Spatiotemporal mapping of dengue cases in Sleman district, Indonesia year 2014-2017

Sulistyawati Sulistyawati, Tri Wahyuni Sukesi, Surahma Asti Mulasari


Background: Dengue is a significant public health problem in the mostly tropical country such as Indonesia. Even though many efforts have been established in Indonesia, in fact, dengue remains drawing attention from the health sector. Geographic Information System (GIS) is a powerful tool to support dengue surveillance on understanding the dengue pattern with the goal to give input for the decision maker. Accordingly, there is a need to providing the presence and the dynamic of dengue case particularly in Sleman district to establish evidence on building dengue control strategy.

Methods: A descriptive study using GIS was performed to provide a spatial-temporal mapping of dengue case. Secondary data which sourced from Sleman district health office was collected for period 2014-2017. This data was grounded into subdistrict level. Quantum GIS and Microsoft Excel were used to analyse the data.

Results: During 2014-2017 dengue spreads over the Sleman district. In 2016, found the increased of subdistrict with high dengue case. The high dengue case found in sub-district with an urban characteristic.

Conclusions: Mapping of dengue using GIS is helpful to understanding the disease presence and dynamic disease over time.


Dengue, Mapping, GIS, Sleman, Indonesia

Full Text:



WHO. Dengue control, 2016. Available at: Accessed on 27 November 2018.

Khoiri A. Indonesia Peringkat Dua Negara Endemis Demam Berdarah. CNN Indones, 2016. Available at: 20160616170332-255-138672/indonesia-peringkat-dua-negara-endemis-demam-berdarah. Accessed on 17 June 2016.

Ministry of Health of Republic Indonesia. KEMENKES OPTIMALKAN PSN, 2017; Available at: 17061500001/kemenkes-optimalkan-psn-cegah-dbd.html. Accessed on 15 June 2016

Duncombe J, Clements A, Hu W, Weinstein P, Ritchie S, Espino FE. Review: Geographical information systems for dengue surveillance. Am J Trop Med Hyg. 2012;86(5):753–5.

Mutheneni SR, Mopuri R, Naish S, Gunti D, Upadhyayula SM. Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013. Parasite Epidemiol Control. 2018;3(1):52–61.

Latif ZA, Mohamad MH. Mapping of dengue outbreak distribution using spatial statistics and geographical information system. 2015 IEEE 2nd Int Conf InformationScience Secur ICISS 2015. 2016.

Sleman Goverment. Letak dan Luas Wilayah., 2018. Available at: Accessed on 2 December 2018.

Kyle JL, Harris E. Global Spread and Persistence of Dengue. Annu Rev Microbiol. 2008;62(1):71–92.

Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA. Climate variability and change in the United States: Potential impacts on vector- and Rodent-Borne diseases. Environ Health Perspect. 2001;109(2):223–33.

Sirisena P, Noordeen F, Kurukulasuriya H, Romesh TA, Fernando LK. 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).

Sukamto. Studi Karakteristik Wilayah dengan Kejadian dbd di Kecamatan Cilacap Selatan Kabupaten Cilacap. Universitas Diponegoro Semarang, 2007.

Masrizal, Sari NP. Analisis Kasus Dbd Berdasarkan Unsur Iklim Dan Kepadatan Penduduk Melalui Pemdekatan Gis Di Tanah Datar. J Kesehat Masy Andalas. 2016;166–71.

Sleman Government. Sleman Topography, 2018. Available at Accessed 2 December 2018.

Medlock JM, Leach SA. Effect of climate change on vector-borne disease risk in the UK. Lancet Infect Dis. 2015;15(6):721-30.

Ogden NH. Climate change and vector-borne diseases of public health significance. FEMS Microbiol Lett. 2017;364(19):1–8.

Haryanto B. Climate Change and Public Health in Indonesia Impacts and Adaptation Budi Haryanto Austral Policy Forum 09-05S. Naut Inst Aust. 2009;1–12.

WHO. Global strategy for Dengue prevention and control 2012-2020. Geneva, Switzerland: WHO; 2012.