77][78][79]...…”

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: Papers Standard Methodologymentioning

confidence: 99%

Section: Q2: Which Techniques Are Most Frequently Used To Visualize E...mentioning

confidence: 99%

“…X [15,53,59,81,91,93] X 3.5. Q5: Do the Existing Visualizations Use Individual-Level Data or Aggregated Data?…”

Section: Q4: Which Combinations Of Spatial-temporal Visualizations Ar...mentioning

confidence: 99%

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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Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Ajagbe

Adigun

2023

Multimed Tools Appl

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Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps in the timely detection of any infectious disease (IDs) and is essential to the management of diseases and the prediction of future occurrences. Many scientists and scholars have implemented DL techniques for the detection and prediction of pandemics, IDs and other healthcare-related purposes, these outcomes are with various limitations and research gaps. For the purpose of achieving an accurate, efficient and less complicated DL-based system for the detection and prediction of pandemics, therefore, this study carried out a systematic literature review (SLR) on the detection and prediction of pandemics using DL techniques. The survey is anchored by four objectives and a state-of-the-art review of forty-five papers out of seven hundred and ninety papers retrieved from different scholarly databases was carried out in this study to analyze and evaluate the trend of DL techniques application areas in the detection and prediction of pandemics. This study used various tables and graphs to analyze the extracted related articles from various online scholarly repositories and the analysis showed that DL techniques have a good tool in pandemic detection and prediction. Scopus and Web of Science repositories are given attention in this current because they contain suitable scientific findings in the subject area. Finally, the state-of-the-art review presents forty-four (44) studies of various DL technique performances. The challenges identified from the literature include the low performance of the model due to computational complexities, improper labeling and the absence of a high-quality dataset among others. This survey suggests possible solutions such as the development of improved DL-based techniques or the reduction of the output layer of DL-based architecture for the detection and prediction of pandemic-prone diseases as future considerations.

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