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

By S. Latif, Muhammad Usman, Sanaullah Manzoor, Waleed Iqbal, Junaid Qadir, Gareth Tyson, Ignacio Castro, Adeel Razi, Maged N. Kamel Boulos, Adrian Weller and 1 more. 2020

238 citations recorded by the source corpus.

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. By mid-August 2020, more than 21 million people have tested positive worldwide. Infections have been growing rapidly and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise the various COVID-19 research activities leveraging data science, where we define data science broadly to encompass the various methods and tools—including those from artificial intelligence (AI), machine learning (ML), statistics, modeling, simulation, and data visualization—that can be used to store, process, and extract insights from data. In addition to reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. We also created a live resource repository at https://github.com/Data-Science-and-COVID-19/Leveraging-Data-Science-To-Combat-COVID-19-A-Comprehensive-Review that we intend to keep updated with the latest resources including new papers and datasets.

Subfields: Artificial Intelligence, Health Information Management, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, unknown): “…TSM based residual feature maps while our work applies two attention modules to the intermediate feature maps generated from regular 2D CNNs with TSM. Machine Learning and COVID-19: Researchers have explored the use of machine learning from various perspectives to help combat COVID-19 [24].”
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020, unknown): “Besides this, X-rays have low ionizing radiations than CT scan [9].”
Spatial analysis and GIS in the study of COVID-19. A review (2020, methodology): “…discussed the different therapies used in COVID-19 patients, while the use of arti ﬁ cial intelligence and big data to study and mitigate effects of the virus was discussed by Pham et al., 2020, and systematisation of ongoing data science activities in this area was done by Latif et al. (2020).”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “1 .”