Big Data Science on COVID-19 Data

Leung, Carson K.; Chen, Yubo; Shang, Siyuan; Deng, Dayong

doi:10.1109/bigdatase50710.2020.00010

Cited by 31 publications

(17 citation statements)

References 59 publications

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“…Research [23] , [24] , [25] has been conducted and developed a framework to examine the usage of mask, population impact, transmission areas etc and the recommend that the everyone should wear a public cloth mask to control the transmission of disease.…”

Section: Literature Surveymentioning

confidence: 99%

Big data analytics for mask prominence in COVID pandemic

Shinde¹,

Desai

Katkar

et al. 2022

Materials Today: Proceedings

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Corona Virus is spreading at an alarming rate in community causing respiratory diseases like SARS and MERS, which has laid down Government agencies and healthcare organizations to adopt and recommend various strategies in order to cease the spread of corona virus. Till the dawn of Vaccine, only available cost-effective preventive aid is the use of face mask. Since the outbreak of covid-19, demand for the face mask has been increased tremendously which has led to the shortage of face mask. Various masks are available in the market, but reuse and decontamination of reusable face mask has become the topic of concern. Commonly available masks in market are N-95, Medical/Surgical Mask and cloth masks. N-95 and Respirators should be reserved for frontline primary Healthcare professionals which are involved in High-risk aerosol generating procedures, while Surgical and medical mask should be used by secondary healthcare professionals and cloth masks by General public.

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Section: Literature Surveymentioning

confidence: 99%

Big data analytics for mask prominence in COVID pandemic

Shinde¹,

Desai

Katkar

et al. 2022

Materials Today: Proceedings

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show abstract

“…This allows simulations to predict the path of the COVID-19 pandemic, for example to identify hazardous pandemic areas. The pandemic is projected using accessible data points, which puts the accuracy of the prediction into question owing to the uncertainty of the fitting due to lack of thorough inquiry [49]. Accuracy may be affected by various circumstances, including diseased cases, population, living situations, surroundings, and so on.…”

Section: Rq1: Role Of Big Data Applications For Fighting the Covid-19...mentioning

confidence: 99%

“…points, which puts the accuracy of the prediction into question owing to the uncertainty of the fitting due to lack of thorough inquiry [49]. Accuracy may be affected by various circumstances, including diseased cases, population, living situations, surroundings, and so on.…”

Section: Rq1: Role Of Big Data Applications For Fighting the Covid-19...mentioning

confidence: 99%

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

et al. 2021

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The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19.

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“…Several studies discussed different data mining techniques to study the behavior of the COVID-19 [23,4,21]. Authors in [13] uses neural networks, which takes advantage of few-shot learning and autoencoder to perform predictive analysis on COVID-19 data.…”

Section: Related Workmentioning

confidence: 99%

“…Because of the rapid global spread of COVID-19, and the cooperation of medical institutions worldwide, a tremendous amount of public data -more data than ever before for a single virus -has been made available for researchers [14,9,5]. This "big data" opens up new opportunities to analyse the behavior of this virus [21,20]. Despite these opportunities, the huge size of the data poses a challenge for its processing on smaller systems [9].…”

Section: Introductionmentioning

confidence: 99%

Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models

Ali¹,

Zhou²,

Patterson³

2021

Preprint

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Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never before, by leveraging such big data. This data is of many different varieties, and can be of different levels of veracity e.g., precise, imprecise, uncertain, and missing, making it challenging to extract important information from such data. Yet, efficient analyses of this continuously growing and evolving COVID-19 data is crucial to inform -often in real-time -the relevant measures needed for controlling, mitigating, and ultimately avoiding viral spread. Applying machine learning based algorithms to this big data is a natural approach to take to this aim, since they can quickly scale to such data, and extract the relevant information in the presence of variety and different levels of veracity. This is important for COVID-19, and for potential future pandemics in general.In this paper, we design a straightforward encoding of clinical data (on categorical attributes) into a fixed-length feature vector representation, and then propose a model that first performs efficient feature selection from such representation. We apply this approach on two clinical datasets of the COVID-19 patients and then apply different machine learning algorithms downstream for classification purposes. We show that with the efficient feature selection algorithm, we can achieve a prediction accuracy of more than 90% in most

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Big Data Science on COVID-19 Data

Cited by 31 publications

References 59 publications

Big data analytics for mask prominence in COVID pandemic

Big data analytics for mask prominence in COVID pandemic

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models

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