Online ISSN: 3007-0244,
Print ISSN:  2410-4280
Background. The study focuses on the need for effective strategies to address global challenges, such as the COVID-19 pandemic, which requires not only medical but also technological responses. In this context, the research aims to analyze issues and assess the potential use of artificial intelligence (AI) in predicting the spread of the virus, taking into account current knowledge and identifying gaps in understanding this field. The aim of this study is to analyze scientific publications dedicated to the use of artificial intelligence in the field of forecasting and prevention of COVID-19 infection. Search strategy. The search for literary sources was conducted in the PubMed database, and the selection of scientific works was based on keywords related to the COVID-19 pandemic and forecasting using artificial intelligence technologies. The search yielded 3,894 publications, extracted on December 27, 2023. Bibliometric analysis was performed using VOSviewer software version 1.6.19, visualizing the interconnections between keywords, identifying clusters of similar terms, and facilitating a deeper understanding of the research topic, trends, and directions in the field of artificial intelligence for combating the COVID-19 pandemic. Exclusion of articles not meeting the keyword criteria was done manually. From the initial pool of 3,894 works, a final set of 23 most relevant publications was selected, reflecting the researched theme and meeting the established search criteria. Conclusions. Contemporary trends and prospects of utilizing models and AI for forecasting COVID-19 outbreaks demonstrate an interdisciplinary approach, encompassing statistical analysis, simulation models, machine learning, and intelligent data analysis. The study emphasizes the importance of data quality, the selection of appropriate algorithms based on country-specific data, and the potential of AI to make a significant contribution to decision-making in public health and pandemic management.
Yedil D. Omarbekov1, Sholpan Е. Тоkanova1, Erlan A. Ospanov1, Bauyrzhan А. Nauryzbayev2, Almas Zh. Zhakhiyanov2, 1 NCJSC «Semey Medical University», Semey, Republic of Kazakhstan; 2 Alikhan Bokeikhan University, Semey, Republic of Kazakhstan.
1. Bansal A. [et al.]. Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review // Journal of medical systems. 2020. № 9 (44): 156. 2. Bertacchini F., Bilotta E., Pantano P. S. On the temporal spreading of the SARS-CoV-2 // PLOS ONE. 2020. № 10 (15): e0240777. 3. Borovkov A. I., Bolsunovskaya M. V., Gintciak A.M. Intelligent Data Analysis for Infection Spread Prediction // Sustainability (Switzerland). 2022. № 4 (14): 1995. 4. Bousquet A. [et al.]. Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19 // Scientific Reports. 2022. № 1 (12): 3030. 5. Chen J. [et al.]. A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 // ACM Computing Surveys (CSUR). 2021. № 8 (54): 1-32. 6. Comito C., Pizzuti C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review // Artificial Intelligence in Medicine. 2022. (128). P. 102286. 7. Fard S. G. [et al.]. Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review // Heliyon. 2021. № 10 (7): e08143. 8. Ismail L. [et al.]. Automated artificial intelligence-enabled proactive preparedness real-time system for accurate prediction of COVID-19 infections- Performance evaluation // Frontiers in medicine. 2022. (9): 871885. 9. Jakhar D., Kaur I. Current applications of artificial intelligence for COVID‐19 // Dermatologic Therapy. 2020. № 4 (33): e13654. 10. Jung S.Y. [et al.]. Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation // Journal of medical Internet research. 2020. № 9 (22): e19907. 11. Li A. Guest Editor’s Introduction: COVID-19 and Data Science // Annals of Data Science. 2022. № 5 (9): 885-888. 12. Liao Z. [et al.]. SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD // Computers in Biology and Medicine. 2021. (138): 104868. 13. Mohamadou Y., Halidou A., Kapen P.T. A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19 // Applied Intelligence (Dordrecht, Netherlands). 2020. № 11 (50): 3913. 14. Muñoz-Organero M., Queipo-álvarez P. Deep Spatiotemporal Model for COVID-19 Forecasting // Sensors 2022, Vol. 22, Page 3519. 2022. № 9 (22): 3519. 15. Musulin J. [et al.]. Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review // International journal of environmental research and public health. 2021. № 8 (18): 4287. 16. Niazkar H. R., Niazkar M. Application of artificial neural networks to predict the COVID-19 outbreak // Global Health Research and Policy. 2020. № 1 (5): 1-11. 17. Safari A., Hosseini R., Mazinani M. A novel deep interval type-2 fuzzy LSTM (DIT2FLSTM) model applied to COVID-19 pandemic time-series prediction // Journal of biomedical informatics. 2021. (123): 103920. 18. Shuja J. [et al.]. COVID-19 open source data sets: a comprehensive survey // Applied Intelligence (Dordrecht, Netherlands). 2021. № 3 (51): 1296. 19. Srinivasa Rao A.S.R., Vazquez J.A. Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine // Infection Control & Hospital Epidemiology. 2020. № 7 (41): 826-830. 20. Thomson R., Mosier R., Worosz M. COVID research across the social sciences in 2020: a bibliometric approach // Scientometrics. 2023. № 6 (128): 3377. 21. Ting D.S. W. [et al.]. Digital technology and COVID-19. // Nature Medicine. 2020. № 4 (26): 459–461. 22. Wang T. [et al.]. Artificial intelligence against the first wave of COVID-19: evidence from China // BMC Health Services Research. 2022. № 1 (22): 767. 23. Zhan C. [et al.]. Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data // PLOS ONE. 2020. № 10 (15): e0241171.
Number of Views: 127842

Category of articles: COVID-19 - Topical Subject

Bibliography link

Omarbekov Ye.D., Тоkanova Sh.Е., Ospanov E.A., Nauryzbayev B.А., Zhakhiyanov A.Zh. Application of Artificial Intelligence in combating the COVID-19 pandemic: current trends and prospects. Review // Nauka i Zdravookhranenie [Science & Healthcare]. 2024, (Vol.26) 1, pp. 140-146. doi 10.34689/SH.2024.26.1.018

Авторизируйтесь для отправки комментариев