ArticleIEEE transactions on artificial intelligence2020
Leveraging Data Science to Combat COVID-19: A Comprehensive Review.
Article in IEEE transactions on artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
70 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.Journal of medical systems · 2022Pooled it
- Building data science capacity in the public health workforce: a Public Health Data Science Life Cycle framework.Frontiers in public health · 2026Review
- Assessing the impact of interregional mobility on COVID19 spread in Spain using transfer entropy.Scientific reports · 2025Article
- Improving CNN predictive accuracy in COVID-19 health analytics.Scientific reports · 2025Article
- Predicting Escalation of Care for Childhood Pneumonia Using Machine Learning: Retrospective Analysis and Model Development.JMIRx med · 2025Article
- Lung pneumonia severity scoring in chest X-ray images using transformers.Medical & biological engineering & computing · 2024Article
- Short- and Long-Term Predicted and Witnessed Consequences of Digital Surveillance During the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Mask-Transformer-Based Networks for Teeth Segmentation in Panoramic Radiographs.Bioengineering (Basel, Switzerland) · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19.Journal of healthcare informatics research · 2023Article
- Automatic detection of health misinformation: a systematic review.Journal of ambient intelligence and humanized computing · 2023Article
- Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.Computers in biology and medicine · 2023Article
- MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.Expert systems with applications · 2023Article
- Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model.Expert systems with applications · 2023Article
- Selective Deeply Supervised Multi-Scale Attention Network for Brain Tumor Segmentation.Sensors (Basel, Switzerland) · 2023Article
- Densely attention mechanism based network for COVID-19 detection in chest X-rays.Scientific reports · 2023Article
- A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms.Multimedia tools and applications · 2023Article
- Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.International journal of data science and analytics · 2023Article
- Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.BMC infectious diseases · 2022Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.