Developing a hybrid clustering model for Covid-19 vaccine distribution in Bangkok metropolis, Thailand
Keywords:
Partitional Clustering, Vaccine distribution, Covid-19, K-Means, Center-of-gravityAbstract
The Covid-19 cases are rapidly and continuously increasing in many areas recently, particularly in the Asia and Pacific region. Bangkok, as one of the biggest capitals in Southeast Asia, has a high prevalence rate of Covid-19 patients. Furthermore, the number of deaths from this pandemic is constantly rising. Therefore, the central government put an effort to control the Covid-19 situation by increasing the vaccination rate in many endemic areas, including Bangkok. Despite that Covid-19 vaccine has been prepared since the 1st quarter of 2021, vaccine distribution in this city is still limited. One of the main problems is insufficient vaccination centers serving all populations in Bangkok and metropolitan areas. This paper proposes a novel perspective of vaccine distribution in the metropolis, using a hybrid clustering model. This model is the integration between the concepts of partitional clustering and center-of-gravity. Bangkok is chosen as a case study to verify the performance of this model.
The results showed that K-Means is outperformed other benchmark via silhouette width value. In addition, this model suggests increasing the number of vaccination centers in some areas of Bangkok. This model suggests extra six vaccination centers apart from 25 current centers established by the central government, for a total of 31 centers. This can be a good case study for all involved organizations to consider increasing vaccination centers in different areas for the future period.
References
Apipratchayasakul, K. (2010). Inventory Management (1st ed.). Focus Media And Publishing Company LTD. http://lib.neu.ac.th/ULIB/dublin.php?ID=16126
Banerjee, A., & Rajesh N., D. (2004). Validating clusters using the Hopkins statistic. 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 149–153. https://doi.org/10.1109/FUZZY.2004.1375706
Bangkok, S. (2020). Bangkok 2020 Statistics. http://www.bangkok.go.th/upload/user/00000130/BMA_STATISTICS 2563/ebook 63.pdf
Cadavid, J. P. U., Lamouri, S., Grabot, B., & Fortin, A. (2019). Machine Learning in Production Planning and Control : A Review of Empirical Literature. IFAC-PapersOnLine 52, 13, 385–390.
Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and Its Applications, 519, 127–139. https://doi.org/10.1016/j.physa.2018.11.061
Chaiyawong, K. (2017). A guideline for selecting the location of super store [Thammasat University]. http://ethesisarchive.library.tu.ac.th/thesis/2017/TU_2017_5716033187_3158_8131.pdf
Chang, H. wen, Tai, Y. chin, & Hsu, J. Y. jen. (2009). Context-aware taxi demand hotspots prediction. International Journal of Business Intelligence and Data Mining, 5(1), 3. https://doi.org/10.1504/ijbidm.2010.030296
Davahli, M. R., Karwowski, W., & Fiok, K. (2021). Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks. PLoS ONE, 16(7 July), 1–14. https://doi.org/10.1371/journal.pone.0253925
Department of Disease Control, M. (2021). Covid-19 statistic Thailand. https://ddc.moph.go.th/
Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. https://doi.org/10.1016/S1473-3099(20)30120-1
Dzimińska, P., Drzewiecki, S., Ruman, M., Kosek, K., Mikołajewski, K., & Licznar, P. (2021). The use of cluster analysis to evaluate the impact of covid-19 pandemic on daily water demand patterns. Sustainability (Switzerland), 13(11). https://doi.org/10.3390/su13115772
Flynt, A., & Dean, N. (2016). A Survey of Popular R Packages for Cluster Analysis. Journal of Educational and Behavioral Statistics, 41(2), 205–225. https://doi.org/10.3102/1076998616631743
Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2021). South Asian countries are less fatal concerning COVID-19: a hybrid approach using machine learning and M-AHP. In Computational Intelligence Techniques for combating COVID-19 (pp. 1–26). Springer, Cham.
Gunawardena, T. (2016). Algorithms : Clustering. https://www.slideshare.net/tilanigunawardena/hierachical-clustering?from_action=save
Hannah, R., Esteban, O.-O., Diana, B., Edouard, M., Joe, H., Bobbie, M., Charlie, G., Cameron, A., Lucas, R.-G., & Max, R. (2020). Coronavirus Pandemic (COVID-19). OurWorldInData.Org. https://ourworldindata.org/coronavirus
Kantasa-Ard, A., Nouiri, M., Bekrar, A., Ait El Cadi, A., & Sallez, Y. (2019). Dynamic clustering of PI-hubs based on forecasting demand in physical internet context. In Studies in Computational Intelligence (Vol. 853, pp. 27–39). Springer Verlag. https://doi.org/10.1007/978-3-030-27477-1_3
Kassambara, A. (2018). Type of clustering methods: overview and quick start R code. https://www.datanovia.com/en/blog/types-of-clustering-methods-overview-and-quick-start-r-code/
MacQueen, J. (1967). Some Methods for classification and Analysis of Multivariate Observations. 5th Berkeley Symposium on Mathematical Statistics and Probability 1967. https://doi.org/citeulike-article-id:6083430
Malika, C., Nadia, G., Boiteau, V., & Niknafs, A. (2014). NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistical Software, 61(6), 1–36. http://www.jstatsoft.org/v61/i06/paper
Mitra, R., Moore, S. A., Gillespie, M., Faulkner, G., Vanderloo, L. M., Chulak-Bozzer, T., Rhodes, R. E., Brussoni, M., & Tremblay, M. S. (2020). Healthy movement behaviours in children and youth during the COVID-19 pandemic: Exploring the role of the neighbourhood environment. Health and Place, 65(August), 102418. https://doi.org/10.1016/j.healthplace.2020.102418
Murray, P. W., Agard, B., & Barajas, M. A. (2015). Forecasting supply chain demand by clustering customers. IFAC-PapersOnLine, 28(3), 1834–1839. https://doi.org/10.1016/j.ifacol.2015.06.353
Nananukul, N. (2013). Clustering model and algorithm for production inventory and distribution problem. Applied Mathematical Modelling, 37(24), 9846–9857. https://doi.org/10.1016/j.apm.2013.05.029
Panyasan, C., & Klankamsorn, S. (2012). Site Selection of Automotive Changing Tire Center in Bangkok and Metropolitan. IE Network Conference, 489–491. http://www.dms.eng.su.ac.th/filebox/FileData/WPS012.pdf
Pluempirom, J., & Ngamsaard, W. (2020). Feasibility Study and Finding a Suitable Location for Frozen Food Distribution Center Investment Project: A Case Study of XYZ Company Limited. The 15th RSU National Graduate Research Conference, 1145–1156.
Simić, S., Banković, Z., Simić, D., & Simić, S. D. (2018). A Hybrid Clustering Approach for Diagnosing Medical Diseases. Hybrid Artificial Intelligent Systems, 10870, 741–752. https://doi.org/https://doi.org/10.1007/978-3-319-92639-1_62
Simić, S., Banković, Z., Villar, J. R., Simić, D., & Simić, S. D. (2021). A hybrid fuzzy clustering approach for diagnosing primary headache disorder. Logic Journal of the IGPL, 29(2), 220–235. https://doi.org/10.1093/jigpal/jzaa048
Siriliang, P., & Jarupan, P. (2019). Selection of Location for Good Transportation Case Study of Delivery Service Company for Consumer Goods [King Mongkut’s University of Technology North Bangkok]. http://mhle.eng.kmutnb.ac.th/upload/student-abstract/d0147d95ff96ec10f1f3b59c77b9e43b.pdf
Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501–514. https://doi.org/10.1016/j.ejor.2003.08.037
Downloads
Published
How to Cite
Issue
Section
License
- เนื้อหาและข้อมูลในบทความที่ลงตีพิมพ์ในวารสารกฎหมายและนโยบายสาธารณสุข ถือเป็นข้อคิดเห็นและความรับผิดชอบของผู้เขียนบทความโดยตรงซึ่งกองบรรณาธิการวารสาร ไม่จำเป็นต้องเห็นด้วย หรือร่วมรับผิดชอบใด ๆ
- บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารกฎหมายและนโยบายสาธารณสุข ถือเป็นลิขสิทธิ์ของวารสารฯ หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใด ๆ จะต้องอ้างอิงเสมอ
