A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic

Mardhani Riasetiawan; Ahmad Ashari

doi:10.28991/ESJ-2023-SPER-015

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Mardhani Riasetiawan
mardhani@ugm.ac.id
Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, 55281,, Indonesia https://orcid.org/0000-0002-5832-5559
Ahmad Ashari Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, 55281,, Indonesia

Vol. 7 (2023): Special Issue "COVID-19: Emerging Research"

Special Issue "COVID-19: Emerging Research"

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The COVID-19 pandemic has highlighted the importance of effective knowledge management in mitigating the impact of public health crises. Big data analytics can play a critical role in providing insights and informing decision-making during a pandemic. However, the challenges associated with collecting, analyzing, and managing the data, especially with privacy and security concerns, make it a complex task. This paper proposes a knowledge management framework for COVID-19 mitigation using a big data analytics approach. The framework includes a systematic process for data collection, analysis, and dissemination, as well as a set of best practices for knowledge management. Additionally, the framework complies with data protection and privacy regulations. The proposed framework aims to support public health officials and other stakeholders in effectively managing the COVID-19 pandemic by providing timely and accurate information. It can also be adapted and applied to other public health crises and be a useful tool for addressing the challenges associated with big data analytics in the context of public health. The paper presents the proposed framework in detail and provides components of how the framework can be applied to COVID-19 in Indonesia.

Doi: 10.28991/ESJ-2023-SPER-015

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Riasetiawan, M., & Ashari, A. (2023). A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic. Emerging Science Journal, 7, 214–224. https://doi.org/10.28991/ESJ-2023-SPER-015

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Hasan, I., Dhawan, P., Rizvi, S. A. M., & Dhir, S. (2022). Data analytics and knowledge management approach for COVID-19 prediction and control. International Journal of Information Technology (Singapore). doi:10.1007/s41870-022-00967-0.

WHO. (2021). COVID-19 Global Research & Innovation Forum, 13 &14 May 2021. World Health Organization (WHO), Geneva, Switzerland. Available online: https://www.who.int/teams/blueprint/covid-19/covid-19-global-research-innovation-forum (accessed on January 2023).

Dong, J., Wu, H., Zhou, D., Li, K., Zhang, Y., Ji, H., Tong, Z., Lou, S., & Liu, Z. (2021). Application of Big Data and Artificial Intelligence in COVID-19 Prevention, Diagnosis, Treatment and Management Decisions in China. Journal of Medical Systems, 45(9), 84. doi:10.1007/s10916-021-01757-0.

Miller, A. R., Charepoo, S., Yan, E., Frost, R. W., Sturgeon, Z. J., Gibbon, G., Balius, P. N., Thomas, C. S., Schmitt, M. A., Sass, D. A., Walters, J. B., Flood, T. L., & Schmitt, T. A. (2022). Reliability of COVID-19 data: An evaluation and reflection. PLOS ONE, 17(11), e0251470. doi:10.1371/journal.pone.0251470.

Zwitter, A., & Gstrein, O. J. (2020). Big data, privacy and COVID-19 – learning from humanitarian expertise in data protection. Journal of International Humanitarian Action, 5(1). doi:10.1186/s41018-020-00072-6.

Kaur, S., Bherwani, H., Gulia, S., Vijay, R., & Kumar, R. (2021). Understanding COVID-19 transmission, health impacts and mitigation: timely social distancing is the key. Environment, Development and Sustainability, 23(5), 6681–6697. doi:10.1007/s10668-020-00884-x.

COVID-19 Hotline 119. (2023). COVID-19 Indonesia Task Force. Available online: https://covid19.go.id/ (accessed on January 2023).

Riasetiawan, M., Ashari, A., & Prastowo, B. N. (2021). 360Degree Data Analysis and Visualization for COVID-19 Mitigation in Indonesia. 2021 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA), Medan, Indonesia. doi:10.1109/databia53375.2021.9650174.

Bej, A., Maulik, U., & Sarkar, A. (2022). Time-Series Prediction for the Epidemic Trends of COVID-19 Using Conditional Generative Adversarial Networks Regression on Country-Wise Case Studies. SN Computer Science, 3(5), 352. doi:10.1007/s42979-022-01225-7.

Bilgehan, B., Özyapıcı, A., Hammouch, Z., & Gurefe, Y. (2022). Predicting the spread of COVID-19 with a machine learning technique and multiplicative calculus. Soft Computing, 26(16), 8017–8024. doi:10.1007/s00500-022-06996-y.

Sinha, T., Chowdhury, T., Shaw, R. N., & Ghosh, A. (2022). Analysis and Prediction of COVID-19 Confirmed Cases Using Deep Learning Models: A Comparative Study. Lecture Notes in Networks and Systems, 218, 207–218. doi:10.1007/978-981-16-2164-2_18.

Harkiolakis, N. (2013). Knowledge Management. Encyclopedia of Corporate Social Responsibility. Springer, Berlin, Germany. doi:10.1007/978-3-642-28036-8_421.

Bhalerao, S., & Chavan, P. (2022). COVID 19 Prediction Model Using Prophet Forecasting with Solution for Controlling Cases and Economy. Studies in Systems, Decision and Control, 378, 139–151. doi:10.1007/978-3-030-77302-1_8.

Ayinde, K., Bello, H. A., Rauf, R. I., Attah, O. M., Nwosu, U. I., Bodunwa, O. K., Ojo, O. O., Ogundokun, R. O., Fayose, T. S., Akinbo, R. Y., Adejumo, A. O., Akinsola, O., Akomolafe, A. A., Olatayo, T. O., Aladeniyi, O. B., Olamide, E. I., & Olanrewaju, S. O. (2022). Modeling Covid-19 Cases in West African Countries: A Comparative Analysis of Quartic Curve Estimation Models and Estimators. Studies in Systems, Decision and Control, 366, 359–454. doi:10.1007/978-3-030-72834-2_12.

Debnath, S., Modak, S., & Sarkar, D. (2022). Forecasting on Global Dynamics for Coronavirus (COVID-19) Outbreak Using Time Series Modelling. Studies in Systems, Decision and Control, 366, 929–955. doi:10.1007/978-3-030-72834-2_27.

Zhang, Y., He, Z., & Tao, Y. (2022). Design and Implementation of Epidemic Prevention and Control Measures Recommendation System Based on K-means Clustering Algorithm. Proceedings of the 8th International Conference on Computing and Artificial Intelligence, 301–306. doi:10.1145/3532213.3532258.

Belle, A., Thiagarajan, R., Soroushmehr, S. M., Navidi, F., Beard, D. A., & Najarian, K. (2015). Big data analytics in healthcare. BioMed research international, 370194, 1-16. doi:10.1155/2015/370194.

Rehman, A., Naz, S., & Razzak, I. (2022). Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems, 28(4), 1339-1371.doi:10.1007/s00530-020-00736-8.

Boudreaux, N. K., Penovich, L. M., O'Malley, A. J. (2016). Using social media for public health crises: A systematic review. Journal of Medical Internet Research, 18(5), e107. doi:10.2196/38015.

Jayasri, N. P., & Aruna, R. (2022). Big data analytics in health care by data mining and classification techniques. ICT Express, 8(2), 250-257. doi:10.1016/j.icte.2021.07.001.

Hassan, M., Awan, F. M., Naz, A., deAndrés-Galiana, E. J., Alvarez, O., Cernea, A., ... & Kloczkowski, A. (2022). Innovations in genomics and big data analytics for personalized medicine and health care: A review. International journal of molecular Sciences, 23(9), 4645. doi:10.3390/ijms23094645.

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A Proposed Framework of Knowledge Management for COVID-19 Mitigation based on Big Data Analytic

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