Spatio-Temporal Analysis of Dengue Cases in Kuningan District Since 2008-2017
Abstract
Dengue has spread to over 400 of Indonesia’s 497 districts, including West Java Province in which 26 of its districts have been declared as hyper-endemic. A study was conducted to describe the spread of dengue incidences and its cluster during 2008-2017 in Kuningan District. The district is located in an important route, in migration and in the economic field, connecting the northern part of West Java to the southern part. A spatio-temporal analysis based on monthly dengue incidences from the local District Health Office was performed using SaTScan™. This study revealed there were Statistically significant high-risk dengue clusters with various RR in half of the subdistricts in Kuningan in the ten-year periods of 2008-2017 and a retrospective space-time analysis detected 17 significant clusters (P<0.001). Subdistrict Kuningan is detected as a high-risk area every year except for 2008, whereas Jalaksana emerged as a high-risk cluster in six of ten-year periods. We conclude that there was a dynamic spread of dengue cases initiated from the north part of Kuningan District to western areas. This study results do not properly predict RR due to a lack of information on some significant factors, such as vector density and related environmental and socioeconomic parameters. However, this study has provided a perspective on dengue incidence that can be used by local health managers and disease surveillance personnel to monitor prospective outbreaks and make decisions about how to implement an effective response.
References
2. Halstead SB, Thomas SJ. Dengue vaccines. In: Plotkin SA, Orenstein WA, Offit PA, Edwards KM, eds. Plotkin’s vaccines (Seventh Edition). Seventh Ed. Elsevier; 2018:241-251.e6.
3. Aryati A, Wrahatnala BJ, Yohan B, Fanny M, Hakim FKN, Sunari EP, et al. Dengue virus serotype 4 is responsible for the outbreak of dengue in East Java City of Jember, Indonesia. Viruses. 2020;12(9):1-20. doi:10.3390/v12090913.
4. Sekretariat Jenderal Kemenkes RI. Profil kesehatan Indonesia Tahun 2013. Jakarta: Kementerian Kesehatan RI; 2014.
5. Sekretariat Jenderal Kemenkes RI. Profil kesehatan Indonesia 2017. Jakarta: Kementerian Kesehatan RI; 2018.
6. Dhewantara PW, Ruliansyah A, Fuadiyah MEA, Astuti EP, Widawati M. Space-time scan statistics of 2007-2013 dengue incidence in Cimahi city, Indonesia. Geospat Health. 2015;10(2):255-60. doi:10.4081/gh.2015.373.
7. Kulldorff M, Nagarwalla N. Spatial disease clusters: detection and inference. Stat Med. 1995;14(8):799-810. doi:10.1002/sim.4780140809.
8. BPS Provinsi Jawa Barat. Jawa Barat Province in figures. Bandung: Badan Pusat Statistik Provinsi Jawa Barat; 2018.
9. BPS Kabupaten Kuningan. Kabupaten Kuningan dalam angka. Kuningan: Badan Pusat Statistik Kabupaten Kuningan; 2018.
10. Yuanita YN, Setiani O, Wahyuningsih NE. Spatial analysis of breeding place and larva density existence with DHF (dengue hemorrhagic fever) incidence rate in Pringsewu District, Indonesia. Int J English Lit Soc Sci. 2019;4(5):1357-64. doi:10.22161/ijels.45.17.
11. Arifin NF, Adi MS, Suhartono S, Martini M, Suwondo A. Spatial and temporal determinantsfor dengue haemorrhagic fever: a descriptive study in Tanjungpinang City, Indonesia. IOSR J Dent Med Sci. 2017;16(10):34-38. doi:10.9790/0853-1610133438.
12. Kusairi A, Yulia R. Mapping of dengue fever distribution based on Indonesian national standart cartography rules as a prevention indicator of outbreak. J Pendidik IPA Indones. 2020;9(1):91-6. doi:10.15294/jpii.v9i1.21811.
13. Salim MF, Syairaji M. Time-series analysis of climate change effect on increasing of dengue hemorrhagic fever (DHF) case with geographic information system approach in Yogyakarta, Indonesia. International Procceedings 2Ed International Scientific Meeting on Health Information Management; 19 Desember 2020; Surakarta. p248-56.
14. Dhewantara PW, Prasetyowati H, Ridwan W, Hakim L. The application of spatiotemporal scan statistics to detect high-risk clusters for dengue fever in Jakarta, Indonesia. In: International Conference on Science and Applied Science (ICSAS2020); 2020. doi:10.1063/5.0030342.
15. Sulistyawati S, Astuti FD, Ramadona AL. Exploring spatio-temporal cluster for dengue prevention in urban area of Indonesia. Int J Public Heal Clin Sci. 2019;6(1):176-85.
16. Irda Sari SY, Adelwin Y, Rinawan FR. Land use changes and cluster identification of dengue hemorrhagic fever cases in Bandung, Indonesia. Trop Med Infect Dis. 2020;5(2):1-9. doi:10.3390/tropicalmed5020070.
17. Murray NEA, Quam MB, Wilder-Smith A. Epidemiology of dengue: past, present and future prospects. Clin Epidemiol. 2013;5(1):299-309. doi:10.2147/CLEP.S34440.
18. BPS Kabupaten Kuningan. Kecamatan Kuningan dalam angka. Kuningan: BPS Kabupaten Kuningan; 2018.
19. Weiss RA, McMichael AJ. Social and environmental risk factors in the emergence of infectious diseases. Nat Med. 2004;10(12 Suppl):S70-6. doi:10.1038/nm1150.
20. Wearing HJ, Rohani P. Ecological and immunological determinants of dengue epidemics. Proc Natl Acad Sci USA. 2006;103(31):11802-7. doi:10.1073/pnas.0602960103.
21. Akter R, Naish S, Hu W, Tong S. Socio-demographic, ecological factors and dengue infection trends in Australia. PLoS One. 2017;12(10):e0185551.
22. Fuadiyah M. Pengaruh iklim terhadap kejadian dengue. In: Suwandono A, ed. Dengue update: menilik perjalanan dengue di Jawa Barat. Jakarta: LIPI Press; 2019:167-94.
23. Fuadiyah M, Widawati M. Faktor iklim berpengaruh terhadap kejadian demam berdarah dengue di Kota Cimahi Tahun 2004-2013. SPIRAKEL. 2018;10(2):86-96. doi:10.22435/spirakel.v10i2.356
24. Astuti EP, Dhewantara PW, Prasetyowati H, Ipa M, Herawati C, Hendrayana K. Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance. Parasites and Vectors. 2019;12(1):1-12. doi:10.1186/s13071-019-3446-3.
25. Jain R, Sontisirikit S, Iamsirithaworn S, Prendinger H. Prediction of dengue outbreaks based on disease surveillance, meteorological and socio-economic data. BMC Infect Dis. 2019;19(1):1-16.doi:10.1186/s12879-019-3874-x.
26. Jaya IGNM, Folmer H. Identifying spatiotemporal clusters by means of agglomerative hierarchical clustering and bayesian regression analysis with spatiotemporally varying coefficients: methodology and application to dengue disease in Bandung, Indonesia. Geogr Anal. 2020;53(4):767-817. doi:10.1111/gean.12264.
27. Supadmi W, Perwitasari DA, Abdulah R, Suwantika AA. Correlation of rainfall and socio-economic with incidence dengue in Jakarta, Indonesia. J Adv Pharm Educ Res. 2019;9(1):134-42.
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