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http://dx.doi.org/10.3961/jpmph.21.461

Trends and Spatial Pattern Analysis of Dengue Cases in Northeast Malaysia  

Masrani, Afiqah Syamimi (Department of Community Medicine, School of Medical Sciences, Health Campus, Universiti Sains Malaysia)
Husain, Nik Rosmawati Nik (Department of Community Medicine, School of Medical Sciences, Health Campus, Universiti Sains Malaysia)
Musa, Kamarul Imran (Department of Community Medicine, School of Medical Sciences, Health Campus, Universiti Sains Malaysia)
Yasin, Ahmad Syaarani (Vector Unit, Kelantan State Health Department)
Publication Information
Journal of Preventive Medicine and Public Health / v.55, no.1, 2022 , pp. 80-87 More about this Journal
Abstract
Objectives: Dengue remains hyperendemic in Malaysia despite extensive vector control activities. With dynamic changes in land use, urbanisation and population movement, periodic updates on dengue transmission patterns are crucial to ensure the implementation of effective control strategies. We sought to assess shifts in the trends and spatial patterns of dengue in Kelantan, a north-eastern state of Malaysia (5°15'N 102°0'E). Methods: This study incorporated data from the national dengue monitoring system (eDengue system). Confirmed dengue cases registered in Kelantan with disease onset between January 1, 2016 and December 31, 2018 were included in the study. Yearly changes in dengue incidence were mapped by using ArcGIS. Hotspot analysis was performed using Getis-Ord Gi to track changes in the trends of dengue spatial clustering. Results: A total of 10 645 dengue cases were recorded in Kelantan between 2016 and 2018, with an average of 10 dengue cases reported daily (standard deviation, 11.02). Areas with persistently high dengue incidence were seen mainly in the coastal region for the 3-year period. However, the hotspots shifted over time with a gradual dispersion of hotspots to their adjacent districts. Conclusions: A notable shift in the spatial patterns of dengue was observed. We were able to glimpse the shift of dengue from an urban to peri-urban disease with the possible effect of a state-wide population movement that affects dengue transmission.
Keywords
Dengue; Incidence studies; Spatial analysis; Disease hot spot; Malaysia;
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1 Suppiah J, Ching SM, Amin-Nordin S, Mat-Nor LA, Ahmad-Najimudin NA, Low GK, et al. Clinical manifestations of dengue in relation to dengue serotype and genotype in Malaysia: a retrospective observational study. PLoS Negl Trop Dis 2018;12(9):e0006817.   DOI
2 Guo C, Zhou Z, Wen Z, Liu Y, Zeng C, Xiao D, et al. Global epidemiology of dengue outbreaks in 1990-2015: a systematic review and meta-analysis. Front Cell Infect Microbiol 2017;7:317.   DOI
3 Gubler DJ. Dengue, urbanization and globalization: the unholy trinity of the 21(st) century. Trop Med Health 2011;39(4 Suppl):3-11.   DOI
4 Ebi KL, Nealon J. Dengue in a changing climate. Environ Res 2016;151:115-123.   DOI
5 Katzelnick LC, Coloma J, Harris E. Dengue: knowledge gaps, unmet needs, and research priorities. Lancet Infect Dis 2017;17(3):e88-e100.   DOI
6 Aziz S, Ngui R, Lim YA, Sholehah I, Nur Farhana J, Azizan AS, et al. Spatial pattern of 2009 dengue distribution in Kuala Lumpur using GIS application. Trop Biomed 2012;29(1):113-120.
7 Hii YL, Zaki RA, Aghamohammadi N, Rocklov J. Research on climate and dengue in Malaysia: a systematic review. Curr Environ Health Rep 2016;3(1):81-90.   DOI
8 Denggi MyHealth. History and epidemiology of dengue; 2017 [cited 2021 Jul 23]. Available from: http://denggi.myhealth.gov.my/history-and-epidemiology-of-dengue/?lang=en.
9 Sull Z. Dengue prevention and control in Malaysia. In: International Conference on Dengue Prevention and Control & International Dengue Expert Consultation Meeting; 2015 Dec 7-8; Tainan, Taiwan.
10 Mohd-Zaki AH, Brett J, Ismail E, L'Azou M. Epidemiology of dengue disease in Malaysia (2000-2012): a systematic literature review. PLoS Negl Trop Dis 2014;8(11):e3159.   DOI
11 Guo Y, Barnett AG, Tong S. Spatiotemporal model or time series model for assessing city-wide temperature effects on mortality? Environ Res 2013;120:55-62.   DOI
12 Ahmad DM, Azman A, Hafizan J, Kamaruzzaman Y, Ismail ZA, Nur HS, et al. Geographical information system (GIS) for relationship between dengue disease and climatic factors at Cheras, Malaysia. Malays J Anal Sci 2015;19(6):1318-1326.
13 Anker M, Arima Y. Male-female differences in the number of reported incident dengue fever cases in six Asian countries. Western Pac Surveill Response J 2011;2(2):17-23.   DOI
14 Yue Y, Sun J, Liu X, Ren D, Liu Q, Xiao X, et al. Spatial analysis of dengue fever and exploration of its environmental and socioeconomic risk factors using ordinary least squares: a case study in five districts of Guangzhou City, China, 2014. Int J Infect Dis 2018;75:39-48.   DOI
15 Jemal Y, Al-Thukair AA. Combining GIS application and climatic factors for mosquito control in Eastern Province, Saudi Arabia. Saudi J Biol Sci 2018;25(8):1593-1602.   DOI
16 Mala S, Jat MK. Geographic information system based spatiotemporal dengue fever cluster analysis and mapping. Egypt J Remote Sens Space Sci 2019;22(3):297-304.   DOI
17 Sumdani H, Frickle S, Le M, Tran M, Zaleta CK. Effects of population density on the spread of disease. Technical report 2014- 05 [cited 2021 Jul 23]. Available from: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=F75696485ED0FCCCCBE820D15A36E221?doi=10.1.1.432.2182&rep=rep1&type=pdf.
18 Cheng Q, Jing Q, Spear RC, Marshall JM, Yang Z, Gong P. The interplay of climate, intervention and imported cases as determinants of the 2014 dengue outbreak in Guangzhou. PLoS Negl Trop Dis 2017;11(6):e0005701.   DOI
19 Ministry of Housing and Local Government Malaysia. National strategic plan for solid waste management; executive summary; 2005 [cited 2020 Oct 20]. Available from: https://jpspn.kpkt.gov.my/resources/index/user_1/PSP/Ringkasan_Eksekutif/ExecSum-Final%20Report.pdf.
20 Sirisena P, Noordeen F, Kurukulasuriya H, Romesh TA, Fernando L. Effect of climatic factors and population density on the distribution of dengue in Sri Lanka: a GIS based evaluation for prediction of outbreaks. PLoS One 2017;12(1):e0166806.   DOI
21 Foley DA, Yeoh DK, Karapanagiotidis T, Nhindri T, Catton M. Fever in the returned traveller: the utility of the Platelia Dengue NS1 antigen enzyme immunoassay for the diagnosis of dengue in a non-endemic setting. Pathology 2020;52(3):370-372.   DOI
22 Mudin RN. Dengue incidence and the prevention and control program in Malaysia. Int Med J Malays 2015. doi: https://doi.org/10.31436/imjm.v14i1.447.   DOI
23 Saat NZ, Hanawi SA, Subhi N, Zulfakar SS, Wahab MI. Practice and attitude on household waste management in Tumpat and Kuala Krai, Kelantan. Res J Soc Sci 2018;11(1):14-17.
24 Chen Y, Zhao Z, Li Z, Li W, Li Z, Guo R, et al. Spatiotemporal transmission patterns and determinants of dengue fever: a case study of Guangzhou, China. Int J Environ Res Public Health 2019;16(14):2486.   DOI
25 Campos NB, Morais MH, Ceolin AP, Cunha MD, Nicolino RR, Schultes OL, et al. Twenty-two years of dengue fever (1996-2017): an epidemiological study in a Brazilian city. Int J Environ Health Res 2021;31(3):315-324.   DOI
26 Aziz S, Aidil RM, Nisfariza MN, Ngui R, Lim YA, Yusoff WS, et al. Spatial density of Aedes distribution in urban areas: a case study of breteau index in Kuala Lumpur, Malaysia. J Vector Borne Dis 2014;51(2):91-96.
27 Swain S, Bhatt M, Pati S, Soares Magalhaes RJ. Distribution of and associated factors for dengue burden in the state of Odisha, India during 2010-2016. Infect Dis Poverty 2019;8(1):31.   DOI
28 Dempsey C. What is the difference between a heat map and a hot spot map?; 2014 [cited 2021 Jul 23]. Available from: https://www.gislounge.com/difference-heat-map-hot-spot-map/.
29 Wilder-Smith A, Tissera H, AbuBakar S, Kittayapong P, Logan J, Neumayr A, et al. Novel tools for the surveillance and control of dengue: findings by the DengueTools research consortium. Glob Health Action 2018;11(1):1549930.   DOI