Hazard Level of Dengue Haemorrhagic Fever in Gorontalo Regency: Prediction of Spatial Distribution with AHP-GIS Integration

  • Ririn Pakaya Bagian Ilmu Kesehatan Masyarakat, Universitas Gorontalo, Gorontalo
    (ID) http://orcid.org/0000-0003-1358-4562
  • Yanti Hz Hano Bagian Ilmu Kesehatan Masyarakat, Universitas Gorontalo, Gorontalo
    (ID) http://orcid.org/0000-0002-5819-101X
  • Muhammad Ramdhan Olii Bagian Teknik Sipil, Universitas Gorontalo, Gorontalo


Under certain climatic conditions, Aedes aegypti and Aedes albopictus mosquitoes can survive and reproduce optimally so that climate change can significantly change the pattern of disease distribution. This study aimed to model the level of Dengue Haemorrhagic Fever (DHF) hazard in Gorontalo Regency by integrating the Analytical Hierarchy Process (AHP), Geographic Information System (GIS) with climatological and topographic factors. The factors that most influence the level of hazard of DHF are annual rainfall, altitude, and humidity. The results obtained show that Gorontalo Regency is dominated by the hazard level class of 94852.31 ha or 44.25% and the moderate class area of 82553.37 ha or 38.5% of the total area of Gorontalo Regency. These results prove that Gorontalo Regency is very at risk of DHF disease. If this is not handled by the government properly, the moderate class will potentially rise to the high class. The prediction model for the DHF hazard level in this study can be made according to local conditions in the research area which have limited data. Changes in climate variables and periodicity that affect the incidence of dengue can be flexibly adapted to this model. The findings from this study provide valuable insights that have the potential to improve mitigation in public health-related interventions.


Ajim Ali, S., & Ahmad, A. (2018). Using analytic hierarchy process with GIS for Dengue risk mapping in Kolkata Municipal Corporation, West Bengal, India. Spatial Information Research, 26(4), 449–469. https://doi.org/10.1007/s41324-018-0187-x

Amiruddin, M. S. (2016). Pemetaan Tingkat Resiko Wabah Demam Berdarah Dengue (DBD) di Kecamatan Sananwetan, Kota Biltar. Institut Teknologi Sepuluh Nopember Surabaya.

Bhatt, B., & Joshi, J. P. (2014). Analytical hierarchy process modeling for malaria risk zones in vadodara district, Gujarat. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, XL–8(1), 171–176. https://doi.org/10.5194/isprsarchives-XL-8-171-2014

Caldwell, J. M., LaBeaud, A. D., Lambin, E. F., Stewart-Ibarra, A. M., Ndenga, B. A., Mutuku, F. M., Krystosik, A. R., Ayala, E. B., Anyamba, A., Borbor-Cordova, M. J., Damoah, R., Grossi-Soyster, E. N., Heras, F. H., Ngugi, H. N., Ryan, S. J., Shah, M. M., Sippy, R., & Mordecai, E. A. (2021). Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents. Nature Communications, 12(1), 1–13. https://doi.org/10.1038/s41467-021-21496-7

Campbell, L. P., Luther, C., Moo-Llanes, D., Ramsey, J. M., Danis-Lozano, R., & Peterson, A. T. (2015). Climate change influences on global distributions of dengue and chikungunya virus vectors. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1665), 1–9. https://doi.org/10.1098/rstb.2014.0135

Dom, N. C., Ahmad, A. H., Latif, Z. A., & Ismail, R. (2016). Application of geographical information system-based analytical hierarchy process as a tool for dengue risk assessment. Asian Pacific Journal of Tropical Disease, 6(12), 928–935. https://doi.org/10.1016/S2222-1808(16)61158-1

Ewing, D. A., Cobbold, C. A., Purse, B. V., Nunn, M. A., & White, S. M. (2016). Modelling the effect of temperature on the seasonal population dynamics of temperate mosquitoes. Journal of Theoretical Biology, 400, 65–79. https://doi.org/10.1016/j.jtbi.2016.04.008

Franch-Pardo, I., Napoletano, B. M., Rosete-Verges, F., & Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the Total Environment, 739, 140033. https://doi.org/10.1016/j.scitotenv.2020.140033

Harapan, H., Michie, A., Mudatsir, M., Sasmono, R. T., & Imrie, A. (2019). Epidemiology of dengue hemorrhagic fever in Indonesia: Analysis of five decades data from the National Disease Surveillance. BMC Research Notes, 12(1), 4–9. https://doi.org/10.1186/s13104-019-4379-9

Hendri, J., Santya, R. N. R. ., & Prasetyowati, H. (2015). Distribusi dan Kepadatan Vektor Demam Berdarah Dengue (DBD) Berdasarkan Ketinggian Tempat di Kabupaten Ciamis Jawa Barat. Jurnal Ekologi Kesehatan, 14(1), 17–28. http://dx.doi.org/10.22435/jek.v14i1.4654.17-28

Idriani, E., Rahmaniati, M. M., & Kurniawan, R. (2019). Dengue surveillance information system: An android-based early warning system for the outbreak of Dengue in Padang, Indonesia. Indian Journal of Public Health Research and Development. Indian Journal of Public Health Research and Development, 10(5), 1386–1390. https://doi.org/10.5958/0976-5506.2019.01124.0

Kementerian Kesehatan RI. (2020). Profil Kesehatan Indonesia Tahun 2019. In Profil Kesehatan Indonesia Tahun 2019. https://doi.org/10.5005/jp/books/11257_5

Kim, J. Y., Eun, S. J., & Park, D. K. (2018). Malaria Vulnerability Map Mobile System Development Using GIS-Based Decision-Making Technique. Mobile Information Systems, 2018. https://doi.org/10.1155/2018/8436210

Li, X., Liu, T., Lin, L., Song, T., Du, X., Lin, H., Xiao, J., He, J., Liu, L., Zhu, G., Zeng, W., Guo, L., Cao, Z., Ma, W., & Zhang, Y. (2017). Application of the analytic hierarchy approach to the risk assessment of Zika virus disease transmission in Guangdong Province, China. BMC Infectious Diseases, 17(1), 1–9. https://doi.org/10.1186/s12879-016-2170-2

Murad, A. (2018). Using GIS for determining variations in health access in jeddah city, Saudi Arabia. ISPRS International Journal of Geo-Information, 7(7). https://doi.org/10.3390/ijgi7070254

Murray, N. E. A., Quam, M. B., & Wilder-Smith, A. (2013). Epidemiology of dengue: Past, present and future prospects. Clinical Epidemiology, 5(1), 299–309. https://doi.org/10.2147/CLEP.S34440

Musa, G. J., Chiang, P. H., Sylk, T., Bavley, R., Keating, W., Lakew, B., Tsou, H. C., & Hoven, C. W. (2013). Use of GIS Mapping as a Public Health Tool–-From Cholera to Cancer. Health Services Insights, 6, 111–116. https://doi.org/10.4137/HSI.S10471

Nakhapakorn, K., & Tripathi, N. K. (2005). An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. International Journal of Health Geographics, 4(13), 1–13. https://doi.org/10.1186/1476-072X-4-13

Nazri Che, D., Abu Hassan, A., Zulkiflee Abd, L., & Rodziah, I. (2016). Application of geographical information system-based analytical hierarchy process as a tool for dengue risk assessment. Asian Pacific Journal of Tropical Disease, 6(12), 928–935. https://doi.org/10.1016/S2222-1808(16)61158-1

Nuraini, N., Fauzi, I. S., Fakhruddin, M., Sopaheluwakan, A., & Soewono, E. (2021). Climate-based dengue model in Semarang, Indonesia: Predictions and descriptive analysis. Infectious Disease Modelling, 6, 598–611. https://doi.org/10.1016/j.idm.2021.03.005

Pinontoan, O. R. (2018). Pengendalian Vektor. Unsrat Press.

Rakotoarison, H. A., Rasamimalala, M., Rakotondramanga, J. M., Ramiranirina, B., Franchard, T., Kapesa, L., Razafindrakoto, J., Guis, H., Tantely, L. M., Girod, R., Rakotoniaina, S., Baril, L., Piola, P., & Rakotomanana, F. (2020). Remote sensing and multi-criteria evaluation for malaria risk mapping to support indoor residual spraying prioritization in the central highlands of Madagascar. Remote Sensing, 12(10), 1–22. https://doi.org/10.3390/rs12101585

Ramos, L. M. L., Obando, O. A. A., Duque, J. E., & García-Merchán, V. H. (2020). Effect of altitude on wing metric variation of Aedes aegypti (Diptera: Culicidae) in a region of the Colombian Central Andes. PLoS ONE, 15(8 August), 1–15. https://doi.org/10.1371/journal.pone.0228975

Reinhold, J. M., Lazzari, C. R., & Lahondère, C. (2018). Effects of the environmental temperature on Aedes aegypti and Aedes albopictus mosquitoes: A review. Insects, 9(4). https://doi.org/10.3390/insects9040158

Salim, M. F., & Syairaji, M. (2020). Time-Series Analysis of Climate Change Effect on Increasing of Dengue Hemorrhagic Fever (DHF) Case with Geographic Information System Approach in Yogyakarta, Indonesia. International Proceedings the 2Ed International Scientific Meeting on Health Information Management, 5, 248–256. https://www.publikasi.aptirmik.or.id/index.php/ismohim2020/article/view/142/

Saran, S., Singh, P., Kumar, V., & Chauhan, P. (2020). Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19. Journal of the Indian Society of Remote Sensing, 48(8), 1121–1138. https://doi.org/10.1007/s12524-020-01140-5

Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M. B., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., & Midgley, P. M. (2013). Climate change 2013 the physical science basis: Working Group I contribution to the fifth assessment report of the intergovernmental panel on climate change. In Climate Change 2013 the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Vol. 5). https://doi.org/10.1017/CBO9781107415324

Sulekan, A., Suhaila, J., & Wahid, N. A. A. (2021). Assessing the Effect of Climate Factors on Dengue Incidence via a Generalized Linear Model. Open Journal of Applied Sciences, 10(04), 549–563. https://doi.org/10.4236/ojapps.2021.104039

Sulistyawati, S. (2020). Measuring the dengue risk area using Geographic Information System: a review. Insights in Public Health Journal, 1(1), 36. https://doi.org/10.20884/1.iphj.2020.1.1.3012

Tamengkel, H. V., Sumampouw, O. J., & Pinontoan, O. R. (2020). Ketinggian Tempat Dan Kejadian Demam Berdarah Dengue. Indonesian Journal Of Public Health and Community Medicine, 1(1), 12–18. https://doi.org/10.35801/ijphcm.1.1.2020.26642

Tjaden, N. B., Thomas, S. M., Fischer, D., & Beierkuhnlein, C. (2013). Extrinsic Incubation Period of Dengue: Knowledge, Backlog, and Applications of Temperature Dependence. PLoS Neglected Tropical Diseases, 7(6), 1–5. https://doi.org/10.1371/journal.pntd.0002207

Tran, A., L’Ambert, G., Lacour, G., Benoît, R., Demarchi, M., Cros, M., Cailly, P., Aubry-Kientz, M., Balenghien, T., & Ezanno, P. (2013). A rainfall- and temperature-driven abundance model for Aedes albopictus populations. International Journal of Environmental Research and Public Health, 10(5), 1698–1719. https://doi.org/10.3390/ijerph10051698

Tsheten, T., Clements, A. C. A., Gray, D. J., & Wangdi, K. (2021). Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan. PLoS Neglected Tropical Diseases, 15(2), 1–17. https://doi.org/10.1371/journal.pntd.0009021

Verdonschot, P. F. M., & Besse-Lototskaya, A. A. (2014). Flight distance of mosquitoes (Culicidae): A metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica, 45, 69–79. https://doi.org/10.1016/j.limno.2013.11.002

WHO Regional Publication SEARO. (2011). Comprehensive guidelines for prevention and control of dengue and dengue haemorrhagic fever. In WHO Regional Publication SEARO (Issue 1). https://apps.who.int/iris/handle/10665/204894

WHO. (2018). Managing Epidemics, Key facts about major deadly diseases. https://www.who.int/emergencies/diseases/managing-epidemics/en/

Xu, L., Stige, L. C., Chan, K. S., Zhou, J., Yang, J., Sang, S., Wang, M., Yang, Z., Yan, Z., Jiang, T., Lu, L., Yue, Y., Liu, X., Lin, H., Xu, J., Liu, Q., & Stenseth, N. C. (2017). Climate variation drives dengue dynamics. Proceedings of the National Academy of Sciences of the United States of America, 114(1), 113–118. https://doi.org/10.1073/pnas.1618558114

How to Cite
Pakaya, R., Hano, Y. H., & Olii, M. R. (2021). Hazard Level of Dengue Haemorrhagic Fever in Gorontalo Regency: Prediction of Spatial Distribution with AHP-GIS Integration. Al-Sihah: The Public Health Science Journal, 13(2), 126-139. https://doi.org/10.24252/al-sihah.v13i2.21788
Volume 13, Nomor 2, Tahun 2021
Abstract viewed = 309 times