Spatial pattern of dengue cases: An analysis in Bangi District, Selangor, Malaysia
In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.
Abd Majid N, Muhamad Nazi N, Mohamed AF, 2019. Distribution and spatial pattern analysis on dengue cases in Seremban District, Negeri Sembilan, Malaysia. Sustainability 11:3572. DOI: https://doi.org/10.3390/su11133572
Abd Majid N, Rainis R, Muhiyuddin WM, 2016. [Analisis Taburan dan Corak Ruangan Pelbagai Jenis Kegagalan Cerun di Pulau Pinang, Malaysia]. Int J Environ Soc Space 4:1-15. [in Malaysian].
Abdul Samad H, Shaharudin I, 2017. The emerging Kuala Lumpur extended mega urban region (KLEMUR): implications on urban prosperity in Malaysia. Int J Malay World Civilis 5:67-74.
Ahmad R, Suzilah I, Wan Najdah WMA, Topek O, Mustafakamal I, Lee HL, 2018. Factors determining dengue outbreak in Malaysia. PLoS One 13:e0193326. DOI: https://doi.org/10.1371/journal.pone.0193326
Aziz S, Aidil, RM, Nisfariza MN, Ngui R, Lim YAL, Wan Yusoff WS, Ruslan R, 2014. Spatial density of Aedes distribution in urban areas: a case study of breteau index in Kuala Lumpur, Malaysia. J Vector Borne 51:91-6.
Das M, Gopalakrishnan R, Kumar D, Gayan J, Baruah I, Veer V, Dutta P, 2014. Spatiotemporal distribution of dengue vectors & identification of high risk zones in district Sonitpur, Assam, India. Indian J Med Res 140:278-84.
Eryando T, Susanna D, Pratiwi D, Nugraha, F, 2012. Standard deviational ellipse (SDE) models for malaria surveillance, case study: Sukabumi district-Indonesia, in 2012. Malar J 11:P130. DOI: https://doi.org/10.1186/1475-2875-11-S1-P130
ESRI, 2018. An overview of the spatial statistics toolbox. Available from: https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statistics-toolbox/an-overview-of-the-spatial-statistics-toolbox.htm Accessed: 21 January 2021.
Hii YL, Zaki RA, Aghamohammadi N, Rocklöv J, 2016. Research on climate and Dengue in Malaysia: a systematic review. Curr Environ Health Rep 3:81-90. DOI: https://doi.org/10.1007/s40572-016-0078-z
Khormi HM, Kumar L, Elzahrany RA, 2011. Modeling spatio-temporal risk changes in the incidence of dengue fever in Saudi Arabia: a geographical information system case study. Geospat Health 6:77-84. DOI: https://doi.org/10.4081/gh.2011.159
Kweka EJ, Kimaro EE, Munga S, 2016. Effect of deforestation and land use changes on mosquito productivity and development in Western Kenya Highlands: implication for malaria risk. Front Public Health 26:238. DOI: https://doi.org/10.3389/fpubh.2016.00238
Malaysia Ministry of Health, 2019. History and epidemiology of dengue. Available from: http://denggi.myhealth.gov.my/history-and-epidemiology-of-dengue/?lang=en Accessed: 19 January 2021.
Masnita MY, Nazri CD, Rodziah I, Ariza Z, 2018. Assessing the temporal distribution of dengue vectors mosquitoes and its relationship with weather variables. Serangga 23:112-25.
Mohiddin A, Jaal Z, Md-Lasim A, Dieng H, Zuharah WF, 2015. Assessing dengue outbreak areas using vector surveillance in north east district, Penang Island, Malaysia. Asian Pac J Trop Dis 5:869-76. DOI: https://doi.org/10.1016/S2222-1808(15)60947-1
Mutheneni SR, Mopuri R, Naish S, Gunti D, Upadhyayula SM, 2016. Spatial distribution and cluster analysis of dengue using self-organising maps in Andhra Pradesh, India, 2011-2013. Parasite Epidemiol Control 4:52-61. DOI: https://doi.org/10.1016/j.parepi.2016.11.001
Nik Syaza Lina NR, Haliza AR, 2017. The association between climatic factors and dengue fever: a study in Subang Jaya and Sepang, Selangor. Malaysian J Public Health Med 1:140-50.
Rohani A, Aidil Azahary AR, Malinda M, Zurainee MN, Rozilawati H, Wan Najdah WMA, Lee HL, 2014. Eco-virological survey of Aedes mosquito larvae in selected dengue outbreak areas in Malaysian J Vector Borne Dis 51:327-32.
Rozilawati H, Tanaselvi K, Nazni WA, Mohd Masri S, Zairi J, Adanan CR, Lee HL, 2015. Surveillance of Aedes albopictus Skuse breeding preference in selected dengue outbreak localities, peninsular malaysia. Trop Biomed 32:49-64.
Shaharudin I, Shamsul AS, Tahir A, Mariam M, Azah D, Nik Shamsidah NI, 2002. [Sistem maklumat geografi (GIS) dan sektor kesihatan awam: kajian demam denggi di Bandar Baru Bangi dan Kajang]. Jurnal Kesihatan Masyarakat 8:34-42. [in Malaysian].
Thiruchelvam L, Dass SC, Zaki R, Yahya A, Asirvadam VS, 2018. Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia. Geospat Health 13:613. DOI: https://doi.org/10.4081/gh.2018.613
WHO (World Health Organisation), 2020. Dengue and severe dengue. World Health Organisation, Geneva, Switzerland. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue Accessed: 19 January 2021.
Williams CR, Gill BS, Mincham G, Mohd Zaki AH, Abdullah N, Mahiyuddin WRW, Ahmad R, Shahar MK, Harley D, Viennet E, Azil A, Kamaluddin A, 2015. Testing the impact of virus importation rates and future climate change on dengue activity in Malaysia using a mechanistic entomology and disease model. Epidemiol Infect 143:2856-64. DOI: https://doi.org/10.1017/S095026881400380X
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