COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records

Lan, Hai; Sha, Dexuan; Malarvizhi, Anusha Srirenganathan; Liu, Yi; Li, Yun; Meister, Nadine; Liu, Qian; Wang, Zifu; Yang, Jian; Yang, Chaowei

doi:10.1109/access.2021.3085682

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…The COVID-19 pandemic has resulted in inevitable radical changes across almost all areas of daily life, and became a global threat to public health, including the broadly and severely in education and the economy, millions of people have had to work and learn from their home due to the pandemic [2] [3]. It has rapidly induced scientific and technological developments to overcome the limitations of adaptations for survival across many aspects of human life [4] [5]. The COVID-19 crisis has also induced new learning models for continued education, with online education as an alternative solution [6] [7], and online education with new technologies have begun to be used in an ongoing digital transformation across the board in education, and to increase the quality of the education on offer [8] [9].…”

Section: Introductionmentioning

confidence: 99%

IVUL Model: An Intelligent Learning Development Process

Chaiyarak¹,

Nilsook²,

Wannapiroon³

2022

Int. J. Emerg. Technol. Learn.

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The purposes of this research study are to develop an intelligent virtual universal learning (IVUL) model and to evaluate its appropriateness. The study consisted of two phases. Phase 1 involved the development of the IVUL model for univer-sal learning. The conceptual frameworks and theories in the documents and re-search studies on universal design for learning, intelligent learning and virtual learning were studied by the researchers. All main components were then synthe-sised to design the IVUL model. This process can be divided into three main steps: engagement, representation, and action and expression. Each step has sub-steps: access, build and internalise. However, the details of these are dependent on the main steps. The second component is the intelligent learning process, an important process of the model that drives learners to automatically learn by themselves. Artificial intelligence is used as a crucial component that promotes and supports each learner to meet learning goals and objectives according to the universal learning model. The third component is the virtual learning process, which results in learning through computer environments and the Internet. Phase 2 involved an evaluation of the appropriateness of the IVUL model, with in-depth interviews with 20 experts in education and digital technologies. The appropriate-ness of the model was evaluated using the 5-point Likert scale. The findings show that the designed IVUL model can be used for learning development at the highest level.

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Section: Introductionmentioning

confidence: 99%

IVUL Model: An Intelligent Learning Development Process

Chaiyarak¹,

Nilsook²,

Wannapiroon³

2022

Int. J. Emerg. Technol. Learn.

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“…Several data scraping toolsets linked to public dashboards have been reported including the COVID-19 European Regional Tracker [ 12 ], CRISPER from Australia [ 14 ], and the COVID-Scraper, which presents spatiotemporal data from 58 countries [ 21 ]. The COVID-Scraper is an open-source toolset for the extraction, processing, storage, and dissemination of spatiotemporal data related to the COVID-19 pandemic.…”

Section: Sources Of Datamentioning

confidence: 99%

“…However, some organizations make online data visualisation platforms only available only by paid subscription (such as the New York Times and The Telegraph) or limit access to authorized individuals due to the sensitive nature of the data (such as the COVER-NC vaccine equity dashboard [ 23 ]). Interestingly, the COVID-19 scraper is openly reported to have functions to bypass mechanisms for restricting data access [ 21 ], thereby allowing wider dissemination of information that some official bodies consider sufficiently sensitive to restrict. Choice of language is an important aspect of accessibility—the Japan LIVE dashboard is available in Japanese, Chinese, and English [ 24 ], opening access to a wider international audience.…”

Section: Access Control For Data Visualisation Platformsmentioning

confidence: 99%

Spatial and Temporal Data Visualisation for Mass Dissemination: Advances in the Era of COVID-19

Clements

2023

TropicalMed

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The COVID-19 pandemic is the first major pandemic of the digital age and has been characterised by unprecedented public consumption of spatial and temporal disease data, which can enable greater transparency and accountability of governments to the public for their public health decisions. A variety of state and non-state actors have collated and presented maps, charts, and plots of data related to the pandemic in both static and dynamic formats. In particular, there has been a proliferation of online dashboards presenting data related to the pandemic. The sources and types of information displayed have evolved rapidly during the pandemic, with a general trend towards providing more specialised information pertinent to specific aspects of epidemiology or disease control, as opposed simply to disease and death notifications. Limited evaluation of the quality of COVID-19 data visualisation tools has been conducted and significant effort now needs to be spent on standardisation and quality improvement of national and international data visualisation systems including developing common indicators, data quality assurance mechanisms and visualisation approaches, and building compatible electronic systems for data collection and sharing. The increasing availability of disease data for public consumption presents challenges and opportunities for government, media organisations, academic research institutions, and the general public. A key challenge is ensuring consistency and effectiveness of public health messaging to ensure a coordinated response and public trust in intervention strategies. Capitalising on opportunities for greater government accountability for public health decision-making, and more effective mobilisation of public health interventions, is predicated on the provision of accurate and timely information.

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“…Chest CT images, in addition to having a high detection rate, also have the ability to provide useful information regarding the COVID-19 intensity. Patients diagnosed with severe instances of COVID-19 had a greater proportion of lung involvement on thoracic CT images than those diagnosed with less severe forms of the disease, according to the findings of a research that was conducted in [31,32]. Similarly, [33] discovered that chest CT images could help forecast the development of COVID-19 in patients.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Even though chest CT images have demonstrated some degree of success in the identification of COVID-19, there are still questions regarding the possibility of producing erroneous positive results. Work in [31,32,33] conducted research in which the authors discovered that patients with pneumonia that was not caused by COVID-19 could have identical CT findings to patients who had COVID-19, which could lead to a potential misinterpretation. In addition, excessive dependence on chest CT images may result in an increase in the amount of radiation exposure that patients receive, which is a cause for caution regarding the potential for adverse long-term health impacts.…”

Section: Literature Reviewmentioning

confidence: 99%

MXRFCS: Design of an augmented Multidomain XRay Feature representation model for identification of CoVID Severity levels

Thombre

Malik

Kumar

2023

Preprint

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The COVID-19 pandemic has placed an enormous strain on healthcare systems worldwide, leading to a need for more efficient methods of identifying the severity of COVID-19 patients to efficiently allocate resources. Existing Xray processing models for identification of COVID-19 are either highly complicated or showcase lower efficiency when applied for real-time scenarios. To overcome these issues, this paper presents a novel approach for identifying the severity of COVID-19 patients using an augmented multimodal X-ray feature representation model. The proposed model combines X-ray images, clinical data, and demographic information to create a robust representation of individual patient condition. The collected information is converted into multidomain feature sets, including frequency, Gabor, Wavelet and entropy components. A customized deep neural network is trained on this representation to predict the severity level of COVID-19 patients. To evaluate the performance of the proposed model, we used a dataset of X-ray images and clinical data from COVID-19 patients. Our results demonstrate that the proposed model outperforms existing methods for identifying COVID-19 severity levels, achieving an accuracy of 98.5% on multiple dataset samples. The proposed model's performance was observed to be promising in terms of precision, recall and delay, thus has the potential to aid in the early identification and effective management of severe COVID-19 cases, thus contributing to the global effort to combat the COVID-19 pandemic under clinical use cases.

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COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records

Cited by 13 publications

References 30 publications

IVUL Model: An Intelligent Learning Development Process

IVUL Model: An Intelligent Learning Development Process

Spatial and Temporal Data Visualisation for Mass Dissemination: Advances in the Era of COVID-19

MXRFCS: Design of an augmented Multidomain XRay Feature representation model for identification of CoVID Severity levels

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