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

A novel coronavirus from patients with pneumonia in China, 2019

Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244 · see citing works

Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis

Rodriguez-Morales, A. J., Cardona-Ospina, J. A., Gutiérrez-Ocampo, E., Villamizar-Peña, R., Holguin-Rivera, Y., Escalera-Antezana, J. P., Alvarado-Arnez, L. E., Bonilla-Aldana, D. K., Franco-Paredes, C., Henao-Martinez, A. F., et al. (2020) · Travel Medicine and Infectious Disease · doi:10.1016/j.tmaid.2020.101623 · Cited by 2,542 · see citing works

Analysis and forecast of COVID-19 spreading in China, Italy and France

Fanelli, D., & Piazza, F. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.109761 · Cited by 963 · see citing works

Can we contain the COVID-19 outbreak with the same measures as for SARS?

Wilder‐Smith, A., Chiew, C. J., & Lee, V. J. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30129-8 · Cited by 961 · see citing works

Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110059 · Cited by 845 · see citing works

Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model

Chatterjee, K., Chatterjee, K., Kumar, A., & Shankar, S. (2020) · Medical Journal Armed Forces India · doi:10.1016/j.mjafi.2020.03.022 · Cited by 386 · see citing works

Current epidemiological and clinical features of COVID-19; a global perspective from China

Tu, H., Tu, S., Gao, S., Shao, A., & Sheng, J. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.04.011 · Cited by 284 · see citing works

Evidence for Limited Early Spread of COVID-19 Within the United States, January–February 2020

CDC COVID-19 Response Team, Jorden, M. A., Rudman, S. L., Villarino, E., Hoferka, S., Patel, M. T., Bemis, K., Simmons, C. R., Jespersen, M., Johnson, J. I., et al. (2020) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6922e1 · Cited by 168 · see citing works

Analysis on novel coronavirus (COVID-19) using machine learning methods

Yadav, M., Perumal, M., & Srinivas, M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110050 · Cited by 160 · see citing works

Optimal Control Design of Impulsive SQEIAR Epidemic Models with Application to COVID-19

Abbasi, Z., Zamani, I., Mehra, A. H. A., Shafieirad, M., & Ibeas, A. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110054 · Cited by 107 · see citing works

A deep transfer learning approach for improved post-traumatic stress disorder diagnosis

Banerjee, D., Islam, K., Xue, K.-Y., Mei, G., Xiao, L., Zhang, G., Xu, R., Lei, C., Ji, S., & Li, J. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-019-01337-2 · Cited by 96 · see citing works

A structured literature review on the interplay between emerging technologies and COVID-19 – insights and directions to operations fields

de Queiroz, M. M., & Wamba, S. F. (2021) · Annals of Operations Research · doi:10.1007/s10479-021-04107-y · Cited by 88 · see citing works

Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19

Kavadi, D. P., Patan, R., Manikandan, R., & Gandomi, A. H. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110056 · Cited by 85 · see citing works

Biometric‐based efficient medical image watermarking in E‐healthcare application

Aparna, P., & Kishore, P. V. V. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2018.5288 · Cited by 78 · see citing works

Particle Swarm Optimization-Based Extreme Learning Machine for COVID-19 Detection

Albadr, M. A. A., Tiun, S. B., Ayob, M. B., & AL‐Dhief, F. T. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10063-x · Cited by 46 · see citing works

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

Albataineh, Z. M., Aldrweesh, F., & Alzubaidi, M. A. (2023) · Cluster Computing · doi:10.1007/s10586-023-03972-5 · Cited by 38 · see citing works

Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning

Yang, C., Zhang, H., & Weng, F. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102953 · Cited by 30 · see citing works

Impact of trimet graph optimization topology on scalable networks

Amiripalli, S. S., & Bobba, V. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-169954 · Cited by 22 · see citing works

Advancing COVID-19 Prediction with Deep Learning Models: A Review

John-Otumu, A. M., Ikerionwu, C., Olaniyi, O. O., Dokun, O., Eze, U. F., & Nwokonkwo, O. C. (2024) · International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) · doi:10.1109/seb4sdg60871.2024.10630186 · Cited by 12 · see citing works

Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review

dos Santos, A. L., Pinhati, C. C., Perdigão, J., Galante, S. C., Silva, L., Veloso, I. Y., Silva, A. C. S. E., & OLIVEIRA, E. A. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102824 · Cited by 8 · see citing works

Performance analysis of MEMS sensor for the detection of cholera and diarrhea

Vineetha, K. V., Sravani, K. G., Sailaja, B. V. S., Kumar, P. A., Guha, K., Kotamraju, S. K., Prabhakar, D., & Rao, K. S. (2018) · Microsystem Technologies · doi:10.1007/s00542-018-3810-9 · Cited by 8 · see citing works

Design, simulation and performance analysis of MEMS based bio-sensors for the detection of cholera and diarrhea

Vineetha, K. V., Sravani, K. G., Sailaja, B. V. S., Guha, K., Varma, P. S., & Rao, K. S. (2018) · Microsystem Technologies · doi:10.1007/s00542-018-3880-8 · Cited by 7 · see citing works

A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19

Christodoulou, A., Katsarou, M., Emmanouil, C., Gavrielatos, M., Georgiou, D., Tsolakou, A., Papasavva, M., Economou, V., Nanou, V., Nikolopoulos, I., et al. (2024) · BioTech · doi:10.3390/biotech13030022 · Cited by 5 · see citing works

COVID-19 seroprevalence estimation and forecasting in the USA from ensemble machine learning models using a stacking strategy

Sagastabeitia, G., Doncel, J., Aguilar, J., Anta, A. F., & Ramírez, J. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124930 · Cited by 4 · see citing works

Identifying Factors Associated with COVID-19 All-Cause 90-Day Readmission: Machine Learning Approaches

Lin, S., Tao, S., Huang, Y., Li, X., & Zhang, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66538-7_9 · Cited by 1 · see citing works

Mental health burden among healthcare workers in Kintampo North Municipal Hospital: A descriptive analysis of stress, depression, and anxiety based on Job Demands-Resources (JD-R) model

Zakaria, M., Bardoe, D., Bio, R. B., Yar, D. D., & Hayford, D. (2025) · PLOS mental health. · doi:10.1371/journal.pmen.0000478 · Cited by 3 · see citing works

Coordinated urban system development and urban health equity in Chinese cities

Lai, X., Zhang, Y., Liu, P., Ma, X., Qi, D., & Fukuda, H. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107902 · Cited by 2 · see citing works

Machine Learning and Artificial Intelligence Predictive Models for Viral Genome and Human Proteome Interactions

Hays, P. V. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1010637 · Cited by 1 · see citing works

Unlocking New Insights for Artificial Intelligence in Applied Psychology: A Bibliometric Analysis and Knowledge Mapping Study

Daovisan, H. (2025) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-025-00587-y · Cited by 1 · see citing works

The AI Hospital Formulary: A Practical Governance Framework for Prescribing, Monitoring, and Deprescribing Artificial Intelligence in Hospitals

Epelde, F. (2026) · Hospitals · doi:10.3390/hospitals3030015

Research Progress of Machine Learning in Pneumonia Death Prediction Model

申, 艳. (2025) · Hans Journal of Biomedicine · doi:10.12677/hjbm.2025.153052

Advanced Battery Monitoring System with Sensor Integration and Remote IoT Access

Ahmed, A., Vinoth, N., Abinesh, G., Rajasekaran, G. R., Ali, M. M. S., & Rajest, S. S. (2025) · Bonfring International Journal of Advances in Image Processing · doi:10.9756/bijaip/v15i1/bij25003

Machine Learning Algorithms for Predicting Drug Efficacy and Adverse Effects Based on Individual Genetic Profiles

Tamrakar, P., Sonali, P., Patil, S., Jadhav, B. R., M, V. T. R. P. K., & Priyanka, T. P. (2025) · International Conference on Data Intelligence and Cognitive Informatics (ICDICI) · doi:10.1109/icdici66477.2025.11135243

Prompt Engineering Strategies for Enhancing Domain Adaptation in Generative AI Systems

Ayyamgari, S., Dash, S., Gore, S., Sharma, S., Keerthika, J., & Karthikayen, A. (2025) · International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) · doi:10.1109/mosicom67153.2025.11398249

Rainfall-driven disruptions in respiratory epidemic surveillance: Evidence from COVID-19 in France and New York State

Caruso, S., Ascione, C., & Valdano, E. (2026) · PLOS Climate · doi:10.1371/journal.pclm.0000894

A Systematic Review of Artificial Intelligence for Software Requirements in Open-Source Software

Alharbi, A., & Slhoub, K. (2026) · IEEE Access · doi:10.1109/access.2026.3715394

A Survey on the Use of AI and ML for Fighting the COVID-19 PandemicPreprint

Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. K. M. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07449 · Cited by 7 · see citing works

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Alafif, T., Tehame, A. M., Bajaba, S. M., Barnawi, A., & Zia, S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18031117 · Cited by 162 · see citing works

Application of machine learning in the fight against the COVID-19 pandemic: A review

Čolaković, A., Avdagić-Golub, E., Begović, M., Memić, B., & Džubur, A. H. (2021) · AFMN Biomedicine · doi:10.5937/afmnai39-38354

Applications of machine learning approaches to combat COVID-19: A survey

Tiwari, S. M., Doğan, O., Jabbar, M. A., Shandilya, S. K., Ortiz, F., Bajpai, S., & Banerjee, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-99878-9.00014-5 · Cited by 19 · see citing works

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic

Islam, M. N., Inan, T. T., Rafi, S., Akter, S. S., Sarker, I. H., & Islam, A. K. M. N. (2020) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3062771 · Cited by 99 · see citing works