Leveraging Health Analytics to Address Public Health Crises: A Comprehensive Analysis
Downloads
Public health crises, whether originating from pandemics, natural disasters, bioterrorism, or other emergent threats, present significant challenges to global health systems, necessitating rapid and organized responses. Health analytics, encompassing the application of data analytics, machine learning, and statistical models to health-related data, has emerged as an essential tool for predicting, managing, and mitigating the effects of such crises. In this article, we explore the evolving role of health analytics in addressing public health emergencies by leveraging real-time data, advanced modeling techniques, and large-scale information from various data streams. Health analytics enables public health officials to identify outbreaks earlier, optimize resource distribution, and improve communication strategies with the public. Additionally, we examine ethical considerations around data privacy, the quality of data sources, and challenges in low-resource settings. Through a critical analysis of key case studies, including the COVID-19 pandemic, we investigate how health analytics can revolutionize public health response frameworks, offering opportunities for more dynamic and responsive health systems. While the power of health analytics is profound, its potential is still hampered by infrastructural deficits, data-sharing issues, and significant ethical dilemmas that must be addressed to fully realize its capabilities.
Downloads
1. Adalja, A. A., & Inglesby, T. V. (2019). A review of the 2019 novel coronavirus (SARS-CoV-2) and the role of predictive modeling in managing pandemics. Health Security, 17(2), 126-131. https://doi.org/10.1089/hs.2020.0024
2. Bertozzi, A. L., Franco, V., & Mohler, G. (2020). The role of predictive models in the COVID-19 pandemic response. Journal of Computational Biology, 27(8), 1007-1015. https://doi.org/10.1089/cmb.2020.0158
3. Cinelli, M., Galeazzi, A., & Galeazzi, A. (2020). The COVID-19 social media challenge: Understanding misinformation and its impact. Scientific Reports, 10(1), 1-11. https://doi.org/10.1038/s41598-020-73516-8
4. IHME COVID-19 Health Service Utilization Forecasting Team. (2020). COVID-19 projections for the United States. Nature, 586(7829), 257-261. https://doi.org/10.1038/s41586-020-2404-3
5. Lazer, D. M., Kennedy, R., & King, G. (2021). The ethics of big data: A review of the literature. Annual Review of Political Science, 24, 267-286. https://doi.org/10.1146/annurev-polisci-050718-103215
6. Paltiel, A. D., & Zheng, A. (2020). Assessment of COVID-19 vaccine allocation strategies. JAMA Network Open, 3(6), e209032. https://doi.org/10.1001/jamanetworkopen.2020.9032
7. Ranney, M. L., Jha, A. K., & Hsu, J. (2020). COVID-19 resource allocation: The role of health analytics in managing shortages. New England Journal of Medicine, 383(6), 551-554. https://doi.org/10.1056/NEJMp2023142
8. Wang, J., Ma, W., & Zhang, Z. (2020). Data quality and infrastructure challenges in health analytics. Journal of Healthcare Engineering, 2020, 1-10. https://doi.org/10.1155/2020/8720137
9. Zhang, L., Zhao, J., & Chen, Q. (2020). Addressing data infrastructure limitations in low-resource settings: Lessons from recent health crises. Global Health Action, 13(1), 172-183. https://doi.org/10.1080/16549716.2020.1750352
10. Ramos, L., Bautista, S., & Bonett, M. C. (2021, September). SwiftFace: Real-Time Face Detection: SwitFace. In Proceedings of the XXI International Conference on Human Computer Interaction (pp. 1-5).
11. Patibandla, K. R. (2024). Automate Amazon Aurora Global Database Using Cloud Formation. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), 262-270.
12. Patibandla, K. R. (2024). Design and Create VPC in AWS. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 1(1), 273-282.
13. Esfahani, M. N. Breaking Language Barriers: How Multilingualism Can Address Gender Disparities in US STEM Fields.
14. Thatoi, P. Strategizing P2P Investments using Socio-Economic Factors.
15. Khalili, A., Naeimi, F., & Rostamian, M. Manufacture and characterization of three-component nano-composites Hydroxyapatite Using Polarization Method.
16. Braimoh, J. (2020). The impact of texting language on Nigerian students: a case study of final year linguistics students. Per Linguam: a Journal of Language Learning= Per Linguam: Tydskrif vir Taalaanleer, 36(1), 15-31.
17. Braimoh, J. J. (2006). Examining the Difficulties of Acquiring the Past Subjunctive in L2 French. Hypothesis, 2008, 2013.
18. Braimoh, J. J. (2022). Linguistic Expressions of Pidgin in Nigerian Stand-up Comedy (Doctoral dissertation, The University of Mississippi).
19. Akpotoghogho, A., & Braimoh, J. J. (2024). The Phonetic Challenges of Vowel Elision for Nigerian Students of French for Specific Purpose (FOS). Valley International Journal Digital Library, 3488-3493.
20. BRAIMOH, J. J., & IGBENEGHU, B. Une Etude Syntaxique des Problèmes del’appropriation du Subjonctif Présent par les Apprenants de l’University of Benin au Nigéria.
21. OGUNTOLA, L. O., ANTHONY, H. M., & OYEWUMI, M. B. (2020). E-learning en période de la covid-19: les écoles nigérianes à la loupe. Akofena: Revue scientifique des Sciences du Langage, Lettres, Langues et Communication,(en ligne), consulté le, 22(01), 2022.
22. Amoako, K., Pusey, R. F., Haddad, W. A., Majin, S., Wheba, A., Okwuogori, C., ... & Sanisetty, V. H. (2021). PULM3: The Effects of a Two-step Coating Process and Flow on Artificial Lung Fiber Fouling. ASAIO Journal, 69(Supplement 2), 88.
23. Dave, A., Banerjee, N., & Patel, C. (2023). FVCARE: Formal Verification of Security Primitives in Resilient Embedded SoCs. arXiv preprint arXiv:2304.11489.
24. Patel, A. D. N. B. C. (2023). RARES: Runtime Attack Resilient Embedded System Design Using Verified Proof-of-Execution. arXiv preprint arXiv:2305.03266.
25. Dave, A. (2013). PCIE configuration for data transfer at rate of 2.5-Giga Bytes per Second (GBPS): for data acquisition system.
26. Dave, A. (2021). A Survey of AI-based smart chiplets and interconnects for vehicles. North American Journal of Engineering Research, 2(4).
27. Dave, A. (2021). Distributed Sensors Based In-Vehicle Monitoring and Security. North American Journal of Engineering Research, 2(4).
28. Gurjar, S., Chauhan, V., Suthar, M., Desai, D., Luhar, H., Patel, V., ... & Dave, N. (2022). Digital Eye for Visually Impaired—DEVI. In Intelligent Infrastructure in Transportation and Management: Proceedings of i-TRAM 2021 (pp. 131-139). Springer Singapore.
29. Dave, A., & Dave, K. Chiplet-Based Architecture for Next-Generation Vehicular Systems. J Artif Intell Mach Learn & Data Sci 2023, 1(4), 915-919.
Copyright (c) 2024 Christian Aliyuda
This work is licensed under a Creative Commons Attribution 4.0 International License.
