(Department of Statistics, Islamic University of Indonesia, Indonesia)(2) * Tuti Purwaningsih
(Department of Statistics, Islamic University of Indonesia, Indonesia)(3) Siaka Konate
(Department of Electronic and Telecommunications, Normal School of Technical and Vocational Education, Mali)(4) Modawy Adam Ali Abdalla
(1) Department of Electrical and Electronic Engineering, Nyala University, Nyala, Sudan; and 2) College of Energy and Electrical Engineering, Hohai University, China)*corresponding author
AbstractIn Indonesia, where the interplay between climate variability and infectious diseases is pronounced, Dengue Fever poses a significant threat, particularly in Central Java, ranking as the province with the third-highest incidence of Dengue cases nationwide. This study adopts a proactive approach, employing cluster analysis techniques—single linkage, average linkage, and Ward’s method—to categorize cities and regencies in Central Java based on their susceptibility to Dengue outbreaks. The comparative analysis, facilitated by standard deviation values, reveals nuanced vulnerability patterns, with the single linkage method presenting the most refined categorization, yielding four distinct vulnerability clusters: very low (0.097), low (0.150), medium (0.205), and high (0.303). Furthermore, spatial analysis utilizing Moran’s Index indicates a positive spatial autocorrelation among Dengue cases (Moran’s I = 0.62, p < 0.05), underscoring the spatial homogeneity in case distribution across regions. These findings emphasize the critical need for targeted interventions and evidence-based policymaking to effectively combat Dengue transmission in Central Java and mitigate its public health impact.
|
DOIhttps://doi.org/10.31763/sitech.v4i2.1203 |
Article metrics10.31763/sitech.v4i2.1203 Abstract views : 157 | PDF views : 27 |
Cite |
Full Text Download
|
References
[1] R. Naidoo et al., “Evaluating the impacts of protected areas on human well-being across the developing world,” Sci. Adv., vol. 5, no. 4, Apr. 2019, doi: 10.1126/sciadv.aav3006.
[2] E. P. Astuti, P. W. Dhewantara, H. Prasetyowati, M. Ipa, C. Herawati, and K. Hendrayana, “Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance,” Parasit. Vectors, vol. 12, no. 1, p. 186, Dec. 2019, doi: 10.1186/s13071-019-3446-3.
[3] S. Bhatia, D. Bansal, S. Patil, S. Pandya, Q. M. Ilyas, and S. Imran, “A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions,” Front. Public Heal., vol. 10, p. 884645, May 2022, doi: 10.3389/fpubh.2022.884645.
[4] W. M. de Souza and S. C. Weaver, “Effects of climate change and human activities on vector-borne diseases,” Nat. Rev. Microbiol., pp. 1–16, Mar. 2024, doi: 10.1038/s41579-024-01026-0.
[5] R. E. Baker et al., “Infectious disease in an era of global change,” Nat. Rev. Microbiol., vol. 20, no. 4, pp. 193–205, Apr. 2022, doi: 10.1038/s41579-021-00639-z.
[6] R. Gibb et al., “Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam,” Nat. Commun., vol. 14, no. 1, p. 8179, Dec. 2023, doi: 10.1038/s41467-023-43954-0.
[7] R. Floyd, Jessica, “Exploring spatiotemporal variation in host population mobility and vector-borne disease exposure,” University of Southampton, p. 189, 2020. [Online]. Available at: https://eprints.soton.ac.uk/450192/.
[8] R. Pakaya, D. Daniel, P. Widayani, and A. Utarini, “Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review,” BMC Public Health, vol. 23, no. 1, p. 2448, Dec. 2023, doi: 10.1186/s12889-023-17185-3.
[9] N. Siringi, S. Mala, and A. Rawat, “Study of K-Means Clustering Algorithm for Identification of Dengue Fever Hotspots,” in Lecture Notes in Electrical Engineering, vol. 601, Springer, 2020, pp. 51–61, doi: 10.1007/978-981-15-1420-3_6.
[10] H. I. Sasmita et al., “Ovitrap surveillance of dengue vector mosquitoes in Bandung City, West Java Province, Indonesia,” PLoS Negl. Trop. Dis., vol. 15, no. 10, p. e0009896, Oct. 2021, doi: 10.1371/journal.pntd.0009896.
[11] G. Zhu, J. Xiao, T. Liu, B. Zhang, Y. Hao, and W. Ma, “Spatiotemporal analysis of the dengue outbreak in Guangdong Province, China,” BMC Infect. Dis., vol. 19, no. 1, p. 493, Dec. 2019, doi: 10.1186/s12879-019-4015-2.
[12] T. W. Kesetyaningsih, S. Andarini, Sudarto, and H. Pramoedyo, “Determination Of Environmental Factors Affecting Dengue Incidence In Sleman District, Yogyakarta, Indonesia,” African J. Infect. Dis., vol. 12, no. 1 Suppl, pp. 13-25, 2018, doi: 10.2101/AJID.12V1S.3.
[13] M. Farahiyah, Nurjazuli, and O. Setiani, “Spatial Analysis of Demography Factor and the Incidence of Dhf in Demak,” Bul. Penelit. Kesehat, vol. 42, no. 1, pp. 25–36, 2014, [Online]. Available at: https://www.researchgate.net/profile/Nurjazuli-Nurjazuli/publication.
[14] D. Puspitawati, “Modeling Spatial Patterns of Dengue Hemorrhagic Fever in Semarang Regency Using Moran’s I Function,” Universitas Kristen Satya Wacana, p. 19, 2012. [Online]. Available at: https://repository.uksw.edu/handle/123456789/2384.
[15] S. Laeli, “Analisis Cluster dengan Average Linkage Method dan Ward ’ s Method untuk Data Responden Nasabah Asuransi Jiwa Unit Link,” Universitas Negeri Yogyakarta, 2014. [Online]. Available at: https://eprints.uny.ac.id/12541/.
[16] Fitriyani, “Determining Areas Prone to Dengue Fever in Indonesia and Analysis of the Effect of Rain Patterns on Attack Rates (Case Study: Indramayu Regency),” IPB (Bogor Agricultural University), 2007. [Online]. Available at: http://repository.ipb.ac.id/handle/123456789/44566.
[17] S. Wierzchoń and M. Kłopotek, Modern Algorithms of Cluster Analysis, vol. 34. Cham: Springer International Publishing, pp. 319-421 , 2018, doi: 10.1007/978-3-319-69308-8.
[18] C. C. Aggarwal, “An Introduction to Cluster Analysis,” in Data Clustering, Chapman and Hall/CRC, 2018, pp. 1–28, doi: 10.1201/9781315373515-1.
[19] A. de Souza et al., “Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature,” Brazilian Arch. Biol. Technol., vol. 65, p. 17, May 2022, doi: 10.1590/1678-4324-2022210331.
[20] H. Usman and N. Sobari, Application of multivariate techniques for marketing research at Sultan Syarif Kasim Riau, 1st ed. Jakarta: Rajawali Pers, p. 243, 2013. [Online]. Available at: https://inlislite.uin-suska.ac.id/opac/detail-opac?id=14749.
[21] M. Rusli and I. K. Suniantara, “Comparison Of The Methods Of The Hierarchy With Non-Hierarchy In Cluster Analysis,” Interational J. Adv. Stud. Comput. Sci. Eng., vol. 6, no. March, pp. 7–10, 2017, [Online]. Available at: https://www.researchgate.net/profile/I-Ketut-Suniantara/publication/343303905.
[22] F. J. Rohlf, “12 Single-link clustering algorithms,” in Handbook of Statistics, vol. 2, Elsevier, 1982, pp. 267–284, doi: 10.1016/S0169-7161(82)02015-X.
[23] P. Yildirim And D. Birant, “K-Linkage: A New Agglomerative Approach for Hierarchical Clustering,” Adv. Electr. Comput. Eng., vol. 17, no. 4, pp. 77–88, 2017, doi: 10.4316/AECE.2017.04010.
[24] P. Praveen, “An Efficient Linkage Criterion for Creating Clusters in Hierarchical Method,” Int. J. Futur. Gener. Commun. Netw., vol. 12, no. 5, pp. 294–300, Dec. 2019. [Online]. Available at: http://sersc.org/journals/index.php/IJFGCN/article/view/4416.
[25] Vijaya, S. Sharma, and N. Batra, “Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering,” in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Feb. 2019, pp. 568–573, doi: 10.1109/COMITCon.2019.8862232.
[26] A. Dogan and D. Birant, “K-centroid link: a novel hierarchical clustering linkage method,” Appl. Intell., vol. 52, no. 5, pp. 5537–5560, Mar. 2022, doi: 10.1007/s10489-021-02624-8.
[27] B. Moseley and J. R. Wang, “Approximation bounds for hierarchical clustering: Average linkage, bisecting K-means, and Local Search,” Adv. Neural Inf. Process. Syst., vol. 2017-Decem, pp. 3095–3104, 2017, [Online]. Available at: https://www.jmlr.org/papers/v24/18-080.html.
[28] N. Randriamihamison, N. Vialaneix, and P. Neuvial, “Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints,” J. Classif., vol. 38, no. 2, pp. 363–389, Jul. 2021, doi: 10.1007/s00357-020-09377-y.
[29] A. Jaeger and D. Banks, “Cluster analysis: A modern statistical review,” WIREs Comput. Stat., vol. 15, no. 3, p. e1597, May 2023, doi: 10.1002/wics.1597.
[30] A. Getis, “Spatial Autocorrelation,” in Handbook of Applied Spatial Analysis, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 255–278, doi: 10.1007/978-3-642-03647-7_14.
[31] A. Murtadho, A. E. Pravitasari, K. Munibah, and E. Rustiadi, “Spatial Distribution Pattern of Village Development Index in Karawang Regency Using Spatial Autocorrelation Approach,” J. Pembang. Wil. KOTA, vol. 16, no. 2, pp. 102–111, Jun. 2020, doi: 10.14710/pwk.v16i2.24883.
[32] P. Kowe, O. Mutanga, J. Odindi, and T. Dube, “Exploring the spatial patterns of vegetation fragmentation using local spatial autocorrelation indices,” J. Appl. Remote Sens., vol. 13, no. 02, p. 1, Jun. 2019, doi: 10.1117/1.JRS.13.024523.
[33] L. Anselin, “The Moran scatterplot as an ESDA tool to assess local instability in spatial association,” in Spatial Analytical Perspectives on GIS, Routledge, 2019, pp. 111–126, doi: 10.1201/9780203739051-8.
[34] T. T. Nguyen and T. D. Vu, “Identification of multivariate geochemical anomalies using spatial autocorrelation analysis and robust statistics,” Ore Geol. Rev., vol. 111, p. 102985, Aug. 2019, doi: 10.1016/j.oregeorev.2019.102985Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Anisahtul Fithriyyah ; Tuti Purwaningsih; Siaka Konate; Modawy Adam Ali Abdalla
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
Science in Information Technology Letters
ISSN 2722-4139
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
W : http://pubs2.ascee.org/index.php/sitech
E : sitech@ascee.org, andri@ascee.org, andri.pranolo.id@ieee.org
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0