Modeling of Coronavirus Behavior to Predict it’s Spread

Khan, Shakir; Alfaifi, Amani

doi:10.14569/ijacsa.2020.0110552

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…When the COVID-19 epidemic started, several African nations quickly expanded their diagnostic and surveillance capabilities [ 9 ]. The early implementation of non-pharmaceutical treatments to stop transmission in the majority of African nations came at a cost to their healthcare systems and post-disaster rehabilitation [ 10 , 11 ]. Data reporting techniques vary widely because of the diversity between African nations and the range of COVID-19 experiences.…”

Section: Introductionmentioning

confidence: 99%

Data quality model for assessing public COVID-19 big datasets

et al. 2023

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For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For public health practitioners and researchers, the reporting of COVID-19 data need to be accurate and easily available. Each nation has a system in place for reporting COVID-19 data, albeit these systems’ efficacy has not been thoroughly evaluated. However, the current COVID-19 pandemic has shown widespread flaws in data quality. We propose a data quality model (canonical data model, four adequacy levels, and Benford’s law) to assess the quality issue of COVID-19 data reporting carried out by the World Health Organization (WHO) in the six Central African Economic and Monitory Community (CEMAC) region countries between March 6,2020, and June 22, 2022, and suggest potential solutions. These levels of data quality sufficiency can be interpreted as dependability indicators and sufficiency of Big Dataset inspection. This model effectively identified the quality of the entry data for big dataset analytics. The future development of this model requires scholars and institutions from all sectors to deepen their understanding of its core concepts, improve integration with other data processing technologies, and broaden the scope of its applications.

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Section: Introductionmentioning

confidence: 99%

Data quality model for assessing public COVID-19 big datasets

et al. 2023

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“…A group of viruses named Coronaviruses, which includes MERS-CoV, SARS-CoV, and finally SARS-CoV-2, are likely reasons for mild to severe respiratory illnesses [1]. As reported in the report from the World Health Organization, the COVID-19 infection was espoused by the severe acute respiratory syndrome SARS-CoV-2 and originated in Wuhan, Republic of China, before spreading to every country and territory throughout the world in 2020 [2]- [4]. Furthermore, in the preliminary days of SARS-CoV-2, various respiratory symptoms including hyperthermia, cough, shortness of breath, exhaustion, and finally pneumonia are frequent [5], [6].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

Gannour¹,

Hamida²,

Saleh³

et al. 2022

IJACSA

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The COVID-19 infection was sparked by the severe acute respiratory syndrome SARS-CoV-2, as mentioned by the World Health Organization, and originated in Wuhan, Republic of China, eventually extending to every nation worldwide in 2020. This research aims to establish an efficient Medical Diagnosis Support System (MDSS) for recognizing COVID-19 in chest radiography with X-ray data. To build an ever more efficient classifier, this MDSS employs the concatenation mechanism to merge pretrained convolutional neural networks (CNNs) predicated on Transfer Learning (TL) classifiers. In the feature extraction phase, this proposed classifier employs a parallel deep feature extraction approach based on Deep Learning (DL). As a result, this approach increases the accuracy of our proposed model, thus identifying COVID-19 cases with higher accuracy. The suggested concatenation classifier was trained and validated using a Chest Radiography image database with four categories: COVID-19, Normal, Pneumonia, and Tuberculosis during this research. Furthermore, we integrated four separate public X-Ray imaging datasets to construct this dataset. In contrast, our mentioned concatenation classifier achieved 99.66% accuracy and 99.48% sensitivity respectively.

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“…It can revolutionize intelligence in transport, energy, financial, and education [4], which is what we intend to study here. In general, there is a need for using big data, which is given many advantages, such as help to predict the performance for example coronavirus modelling and behavior for COVID-19 is predicted with big data [57]. In this research, we will highlight on education filed because Big Data is already using widely in education [5].…”

Section: Introductionmentioning

confidence: 99%

Big Data Application and its Impact on Education

Khan

Alqahtani

2020

Int. J. Emerg. Technol. Learn.

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Big data is employed in widely different fields; we here study how education uses big data. We review the literature of the research about big data in education in the time interval from 2010 to 2020 then review the process of big educational data mining, the tools, and the applications of big data in education. This paper, with the help of these applications, explores the idea to improve the education process. Two methods are applied to validate education process and many parameters are discussed to complete the research.

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Modeling of Coronavirus Behavior to Predict it’s Spread

Cited by 6 publications

References 6 publications

Data quality model for assessing public COVID-19 big datasets

Data quality model for assessing public COVID-19 big datasets

COVID-19 Detection on X-Ray Images using a Combining Mechanism of Pre-trained CNNs

Big Data Application and its Impact on Education

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