Leveraging Data Science to Combat COVID-19: A Comprehensive Review

Latif, Siddique; Usman, Muhammad; Manzoor, Sanaullah; Iqbal, Waleed; Qadir, Junaid; Tyson, Gareth; Castro, Ignacio; Razi, Adeel; Boulos, Maged N. Kamel; Weller, Adrian; Crowcroft, Jon

doi:10.1109/tai.2020.3020521

Cited by 164 publications

(76 citation statements)

References 125 publications

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“…Alafif et al, (Alafif et al, 2021), surveyed AI applications in COVID-19 diagnosis. In (Latif et al, 2020), a review of DL techniques to combat COVID-19 was discussed in detail. Table 1 summarizes the existing literature that uses ML applications to tackle COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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Section: Related Workmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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“…The scientific studies available in this area are scattered, and their numbers are overwhelming, making it hard for researchers to obtain a structured view of the state of the art. Fortunately, interesting reviews quickly took over this situation, providing a complete overview, updated to the first months of the pandemic, and even creating an interesting repository of papers on data science-based applications that can help in the COVID-19 pandemic [13].…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Rodríguez‐Rodríguez

Rodríguez

Shirvanizadeh

et al. 2021

IJERPH

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The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic.

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“…Since the COVID-19 pandemic outbreak, tens of thousands of scientific papers have researched the associations with it, many of which use artificial intelligence and data science. For some authors, data science will be a crucial element in the global response to the pandemic [1]. According to Van der Aalst [2], data science is a cross-disciplinary field of transforming data into absolute values.…”

Section: Introductionmentioning

confidence: 99%

“…Today, data science applications tackle COVID-19 in three main phases: screening, tracking and forecasting, and medical aid [4]. In particular, the different use cases can be arranged in eight lines of application: assessing risk and prioritising patients; testing and diagnostics; simulating and modelling; contact tracing; comprehending social interventions; logistic planning and economic action; automated patient services; and supporting the development of vaccines and new therapies [1] As part of the data science's applications to support a pandemic response, prediction systems to improve the healthcare supply chain appear necessary. Furthermore, the changing and uncertain COVID-19 environment, and the need to maintain social distancing, are barriers to effective supply chain systems.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

Salazar-Concha

Ramírez-Correa

2021

Symmetry

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The blood donation process is essential for health systems. Therefore, the ability to predict donor flow has become relevant for hospitals. Although it is possible to predict this behaviour intention from donor questionnaires, the need to reduce social contact in pandemic settings leads to decreasing the extension of these surveys with the minimum loss of predictivity. In this context, this study aims to predict the intention to give blood again, among donors, based on a limited number of attributes. This research uses data science and learning concepts based on symmetry in a particular classification to predict blood donation intent. We carried out a face-to-face survey of Chilean donors based on the Theory of Planned Behaviour. These data, including control variables, were analysed using the decision tree technique. The results indicate that it is possible to predict the intention to donate blood again with an accuracy of 84.17% and minimal variables. The added scientific value of this article is to propose a more simplified way of measuring a multi-determined social phenomenon, such as the intention to donate blood again and the application of the decision tree technique to achieve this simplification, thereby contributing to the field of data science.

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Leveraging Data Science to Combat COVID-19: A Comprehensive Review

Cited by 164 publications

References 125 publications

Battling COVID-19 using machine learning: A review

Battling COVID-19 using machine learning: A review

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

Predicting the Intention to Donate Blood among Blood Donors Using a Decision Tree Algorithm

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