Articles Open Access
Big Data Analytics Frameworks for Pandemic Prediction and Healthcare Resource Allocation Management
Government Degree College Khairatabad – Hyderabad, India
Abstract
Big Data Analytics frameworks have become essential tools for pandemic prediction and healthcare resource allocation management in modern public health systems. These frameworks integrate distributed computing, machine learning, artificial intelligence, and real-time data processing techniques to analyze large-scale and heterogeneous datasets generated from hospitals, laboratories, mobility systems, social media platforms, and public health surveillance networks. By processing structured and unstructured data, big data systems can identify early outbreak signals, track disease transmission patterns, and predict future infection trends with high accuracy. In addition, these frameworks support healthcare resource allocation by optimizing the distribution of medical supplies, hospital beds, ventilators, vaccines, and healthcare personnel during pandemics. Technologies such as Hadoop, Apache Spark, cloud computing platforms, and real-time streaming systems enable scalable and efficient data processing. Predictive models such as time-series forecasting, regression analysis, and deep learning-based epidemic models enhance decision-making capabilities for healthcare authorities. Despite their benefits, challenges such as data privacy, data heterogeneity, model uncertainty, infrastructure limitations, and real-time processing constraints remain significant. This study explores big data analytics frameworks, literature contributions, methodologies, advantages, and limitations in pandemic prediction and healthcare resource management systems.
Keywords
DOI
PDF Download
Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 4 |
| Number | 5 |
| Pages | 6851-6858 |
| Published | 2020-09-05 |
| Issue | Vol. 4 No. 5 (2020): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
2. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(3), 1–10.
3. Khan, S., Liu, X., & Shakil, K. A. (2016). Big data analytics for healthcare. International Journal of Distributed Sensor Networks, 12(3), 1–12.
4. Wang, H., & Wang, S. (2015). Big data analytics in healthcare systems. IEEE Access, 3, 2056–2069.
5. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.
6. Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2015). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88–95.
7. Li, J., & Wang, X. (2016). Big data analytics in public health. Journal of Biomedical Informatics, 60, 251–259.
8. Belle, A., Thiagarajan, R., Soroushmehr, S., Navidi, F., Beard, D., & Najarian, K. (2015). Big data analytics in healthcare. BioMed Research International, 2015.
9. Sun, J., & Reddy, C. K. (2013). Big data analytics in healthcare. ACM KDD, 1021–1029.
10. Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.
11. Jagadish, H. V. (2015). Big data and science: Myths and reality. Big Data Research, 2(2), 49–52.
12. Kaur, H., & Singh, S. (2016). Healthcare analytics using big data. Procedia Computer Science, 85, 342–348.
13. Dey, N., Ashour, A. S., & Bhatt, S. (2017). Big data analytics in healthcare systems. Springer Handbook of Healthcare Analytics.
14. Khan, R. A., & Uddin, S. (2018). Big data analytics in healthcare: Applications and challenges. Journal of King Saud University – Computer and Information Sciences, 30(3), 354–365.
15. Ginsberg, J., et al. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.
16. Google Flu Trends Team. (2010). Assessing flu trends using big data. Nature, 463, 116–118.
17. Shaman, J., & Karspeck, A. (2012). Forecasting influenza epidemics. PNAS, 109(50), 20425–20430.
18. Nsoesie, E. O., Marathe, M. V., & Brownstein, J. S. (2014). Forecasting global infectious disease spread. Epidemics, 7, 27–37.
19. Yang, W., et al. (2015). Real-time influenza forecasting. Scientific Reports, 5, 1–9.
20. Paul, M. J., & Dredze, M. (2011). You are what you tweet: Social media for public health. ICWSM Proceedings, 265–272.
21. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
22. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
26. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
27. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
28. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
30. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
33. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
38. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
2. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(3), 1–10.
3. Khan, S., Liu, X., & Shakil, K. A. (2016). Big data analytics for healthcare. International Journal of Distributed Sensor Networks, 12(3), 1–12.
4. Wang, H., & Wang, S. (2015). Big data analytics in healthcare systems. IEEE Access, 3, 2056–2069.
5. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219.
6. Zhang, Y., Qiu, M., Tsai, C. W., Hassan, M. M., & Alamri, A. (2015). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88–95.
7. Li, J., & Wang, X. (2016). Big data analytics in public health. Journal of Biomedical Informatics, 60, 251–259.
8. Belle, A., Thiagarajan, R., Soroushmehr, S., Navidi, F., Beard, D., & Najarian, K. (2015). Big data analytics in healthcare. BioMed Research International, 2015.
9. Sun, J., & Reddy, C. K. (2013). Big data analytics in healthcare. ACM KDD, 1021–1029.
10. Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.
11. Jagadish, H. V. (2015). Big data and science: Myths and reality. Big Data Research, 2(2), 49–52.
12. Kaur, H., & Singh, S. (2016). Healthcare analytics using big data. Procedia Computer Science, 85, 342–348.
13. Dey, N., Ashour, A. S., & Bhatt, S. (2017). Big data analytics in healthcare systems. Springer Handbook of Healthcare Analytics.
14. Khan, R. A., & Uddin, S. (2018). Big data analytics in healthcare: Applications and challenges. Journal of King Saud University – Computer and Information Sciences, 30(3), 354–365.
15. Ginsberg, J., et al. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.
16. Google Flu Trends Team. (2010). Assessing flu trends using big data. Nature, 463, 116–118.
17. Shaman, J., & Karspeck, A. (2012). Forecasting influenza epidemics. PNAS, 109(50), 20425–20430.
18. Nsoesie, E. O., Marathe, M. V., & Brownstein, J. S. (2014). Forecasting global infectious disease spread. Epidemics, 7, 27–37.
19. Yang, W., et al. (2015). Real-time influenza forecasting. Scientific Reports, 5, 1–9.
20. Paul, M. J., & Dredze, M. (2011). You are what you tweet: Social media for public health. ICWSM Proceedings, 265–272.
21. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
22. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
26. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
27. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
28. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
30. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
33. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
38. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
Statistics
52
Views
56
Downloads