Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia

  • Heni Prasetyowati Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran, Indonesia.
  • Pandji Wibawa Dhewantara | Center for Research and Development of Public Health Effort, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Jakarta, Indonesia.
  • Joni Hendri Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran, Indonesia.
  • Endang Puji Astuti Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran, Indonesia.
  • Yalemzewod Assefa Gelaw Population Child Health Research Group, School of Women’s & Children’s Health, UNSW, NSW Australia; Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
  • Harapan Harapan Medical Research Unit, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia; Tropical Disease Centre, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia; Department of Microbiology, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia.
  • Mara Ipa Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Ministry of Health of Indonesia, Pangandaran, Indonesia.
  • Widyastuti Widyastuti Jakarta Provincial Health Office, Jakarta, Indonesia.
  • Dwi Oktavia Tatri Lestari Handayani Jakarta Provincial Health Office, Jakarta, Indonesia.
  • Ngabila Salama Jakarta Provincial Health Office, Jakarta, Indonesia.
  • Mirsal Picasso Jakarta Provincial Health Office, Jakarta, Indonesia.


The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman’s rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran’s I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= –0.459, P<0.01) and Tmin (r= –0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.



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Anselin L, 1995. Local indicators of spatial association - LISA. Geogr Anal 27:93-115. DOI:

Ashok K, Guan Z, Saji NH, Yamagata T, 2004. Individual and Combined Influences of ENSO and the Indian Ocean Dipole on the Indian Summer Monsoon. J. Climate 17:3141-55. DOI:<3141:IACIOE>2.0.CO;2

Astuti EP, Dhewantara PW, Prasetyowati H, Ipa M, Herawati C, Hendrayana K, 2019. Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance. Parasit Vectors 12:186. DOI:

Aswi A, Cramb S, Moraga P, Mengersen K. Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review. Epidemiol Infect 2018;147:1-14.

Atique S, Abdul SS, Hsu CY, Chuang TW, 2016. Meteorological influences on dengue transmission in Pakistan. Asian Pac J Trop Med 9:954-61. DOI:

Banu S, Guo Y, Hu W, Dale P, Mackenzie JS, Mengersen K, Tong S, 2015. Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh. Sci Rep 5:16105. DOI:

Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, Brownstein JS, Hoen AG, Sankoh O, Myers MF, George DB, Jaenisch T, Wint GR, Simmons CP, Scott TW, Farrar JJ, Hay SI, 2013. The global distribution and burden of dengue. Nature 496:504-7. DOI:

Biswal S, Reynales H, Saez-Llorens X, Lopez P, Borja-Tabora C, Kosalaraksa P, Sirivichayakul C, Watanaveeradej V, Rivera L, Espinoza F, Fernando L, Dietze R, Luz K, Venâncio da Cunha R, Jimeno J, López-Medina E, Borkowski A, Brose M, Rauscher M, LeFevre I, Bizjajeva S, Bravo L, Wallace D; TIDES Study Group, 2019. Efficacy of a tetravalent dengue vaccine in healthy children and adolescents. N Engl J Med 381:2009-19. DOI:

Carvalho S, Magalhães MAFM, Medronho RA, 2017. Analysis of the spatial distribution of dengue cases in the city of Rio de Janeiro, 2011 and 2012. Rev Saude Publica 51:79. DOI:

Chen Y, Zhao Z, Li Z, Li W, Li Z, Guo R, Yuan Z, 2019. Spatiotemporal transmission patterns and determinants of dengue fever: a case study of Guangzhou, China. Int J Environ Res Public Health 16:2486. DOI:

Cleveland RB, Cleveland WS, McRae JE, Terpenning I, 1990. STL: a seasonal-trend decomposition. J Off Stat 6:3-73.

Desjardins MR, Casas I, Victoria AM, Carbonell D, Dávalos DM, Delmelle EM, 2020. Knowledge, attitudes, and practices regarding dengue, chikungunya, and Zika in Cali, Colombia. Health Place 63:102339. DOI:

Diptyanusa A, Lazuardi L, Jatmiko RH, 2020. Implementation of geographical information systems for the study of diseases caused by vector-borne arboviruses in Southeast Asia: A review based on the publication record. Geospat Health 15:862. DOI:

Fletcher-Lartey SM, Caprarelli G, 2016. Application of GIS technology in public health: successes and challenges. Parasitology 143:401-15. DOI:

Fuentes-Vallejo M, 2017. Space and space-time distributions of dengue in a hyper-endemic urban space: the case of Girardot, Colombia. BMC Infect Dis 17:512. DOI:

Gimenez JO, Alvarez CN, Almirón WR, Stein M, 2020. Meteorological variables associated with the temporal oviposition rate of Aedes aegypti (Diptera: Culicidae) in Resistencia city, Chaco province, Northeastern Argentina. Acta Trop 212:105678. DOI:

Hamid PH, Prastowo J, Ghiffari A, Taubert A, Hermosilla C, 2017. Aedes aegypti resistance development to commonly used insecticides in Jakarta, Indonesia. PLoS One 12:e0189680. DOI:

Harapan H, Michie A, Yohan B, Shu PY, Mudatsir M, Sasmono RT, Imrie A, 2019a. Dengue viruses circulating in Indonesia: A systematic review and phylogenetic analysis of data from five decades. Rev Med Virol 29:e2037. DOI:

Harapan H, Michie A, Mudatsir M, Sasmono RT, Imrie A, 2019b. Epidemiology of dengue hemorrhagic fever in Indonesia: analysis of five decades data from the National Disease Surveillance. BMC Res Notes 12:350. DOI:

Harapan H, Michie A, Sasmono RT, Imrie A, 2020. Dengue: a minireview. Viruses 12:829. DOI:

Hasanah, Susanna D, 2019. Weather implication for dengue fever in Jakarta, Indonesia 2008-2016. KnE Life Sci 4:184. DOI:

Hashizume M, Terao T, Minakawa N, 2009. The Indian Ocean Dipole and malaria risk in the highlands of western Kenya. Proc Natl Acad Sci U S A 106:1857-62. DOI:

Karyanti MR, Uiterwaal CS, Kusriastuti R, Hadinegoro SR, Rovers MM, Heesterbeek H, Hoes AW, Bruijning-Verhagen P, 2014. The changing incidence of dengue haemorrhagic fever in Indonesia: a 45-year registry-based analysis. BMC Infect Dis 14:1-7. DOI:

Kikuti M, Cunha GM, Paploski IA, Kasper AM, Silva MM, Tavares AS, Cruz JS, Queiroz TL, Rodrigues MS, Santana PM, Lima HCAV, Calcagno J, Takahashi D, Gonçalves AHO, Araújo JMG, Gauthier K, Diuk-Wasser MA, Kitron U, Ko AI, Reis MG, Ribeiro GS, 2015. Spatial distribution of dengue in a Brazilian urban slum setting: role of socioeconomic gradient in disease risk. PLoS Negl Trop Dis 9:e0003937. DOI:

Lestari CSW, Yohan B, Yunita A, Meutiawati F, Hayati RF, Trimarsanto H, Sasmono RT, 2017. Phylogenetic and evolutionary analyses of dengue viruses isolated in Jakarta, Indonesia. Virus Genes 53:778-88. DOI:

Li Q, Cao W, Ren H, Ji Z, Jiang H, 2018. Spatiotemporal responses of dengue fever transmission to the road network in an urban area. Acta Trop 183:8-13. DOI:

Machault V, Yébakima A, Etienne M, Vignolles C, Palany P, Tourre YM, Guérécheau M, Lacaux, JP, 2014. Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data. ISPRS Int. J Geo-Inf 3:1352-71. DOI:

Ministry of Health of Indonesia, 2018. 2018 Indonesia health profile. Ministry of Health, Jakarta, Indonesia.

Moran PAP, 1950. Notes on continuous stochastic phenomena. Biometrika 37:17-23. DOI:

Morin CW, Comrie AC, Ernst K, 2013. Climate and dengue transmission: evidence and implications. Environ Health Perspect 121:1264-72. DOI:

Oliveira MA, Ribeiro H, Castillo-Salgado C, 2013. Geospatial analysis applied to epidemiological studies of dengue: a systematic review. Rev Bras Epidemiol 16:907-17. DOI:

Polwiang S, 2020. The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017). BMC Infect Dis 12:208. DOI:

Prasetyowati H, Ginanjar A, 2017. Maya Indeks dan Kepadatan Larva Aedes aegypti di Daerah Endemis DBD Jakarta Timur. Vektora 9:43. DOI:

Pybus OG, Tatem AJ, Lemey P, 2015. Virus evolution and transmission in an ever more connected world. Proc R Soc B 282:20142878. DOI:

Qi X, Wang Y, Li Y, Meng Y, Chen Q, Ma J, Gao GF, 2015. The effects of socioeconomic and environmental factors on the incidence of dengue fever in the Pearl River Delta, China, 2013. PLoS Negl Trop Dis 9:e0004159. DOI:

Ren H, Zheng L, Li Q, Yuan W, Lu L, 2017. Exploring determinants of spatial variations in the dengue fever epidemic using geographically weighted regression model: a case study in the joint Guangzhou-Foshan Area, China, 2014. Int J Environ Res Public Health 14:1518. DOI:

Santos J, Honorio NA, Nobre AA, 2019. Definition of persistent areas with increased dengue risk by detecting clusters in populations with differing mobility and immunity in Rio de Janeiro, Brazil. Cad Saude Publica 35:e00248118. DOI:

Schmidt WP, Suzuki M, Thiem VD, White RG, Tsuzuki A, Yoshida LM, Yanai H, Haque U, Tho le H, Anh DD, Ariyoshi K, 2011. Population density, water supply, and the risk of dengue fever in Vietnam: cohort study and spatial analysis. PLoS Med 8:e1001082. DOI:

Seidahmed OM, Eltahir EA, 2016. A sequence of flushing and drying of breeding habitats of Aedes aegypti (L.) Prior to the low dengue season in Singapore. PLoS Negl Trop Dis 10:e0004842. DOI:

Shepard DS, Undurraga EA, Halasa YA, Stanaway JD, 2016. The global economic burden of dengue: a systematic analysis. Lancet Infect Dis 16:935-41. DOI:

Sumarmo T, 1987. Dengue haemorrhagic fever in Indonesia. Southeast Asian J Trop Med Public Health 18:269-74.

Therneau T, Atkinson B, Ripley B, 2019. Package ‘rpart’ [Online]. Available from:

Thi Tuyet-Hanh T, Nhat Cam N, Thi Thanh Huong L, Khanh Long T, Mai Kien T, Thi Kim Hanh D, Huu Quyen N, Nu Quy Linh T, Rocklöv J, Quam M, Van Minh H, 2018. Climate Variability and Dengue Hemorrhagic Fever in Hanoi, Viet Nam, During 2008 to 2015. Asia Pac J Public Health 30:532-41. DOI:

Tosepu R, Tantrakarnapa K, Nakhapakorn K, Worakhunpiset S, 2018. Climate variability and dengue hemorrhagic fever in Southeast Sulawesi Province, Indonesia. Environ Sci Pollut Res 25:14944-52. DOI:

Watts MJ, Kotsila P, Mortyn PG, Sarto I Monteys V, Urzi Brancati C, 2020. Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico. Int J Health Geogr 19:44. DOI:

WHO (World Health Organization), 2011. Comprehensive guideline for prevention and control of dengue and dengue haemorrhagic fever. World Health Organization, Geneva, Switzerland.

Xu Z, Bambrick H, Yakob L, Devine G, Lu J, Frentiu FD, Yang W, Williams G, Hu W, 2019. Spatiotemporal patterns and climatic drivers of severe dengue in Thailand. Sci Total Environ 656:889-901. DOI:

Zambrano LI, Rodriguez E, Espinoza-Salvado IA, Fuentes-Barahona IC, Lyra de Oliveira T, Luciano da Veiga G, Cláudio da Silva J, Valle-Reconco JA, Rodríguez-Morales AJ, 2019. Spatial distribution of dengue in Honduras during 2016-2019 using a geographic information systems (GIS)-Dengue epidemic implications for public health and travel medicine. Travel Med Infect Dis 2019:101517. DOI:

Zhu G, Liu T, Xiao J, Zhang B, Song T, Zhang Y, Lin L, Peng Z, Deng A, Ma W, Hao Y, 2019. Effects of human mobility, temperature and mosquito control on the spatiotemporal transmission of dengue. Sci Total Environ 651:969-78. DOI:

Original Articles
Dengue, spatial analysis, cluster detection, climate, risk profile, Indonesia.
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How to Cite
Prasetyowati, H., Dhewantara, P. W., Hendri, J., Astuti, E. P., Gelaw, Y. A., Harapan, H., Ipa, M., Widyastuti, W., Handayani, D. O. T. L., Salama, N., & Picasso, M. (2021). Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia. Geospatial Health, 16(1).