AI for Crisis Response: Real-Time Predictive Models During COVID-19 Using Cloud Event Streams
Keywords:
COVID-19, artificial intelligence, predictive modeling, cloud computingAbstract
The COVID-19 pandemic has underscored the critical need for intelligent, real-time crisis management systems. This research explores the development and deployment of scalable artificial intelligence models leveraging cloud-native event stream architectures on AWS and Azure platforms. By integrating heterogeneous data sources—such as epidemiological reports, hospital admissions, supply chain metrics, and geospatial mobility patterns—these predictive models facilitated dynamic decision-making for healthcare logistics, medical resource allocation, and public policy interventions. The implementation capitalized on event-driven microservices, serverless compute, and time-series forecasting algorithms to enable low-latency inferencing and high scalability. Furthermore, containerized ML workflows with CI/CD pipelines allowed seamless retraining and deployment in response to evolving data. This paper provides a comprehensive overview of the architectural patterns, data engineering pipelines, and predictive modeling techniques employed, offering valuable insights for real-time crisis response infrastructure.
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References
1. M. R. Islam, M. Hossain, and R. O. Sinnott, “AI-based predictive models for COVID-19 forecasting: A systematic review,” J. Health Eng., vol. 2020, pp. 1–15, 2020.
2. A. Gupta, P. Arora, and P. Gupta, “Cloud-based predictive analytics for healthcare crisis management during COVID-19,” J. Cloud Comput. Adv. Syst., vol. 8, no. 1, pp. 12–25, 2020.
3. R. Anwar, M. Rehman, and H. Zhang, “Time-series prediction models for COVID-19 case forecasting,” J. Comput. Sci. Technol., vol. 35, no. 6, pp. 1200–1212, 2020.
4. S. M. Sarwar, T. M. Shah, and W. Tariq, “AI and machine learning in logistics for pandemic response,” AI Open, vol. 1, no. 4, pp. 125–139, 2020.
5. J. E. Smith, “Using AWS Lambda for cloud-native predictive models in healthcare,” Cloud Computing for Healthcare, pp. 74–85, 2020.
6. R. V. L. Joshi, M. R. Mirza, and D. K. Meena, “Predictive analytics using machine learning for COVID-19 resource allocation,” IEEE Access, vol. 8, pp. 103104–103114, 2020.
7. L. Zhang, Z. Song, and X. Zhang, “Distributed AI systems for healthcare crisis management: A comparative analysis,” J. Appl. Comput. Sci., vol. 25, no. 4, pp. 205–217, 2020.
8. H. J. Lee and S. P. Singhal, “Scalable cloud solutions for healthcare logistics in the COVID-19 pandemic,” International Journal of Cloud Applications and Computing, vol. 10, no. 2, pp. 110–122, 2020.
9. M. D. Smith, T. M. Johnson, and L. A. Kim, “Real-time machine learning applications for hospital resource forecasting during pandemics,” IEEE Trans. Biomed. Eng., vol. 67, no. 5, pp. 1123–1133, May 2020.
10. F. B. Wang, Z. L. Sun, and S. C. Li, “AI for real-time crisis response: A case study during the COVID-19 outbreak,” J. Intell. Syst., vol. 34, no. 1, pp. 56–70, 2020.
11. R. S. Kumar, M. Sharma, and R. Raj, “Cloud-based event-driven architectures for public health emergency management,” J. Cloud Comput., vol. 9, no. 2, pp. 23–45, 2020.
12. A. E. Jones, P. R. Clark, and H. A. Stevens, “Predictive analytics in healthcare logistics: A case study of ventilator distribution optimization during COVID-19,” IEEE Trans. Ind. Inform., vol. 16, no. 3, pp. 1274–1285, 2020.
13. N. S. Patel, S. K. Thakur, and R. P. Gupta, “Impact of machine learning on healthcare resource optimization during the pandemic,” Healthcare Management Review, vol. 45, no. 2, pp. 57–64, 2020.
14. T. A. Williams, M. J. Lee, and S. L. Hart, “Cloud-native approaches for health data analytics and management during COVID-19,” Int. J. Health Technol. Manag., vol. 21, no. 3, pp. 154–170, 2020.
15. M. S. Lu, J. P. Yang, and L. A. Carver, “Big data analytics and AI-driven predictive models in managing COVID-19 healthcare crisis,” IEEE Access, vol. 8, pp. 21043–21053, 2020.
16. A. B. Saha, G. P. Smith, and F. F. Walters, “Deploying AI-driven predictive models using cloud architectures for crisis response management,” J. Emerg. Technol. Health Inform., vol. 34, pp. 42–55, 2020.
17. H. A. Mollah, T. S. Shahid, and H. T. Al-Amin, “Data-driven decision support systems for epidemic management: AI applications in COVID-19,” J. Health Informatics, vol. 28, no. 1, pp. 30–46, 2020.
18. Y. F. Zhang, W. S. Wong, and S. R. Choudhury, “AI and cloud platforms for predictive analytics and real-time forecasting in pandemic management,” IEEE Trans. Cloud Comput., vol. 8, no. 4, pp. 962–973, 2020.
19. L. K. Patel, S. F. Joshi, and V. K. Mehra, “Scalable cloud-native event processing in public health crisis management,” IEEE J. Emerg. Comput., vol. 2, no. 1, pp. 50–65, 2020.
20. S. T. Batra, M. A. Patel, and P. J. Thomas, “Machine learning and cloud infrastructures for COVID-19 crisis resource forecasting and optimization,” Comput. Sci. Appl. Eng., vol. 25, no. 4, pp. 111–124, 2020.
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