Machine Learning for COVID-19 needs global collaboration and data-sharing

Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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An interactive web-based dashboard to track COVID-19 in real time

Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048 · see citing works

Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,697 · see citing works

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works

A Deep Learning Approach to Antibiotic Discovery

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463 · see citing works

A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT

Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,237 · see citing works

Phenotype and kinetics of SARS-CoV-2-specific T cells in COVID-19 patients with acute respiratory distress syndrome

Weiskopf, D., Schmitz, K. S., Raadsen, M. P., Grifoni, A., Okba, N. M. A., Endeman, H., van den Akker, J. P. C., Molenkamp, R., Koopmans, M. P. G., van Gorp, E. C. M., et al. (2020) · Science Immunology · doi:10.1126/sciimmunol.abd2071 · Cited by 907 · see citing works

Deep Learning-based Detection for COVID-19 from Chest CT using Weak LabelPreprint

Zheng, C., Deng, X., Fu, Q., et al. (2020) · medRxiv · doi:10.1101/2020.03.12.20027185 · Cited by 727 · see citing works

Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state

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Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2019.09.009 · Cited by 561 · see citing works

Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury

Claassen, J., Doyle, K. W., Matory, A., Couch, C., Burger, K. M., Velazquez, A. G., Okonkwo, J. U., King, J.-R., Park, S., Agarwal, S., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1812757 · Cited by 521 · see citing works

Potential inhibitors against 2019-nCoV coronavirus M protease from clinically approved medicines

Liu, X., & Wang, X. (2020) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2020.02.001 · Cited by 464 · see citing works

Global coalition to accelerate COVID-19 clinical research in resource-limited settings

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Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Peiffer‐Smadja, N., Dellière, S., Rodriguez, C., Birgand, G., Lescure, F.-X., Fourati, S., & Ruppé, É. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2020.02.006 · Cited by 144 · see citing works

High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages

Chen, Z., Fan, H., Cai, J., Li, Y., Wu, B., Hou, Y., Xu, S., Zhou, F., Liu, Y., Xuan, W., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.108972 · Cited by 130 · see citing works

Phenotype of SARS-CoV-2-specific T-cells in COVID-19 patients with acute respiratory distress syndromePreprint

Weiskopf, D., Schmitz, K. S., Raadsen, M. P., et al. (2020) · medRxiv · doi:10.1101/2020.04.11.20062349 · Cited by 92 · see citing works

A brief review of antiviral drugs evaluated in registered clinical trials for COVID-19Preprint

Belhadi, D., Peiffer‐Smadja, N., Lescure, F.-X., Yazdanpanah, Y., Mentré, F., & Laouenan, C. (2020) · medRxiv · doi:10.1101/2020.03.18.20038190 · Cited by 51 · see citing works

Swarm Learning for decentralized and confidential clinical machine learning

Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03583-3 · Cited by 884 · see citing works

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00431-6 · Cited by 278 · see citing works

Open science saves lives: lessons from the COVID-19 pandemic

Besançon, L., Peiffer‐Smadja, N., Ségalas, C., Jiang, H., Masuzzo, P., Smout, C., Billy, E., Déforet, M., & Leyrat, C. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01304-y · Cited by 288 · see citing works

Applications of artificial intelligence in battling against covid-19: A literature review

Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210 · see citing works

HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

Jiang, M., Wang, Z.-R., & Dou, Q. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19993 · Cited by 174 · see citing works

Advances in Artificial Intelligence for Infectious-Disease Surveillance

Brownstein, J. S., Rader, B. M., Astley, C. M., & Tian, H. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2119215 · Cited by 173 · see citing works

Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising

Qiu, L., Cheng, J., Gao, H., Xiong, W., & Ren, H. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3274498 · Cited by 98 · see citing works

Labelling instructions matter in biomedical image analysis

Rädsch, T., Reinke, A., Weru, V., Tizabi, M. D., Schreck, N., Kavur, A. E., Pekdemir, B., Roß, T., Kopp‐Schneider, A., & Maier‐Hein, L. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00625-5 · Cited by 90 · see citing works

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study

Kamran, F., Tang, S., Otles, E., McEvoy, D. S., Saleh, S. N., Gong, J., Li, B. Y., Dutta, S., Liu, X., Medford, R. J., et al. (2022) · BMJ · doi:10.1136/bmj-2021-068576 · Cited by 60 · see citing works

Open Science Saves Lives: Lessons from the COVID-19 PandemicPreprint

Besançon, L., Peiffer‐Smadja, N., Ségalas, C., Jiang, H., Masuzzo, P., Smout, C., Billy, É., Déforet, M., & Leyrat, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.13.249847 · Cited by 59 · see citing works

Virtual state, where are you? A literature review, framework and agenda for failed digital transformation

Kempeneer, S., & Heylen, F. (2023) · Big Data & Society · doi:10.1177/20539517231160528 · Cited by 60 · see citing works

A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic

Rubab, S., Khan, M. M. Z. M., Uddin, F., Bangash, Y. A., & Taqvi, S. A. A. (2022) · ChemBioEng Reviews · doi:10.1002/cben.202100044 · Cited by 47 · see citing works

Artificial intelligence in the COVID-19 pandemic: balancing benefits and ethical challenges in China’s response

Ding, X., Shang, B., Xie, C., Xin, J., & Yu, F. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04564-x · Cited by 40 · see citing works

Survey on Privacy-Preserving Techniques for Microdata Publication

Carvalho, T., Moniz, N., Faria, P., & Antunes, L. F. (2023) · ACM Computing Surveys · doi:10.1145/3588765 · Cited by 47 · see citing works

AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions

Karthik, R., Menaka, R., Hariharan, M., & Kathiresan, G. S. (2021) · IRBM · doi:10.1016/j.irbm.2021.07.002 · Cited by 30 · see citing works

Battling COVID-19 using machine learning: A review

Chadaga, K., Prabhu, S., Vivekananda Bhat, K., Niranjana, S., & Umakanth, S. (2021) · Cogent Engineering · doi:10.1080/23311916.2021.1958666 · Cited by 30 · see citing works

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis—Introducing the Multitask Speech Corpus for COVID-19

Qian, K., Schmitt, M., Zheng, H., Koike, T., Han, J., Liu, J., Ji, W., Duan, J., Song, M., Yang, Z., et al. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3067605 · Cited by 28 · see citing works

COVID-19 detection based on self-supervised transfer learning using chest X-ray images

Li, G., Togo, R., Ogawa, T., & Haseyama, M. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02813-x · Cited by 28 · see citing works

Detection and classification of pneumonia in chest X-ray images by supervised learning

Parveen, S., & Khan, K. B. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318118 · Cited by 19 · see citing works

AI-Based Predictive Modelling of the Onset and Progression of Dementia

Hanke, S., Mangialasche, F., Bödenler, M., Neumayer, B., Ngandu, T., Mecocci, P., Untersteiner, H., & Stögmann, E. (2022) · Smart Cities · doi:10.3390/smartcities5020036 · Cited by 18 · see citing works

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia

Liu, Q., Pang, B., Li, H., Zhang, B., Liu, Y., Lai, L., Le, W., Li, J.-Y., Xia, T., Zhang, X., et al. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-20-2580 · Cited by 17 · see citing works

Neural network training method for materials science based on multi-source databases

Guo, J., Chen, Z., Liu, Z., Li, X., Xie, Z. Y., Wang, Z., & Wang, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-19426-8 · Cited by 17 · see citing works

Predicting special care during the COVID-19 pandemic: a machine learning approach

Bezzan, V. P., & Rocco, C. D. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00164-6 · Cited by 16 · see citing works

Characterization and Quantification of User Privacy: Key Challenges, Regulations, and Future Directions

Arshad, R., & Asghar, M. R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3519861 · Cited by 17 · see citing works

Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses

Magazzino, C., Gattone, T., & Giolli, L. (2024) · Energy Ecology and Environment · doi:10.1007/s40974-024-00325-9 · Cited by 14 · see citing works

CoLLaRS : A cloud–edge–terminal collaborative lifelong learning framework for AIoT

Hu, S., Lin, J., Lu, Z., Du, X., Duan, Q., & Huang, S.-C. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.04.046 · Cited by 14 · see citing works

The Machine‐Learning Approach

Mishra, A. K. (2020) · Wiley eBooks · doi:10.1002/9781119602927.ch2 · Cited by 12 · see citing works

Single-Cell Analysis for Whole-Organism Datasets

Pisco, A. O., Tojo, B. M., & McGeever, A. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-031008 · Cited by 10 · see citing works

Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling

Li, G., Togo, R., Ogawa, T., & Haseyama, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106877 · Cited by 12 · see citing works

An Innovative AI-based primer design tool for precise and accurate detection of SARS-CoV-2 variants of concern

Perez-Romero, C. A., Mendoza‐Maldonado, L., Tonda, A. P., Coz, E., Tabeling, P., Vanhomwegen, J., MacSharry, J. A., Szafran, J., Bobadilla‐Morales, L., Corona‐Rivera, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-42348-y · Cited by 11 · see citing works

Non-IID Medical Imaging Data on COVID-19 in the Federated Learning Framework: Impact and Directions

Al-Hafiz, F., & Basuhail, A. A. (2024) · COVID · doi:10.3390/covid4120140 · Cited by 10 · see citing works

Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities

Wagner, F., Xu, W.-T., Saha, P., Liang, Z.-Y., Whitehouse, D., Menon, D., Newcombe, V. F. J., Voets, N., Noble, J., & Kamnitsas, K. (2024) · IEEE Workshop/Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00045 · Cited by 16 · see citing works

ICU Resource Limitations During Peak Seasonal Influenza: Results of a 2018 National Feasibility Study

Lane, C. J., Bhatnagar, M., Lutrick, K., Maves, R. C., Weiner, D., Olvera, D. R., Uyeki, T. M., Cobb, J. P., Brown, J. C., & PREP), F. T. D. P. R. A. E. P. (. (2022) · Critical Care Explorations · doi:10.1097/cce.0000000000000606 · Cited by 10 · see citing works

DISCOVID: discovering patterns of COVID-19 infection from recovered patients: a case study in Saudi Arabia

Alafif, T., Etaiwi, A., Hawsawi, Y. M., Alrefaei, A. F., Albassam, A., & Althobaiti, H. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00973-2 · Cited by 9 · see citing works

An amalgamation of bioinformatics and artificial intelligence for COVID-19 management: From discovery to clinic

Wang, J., Chavda, V. P., Prajapati, R., Bedse, A. P., Patel, J., Popat, S., Jogi, G., Nalla, L. V., Jetha, K. L., Shen, B., et al. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100159 · Cited by 10 · see citing works

Using multiagent modeling to forecast the spatiotemporal development of the COVID-19 pandemic in Poland

Pałka, P., Olszewski, R., Kęsik-Brodacka, M., Wendland, A., Nowak, K., Szczepankowska-Bednarek, U., & Liebers, D. T. (2022) · Scientific Reports · doi:10.1038/s41598-022-15605-9 · Cited by 9 · see citing works

The Case for Integrated Advanced Technology in Applied Behavior Analysis

Neely, L. C., Carnett, A., Quarles, J. P., MacNaul, H. L., Park, S., Oyama, S., Chen, G., Desai, K., & Najafirad, P. (2022) · Advances in Neurodevelopmental Disorders · doi:10.1007/s41252-022-00309-y · Cited by 10 · see citing works

Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana

Kades, K., Scherer, J., Zenk, M., Kempf, M., & Maier-Hein, K. H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18523-6_13 · Cited by 8 · see citing works

Global data sharing of SARS-CoV-2 based on blockchain

Sajedi, H., & Mohammadipanah, F. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01431-3 · Cited by 8 · see citing works

The Effect of Annotation Quality on Wear Semantic Segmentation by CNN

Bilal, M., Podishetti, R., Koval, L., Gaafar, M., Großmann, D., & Bregulla, M. (2024) · Sensors · doi:10.3390/s24154777 · Cited by 7 · see citing works

A novel federated learning framework for medical imaging: Resource‐efficient approach combining PCA with early stopping

Nanekaran, N. P., & Ukwatta, E. (2025) · Medical Physics · doi:10.1002/mp.18064 · Cited by 8 · see citing works

An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study

Brown, J. C., Bhatnagar, M., Gordon, H., Goodner, J., Cobb, J. P., & Lutrick, K. (2022) · JMIR Human Factors · doi:10.2196/35032 · Cited by 6 · see citing works

How can we support the UN Sustainable Development Goals when open data is stagnant?

Li, J., Wu, C., Piao, Y., Qin, Y., Du, X., Zhang, L., & Guo, H. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.05.021 · Cited by 6 · see citing works

A deep dive into enhancing sharing of naturalistic driving data through face deidentification

Thapa, S., & Sarkar, A. (2024) · The Visual Computer · doi:10.1007/s00371-024-03552-7 · Cited by 7 · see citing works

Application of fuzzy logic in multi-sensor-based health service robot for condition monitoring during pandemic situations

Rout, A., Mahanta, G. B., Biswal, B. B., Renin Francy, T., Raj, S. V., & Deepak, B. B. V. L. (2024) · Robotic Intelligence and Automation · doi:10.1108/ria-07-2023-0091 · Cited by 5 · see citing works

Projecting the Short-Term Trend of COVID-19 in Iraq

Aldeer, M. M. N., Hilli, A. A., & Ismail, I. S. (2020) · Digital Government Research and Practice · doi:10.1145/3431769 · Cited by 5 · see citing works

Governance of Collaborative AI Development Strategies

Wiesmüller, S., & Bauer, M. (2023) · CSR, sustainability, ethics & governance · doi:10.1007/978-3-031-09245-9_4 · Cited by 4 · see citing works

Hospital sewage treatment facilities witness the fighting against the COVID-19 pandemic

Li, Z., Wang, J.-X., Lü, M., Zhang, T., Wang, X. C., Li, W., & Yu, H. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.114728 · Cited by 4 · see citing works

An interpretable machine learning model for covid-19 screening

Pinasco, G. C., Farina, E. M. J. D. M., Filho, F. N. B., Fiorotti, W. F., Ferreira, M. C. M., Cruz, S. C. D. S., Colodette, A. L., Loureiro, L. R., Comério, T., Farias, D. C. S., et al. (2022) · Journal of Human Growth and Development · doi:10.36311/jhgd.v32.13324 · Cited by 4 · see citing works

Can Social Cognitive Theory Reduce Gender Inequality in Digitization among Women in Agricultural Activities in Tanzania?

John, Y. (2025) · Journal of Cognition and Culture · doi:10.1163/15685373-12340199 · Cited by 3 · see citing works

FUSION: Uncertainty‐Guided Federated Semi‐Supervised Learning for Medical Image Segmentation

Raheem, A., Yang, Z., Yu, H., Manan, M. A., Sabah, F., & Ahmed, S. (2025) · IET Image Processing · doi:10.1049/ipr2.70147 · Cited by 3 · see citing works

Call for better systems and data to support artificial intelligence for pandemic response

Bates, D. W., Syrowatka, A., Rhee, K. B. L., Jackson, G. P., Alfayez, A., & Kunz, H. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100506 · Cited by 3 · see citing works

Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility NetworksPreprint

Hao, S., Xu, Y., Mukherjee, U. K., Seshadri, S., Ahsen, M. E., Ivanov, A., Souyris, S., Bose, S., & Sridhar, P. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3858274 · Cited by 3 · see citing works

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements

Molamohammadi, M., Taïk, A., Roux, N. L., & Farnadi, G. (2023) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3617694.3623256 · Cited by 2 · see citing works

‘Leading by Science’ through Covid-19: the GDPR & Automated Decision-Making

Mourby, M. J. (2021) · International Journal for Population Data Science · doi:10.23889/ijpds.v5i4.1402 · Cited by 2 · see citing works

Chest X-Ray Pneumonia Detection Using Federated Learning : *Privacy-Preserving Deep Learning for Medical Image Classification

Archana, K. S., Shah, V. H., & Joseph, K. (2025) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit65521.2025.11166991 · Cited by 1 · see citing works

Repurposing of Anti-Infectives for the Management of Onchocerciasis Using Machine Learning and Protein Docking Studies

Tetteh, C. N. O.-K., Mensah, A. A., Ampomah, B., Oppong, M. B., Lartey, M., Donkor, P. O., Opuni, K. F. M., & Adutwum, L. A. (2025) · Bioinformatics and Biology Insights · doi:10.1177/11779322251368252 · Cited by 1 · see citing works

Molecular Multi-target Approach on COVID-19 for Designing Novel Chemicals

Kumar, P., & Ghosh, I. (2021) · Methods in pharmacology and toxicology · doi:10.1007/7653_2020_52 · Cited by 1 · see citing works

Data Empowerment of Decision-Makers in an Era of a Pandemic: Intersection of “Classic” and Artificial Intelligence in the Service of Medicine

Geva, G. A., Ketko, I., Nitecki, M., Simon, S., Inbar, B., Toledo, I., Shapiro, M. J., Vaturi, B., Votta, Y., Filler, D., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/24295 · Cited by 1 · see citing works

Organizing Global Democratic Collaboration in Crisis Contexts: The International Triangulation System

Markopoulos, E., Kirane, I. S., Balaj, D., & Vanharanta, H. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80094-9_25 · Cited by 1 · see citing works

Privacy Preserving K-Means Clustering: A Secure Multi-Party Computation ApproachPreprint

Ramírez, D. H., & Auñón, J. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10453 · Cited by 1 · see citing works

Forecasting HIV/AIDS Incidence in Ghana: A Retrospective Observational Study Using Ensemble Machine Learning ModelsPreprint

Ghanem, V. G. (2025) · Research Square · doi:10.21203/rs.3.rs-6639193/v1 · Cited by 1 · see citing works

SARS-Cov-2 RNA Sequence Classification Based on Territory InformationPreprint

Liu, J.-W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03323

A Self-Assembling Blockchain-Based Database in Global Data Sharing of SARS-CoV-2Preprint

Sajedi, H., Mohammadipanah, F., & Davoodzadeh, M. (2021) · Research Square · doi:10.21203/rs.3.rs-631306/v1

Time for a voluntary crisis research service

Schultze, J. L., Gabriel, M., & Nicotera, P. (2022) · Cell Death and Differentiation · doi:10.1038/s41418-022-00968-3

Topical Repute on Artificial Intelligence-Based Approaches in COVID-19 Supervision

Basu, S., & Shafi, J. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-3791-9.ch008

Machine learning methods applied to COVID-19 patient data

Bezzan, V. P. (2021) · doi:10.47749/t/unicamp.2021.1232336

A study of learning models for COVID-19 disease prediction

Jain, S., & Roy, P. K. (2024) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-024-04775-1

Use of Machine Learning in Diagnosing COVID-19 Infection

Badar, M. S., Kar, B., Dehury, B., Mishra, S., Shamim, S. A., & Alam, M. A. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815256536124010012 · Cited by 1 · see citing works

Tools und Apps für die Physiotherapie

Lübken, A., & Wiemer, M. (2025) · Springer eBooks · doi:10.1007/978-3-662-70813-2_5

COVID-19 pandemic: A worldwide critical review with the machine learning model-based prediction

Misra, N. K., Das, S., Satpathy, S., Addula, S. R., & Trivedi, M. C. (2025) · Journal of The Institution of Engineers (India): Series B · doi:10.1007/s40031-024-01155-3 · Cited by 11 · see citing works

Global Integration and Distribution of Data Through Machine Learning for COVID-19

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Mapping the landscape of Artificial Intelligence applications against COVID-19

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