Neural networks to model COVID-19 dynamics and allocate healthcare resources

被引：0

作者：

Abdullah Ali H. Ahmadini ^{[1
]}

Yashpal Singh Raghav ^{[1
]}

Ali M. Mahnashi ^{[1
]}

Khalid Ul Islam Rather ^{[2
]}

Irfan Ali ^{[3
]}

机构：

[1] Department of Mathematics, College of Science, Jazan University, P.O. Box. 114, Jazan

[2] Division of Statistics & Computer Science, SKUAST-Jammu, Jammu

[3] Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh

来源：

Scientific Reports | / 15卷 / 1期

关键词：

COVID-19; forecasting; Epidemiological modeling; Healthcare resource optimization; Machine learning; Neural networks;

D O I：

10.1038/s41598-025-00153-9

中图分类号：

学科分类号：

摘要：

This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating epidemiological, mobility, vaccination, and environmental data and enables dynamic resource allocation. The results demonstrate significant improvements in forecasting performance and healthcare preparedness compared to traditional models. This work enhances decision-making in pandemic management by leveraging machine learning for real-time operational efficiency. © The Author(s) 2025.

引用

共 33 条

[11] Xu B., Kraemer M.U., Open access epidemiological data from the COVID-19 outbreak, Lancet. Infect. Dis, 20, 5, (2020)
[12] Kar S.K., Ransing R., Arafat S.M.Y., Menon V., Second wave of COVID-19 pandemic in India: barriers to effective governmental response, Epidemiol. Infect, 148, (2020)
[13] Bertsimas D., Digalakis V., Jacquillat A., COVID-19 pandemic: optimal control, vaccine prioritization, and impact of the delay, PLOS ONE, 15, 12, (2020)
[14] Chakraborty T., Ghosh I., Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis, Chaos Solitons Fractals, 135, (2020)
[15] Chimmula V.R., Zhang L., Time series forecasting of COVID-19 transmission in China using LSTM networks, Computers Mater. Continua, 63, 2, pp. 1107-1122, (2020)
[16] Zhou H., Xu Y., Zhang X., Modeling the dynamics of COVID-19 transmission using recurrent neural networks, Comput. Biol. Chem, 88, (2020)
[17] Shahid A., Anwar S., Raza M., COVID-19 transmission prediction using CNN and mobility data, Int. J. Environ. Res. Public Health, 18, 7, (2021)
[18] Zhou Z., Wang H., Incorporating vaccination and climate factors in COVID-19 prediction using machine learning, Computational and Mathematical Methods in Medicine, (2022)
[19] Basu S., Sharma A., Mukherjee S., Healthcare resource allocation during COVID-19: A decision support framework using machine learning, Journal of Healthcare Engineering, (2021)
[20] Chen T., Li Y., Zhang Q., Optimization of medical resource allocation using hybrid neural networks and genetic algorithms, Computational and Mathematical Methods in Medicine, (2022)

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