Developing an Early Warning System Based on Correlation Analysis For Dengue Haemorrhagic Fever


Several cities in Indonesia have been declared as Dengue Haemorrhagic Fever (DHF)-endemic areas, including East Jakarta, an administrative city of DKI Jakarta Province. Surveillance is the most important activity in controlling and monitoring the development of DHF. This ecological study was conducted to assess the correlation between DHF cases, vector density (Larva Free Index [LFI]) and climate (rainfall and humidity). While the DHF cases were collected from hospitals report, vector density data were obtained from public health centres and climate data were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG). The correlations between DHF cases, vector density, and the climate were analysed based on weekly data, from week 1 to week 33 of 2019. This study showed a consistent trend of increasing and decreasing DHF cases with rainfall and LFI. DHF cases and LFI had a strong negative correlation (r = –0.72) at a time lag of six weeks. LFI and rainfall also showed a strong negative correlation (r = –0.86) at a time lag of five weeks. The strongest correlation between DHF cases and rainfall was found at week 8 (r = 0.87). Humidity, also an indicator of climate, had a strong positive correlation (r = 0.80) with DHF cases at the 11th-week time lag. However, contrarily, humidity had a strong negative correlation with LFI at the 5th-week time lag. These findings can be used for developing an early warning system that is reinforced by utilizing the application of HDF–climate information.

Keywords: dengue fever, vector, climate, early warning system

[1] Kementerian Kesehatan Republik Indonesia. (2017). Pedoman pencegahan dan penanggulangan Demam Berdarah Dengue di Indonesia. Jakarta: Kementerian Kesehatan Republik Indonesia.

[2] World Health Organization. (2020). Dengue Situation Updates 2020. Retrieved from https://iris.wpro.

[3] Kementerian Kesehatan Republik Indonesia. (2016, May). Situasi DBD di Indonesia. Retrieved May 1, 2020 from dbd 2016.pdf.

[4] Dinas Kesehatan Provinsi DKI Jakarta. (2020). Laporan tahunan data DBD Provinsi DKI Jakarta 2009 sampai 2019. Jakarta: Dinas Kesehatan Provinsi DKI Jakarta.

[5] Naish, S., et al. (2014). Climate Change and Dengue: A Critical and Systematic Review of Quantitative Modelling Approaches. BMC Infectious Disease, vol. 14, issue 167, pp. 1-14.

[6] Lee, H., et al. (2018). Potential Effects of Climate Change on Dengue Transmission Dynamics in Korea. PLoS One, vol. 13, issue 6, p. e0199205.

[7] Ferede, G., et al. (2018). Distribution and Larval Breeding Habitats of Aedes Mosquito Species in Residential Areas of Northwest Ethiopia. Epidemiology and Health, vol. 40, issue e2018015, pp. 507- 15.

[8] Kurniawati, R. (2015). Analisis spasial sebaran kasus demam berdarah dengue di Kabupaten Jember Tahun 2014. Jember: Universitas Jember.

[9] Díaz-Quijano, F. A., et al. (2008). Rainfall and Acute Febrile Syndrome in a Dengue-Endemic Area. Revista De Salud Publica, vol. 10, issue 2, pp. 250–9.

[10] Zubaidah, T., Ratodi, M. and Marlinae, L. (2016). Pemanfaatan informasi iklim sebagai sinyal peringatan dini kasus DBD di Banjarbaru, Kalimantan Selatan. Vektora, vol. 8, issue 2, pp. 99–106.

[11] Kementerian Kesehatan Republik Indonesia. (2013). Pedoman survei entomologi DBD dan kunci identifikasi nyamuk Aedes. Jakarta: Kementerian Kesehatan Republik Indonesia.

[12] Iriani, Y. (2012). Hubungan Antara Curah Hujan Dan Peningkatan Kasus Demam Berdarah Dengue Anak Di Kota Palembang. Sari Pediatri, vol. 13, issue 6, pp. 378–83.

[13] Ehelepola, N. D. B., et al. (2015). A Study of the Correlation Between Dengue and Weather in Kandy City, Sri Lanka (2003 -2012) and Lessons Learned. Infectious Diseases of Poverty, vol. 4, issue 42, pp. 1-14.

[14] Sanchez, L., et al. (2006). Aedes Aegypti Larval Indices and Risk for Dengue Epidemics. Emerging Infectious Disease, vol. 12, issue 5, pp. 800-6.