Original Articles
18 July 2025
Vol. 20 No. 2 (2025)
A Bayesian spatiotemporal Poisson conditional autoregressive model for dengue haemorrhagic fever in Indonesia integrating satellite-generated environmental data
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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In association with cases of Dengue Haemorrhagic Fever (DHF), Indonesia’s Breteau Index has consistently fallen below the national standard of 95% over the past 12 years (2007–2019). Currently, the country relies on survey methods to map DHF spread, but these methods are costly and require substantial resource support since monitoring DHF cases necessitates considering both spatial and temporal aspects. As an alternative, we proposed a pilot study utilizing a localized version of the hierarchical Bayesian spatiotemporal conditional autoregressive model (LHBSTCARM) to predict the DHF cases in Makassar City, Indonesia. Using this approach, we examined the relationship between DHF and the normalized difference built-up index (NDBI), the Normalized Difference Vegetation Index (NDVI), and the Normalized Difference Water Index (NDWI) that were downloaded from the Sentinel-2 satellite. Based on these datasets, we identified an optimal LHBSTCARM model that classified areas in Makassar City into distinct spatial risk groups based on the likelihood of dengue occurrence. Specifically, the model identified four districts with low relative risk, one with high relative risk and the remaining districts with moderate relative risk. Incorporating covariates, the model also revealed that NDVI and NDWI were significant predictors for dengue outbreaks, whereas NDBI was not. Both significant covariates showed negative effects, with a one-unit increase in NDVI and NDWI associated with reductions in DHF cases by 84.5% and 81.5%, respectively. Thus, NDVI and NDWI are the environmental variables of choice for the prediction of DHF incidence.
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Alghamdi T, Elgazzar K, Sharaf T. 2021. Spatiotemporal traffic prediction using hierarchical bayesian modeling. Futur Internet 9:1–6. DOI: https://doi.org/10.1109/ICCSPA49915.2021.9385767
Anggraeni F, Mahmudah N. 2021. Bayesian Spatial Survival Lognormal 3 Parameter models for event processes dengue fever in Tuban. IAENG Int J Appl Math 51:1–8.
Ardiansyah M, Wijayanto H, Kurnia A, Djuraidah A. 2023. Geo-additive mixed model with variable selection using the adaptive elastic net to handle nonresponse in official rice productivity survey. Spat Stat 56:100761. DOI: https://doi.org/10.1016/j.spasta.2023.100761
Aswi A, Cramb S, Duncan E, Hu W, White G, Mengersen K. 2020a. Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling. Spat Spatiotemporal Epidemiol. 33;100335. DOI: https://doi.org/10.1016/j.sste.2020.100335
Aswi A, Cramb S, Duncan E, Hu W, White G, Mengersen K. 2020b. Bayesian spatial survival models for hospitalisation of Dengue: A case study of Wahidin hospital in Makassar, Indonesia. Int J Environ Res Public Health 17:0878. DOI: https://doi.org/10.3390/ijerph17030878
Aswi A, Sukarna S, Cramb S, Mengersen K. 2021. Effects of climatic factors on dengue incidence: a comparison of bayesian spatio-temporal models. J Phys Conf Ser 1863:012050. DOI: https://doi.org/10.1088/1742-6596/1863/1/012050
Baharom M, Ahmad N, Hod R, Manaf MRA. 2022. Dengue early warning system as outbreak prediction tool: a systematic review. Risk Manag Healthc Policy 15:871–86. DOI: https://doi.org/10.2147/RMHP.S361106
Bergquist R, Manda S. 2019. The world in your hands: Geohealth then and now. Geospat Health 14:779. DOI: https://doi.org/10.4081/gh.2019.779
BPS-Makassar. 2024. Kota Makassar dalam Angka 2024. Makassar.
Chen ZY, Deng XY, Zou Y, He Y, Chen SJ, Wang QT, Xing DG, Zhang Y. 2023. A Spatio-temporal Bayesian model to estimate risk and influencing factors related to tuberculosis in Chongqing, China, 2014–2020. Arch Public Heal 81:1–12. DOI: https://doi.org/10.1186/s13690-023-01044-z
Dian Maya Sari, Sarumpaet SM, Hiswani. 2018. Determinan Kejadian Demam Berdarah Dengue (DBD) di Kecamatan Medan Tembung. J Kesehat Pena Med 8:9–25.
Faridah L, Suroso DSA, Fitriyanto MS, Andari CD, Fauzi I, Kurniawan Y, Watanabe K. 2022. Optimal validated multi-factorial climate change risk assessment for adaptation planning and evaluation of infectious disease: a case study of dengue hemorrhagic fever in Indonesia. Trop Med Infect Dis 7:0172. DOI: https://doi.org/10.3390/tropicalmed7080172
Fitri DJ, Djuraidah A, Wijayanto H. 2024. bayesian conditional negative binomial autoregressive model: a case study of stunting on Java Island 2021. Commun Math Biol Neurosci 2024:8281.
García JA, Acero FJ, Portero J. 2023. A Bayesian hierarchical spatio-temporal model for extreme temperatures in Extremadura (Spain) simulated by a regional climate model. Clim Dyn 61:1489–503. DOI: https://doi.org/10.1007/s00382-022-06638-x
Hatulesila JW, Mardiatmoko G, Irwanto I. 2019. Analisis Nilai Indeks Kehijauan (NDVI) pada Pola Ruang Kota Ambon, Provinsi Maluku. J Hutan Pulau-Pulau Kecil 3:55–67. DOI: https://doi.org/10.30598/jhppk.2019.3.1.55
Hii YL, Zhu H, Ng N, Ng LC, Rocklo J. 2012. Forecast of dengue incidence using temperature and rainfall. PLoS Negl Trop Dis 6:1908. DOI: https://doi.org/10.1371/journal.pntd.0001908
Islam MT, Quispe C, Herrera-Bravo J, Sarkar C, Sharma R, Garg N, Fredes LI, Martorell M, Alshehri MM, Sharifi-Rad J, et al. 2021. Production, transmission, pathogenesis, and control of dengue virus: a literature-based undivided perspective. Biomed Res Int 2021:1–23. DOI: https://doi.org/10.1155/2021/4224816
Khaidir K, Zara N, Ikhsan R. 2022. Gambaran Penyakit Demam Berdarah Dengue di Poliklinik Umum Puskesmas Muara Batu Aceh Utara. Galen J Kedokt dan Kesehat Mhs Malikussaleh 1:44 DOI: https://doi.org/10.29103/jkkmm.v1i1.7909
Kurniawati Y, Wijayanto H, Kurnia A, Domiri DD, Susetyo B. 2023. Selection of multinomial logit models based on accuracy reclassification of the area sampling frame labels. Sci Technol Asia 28:18–30.
Kusumaningrum D, Wijayanto H, Kurnia A, Notodiputro KA, Ardiansyah M, Parvez IM. 2024. Four-parameter beta mixed models with survey and sentinel 2A satellite data for predicting paddy productivity. Smart Agric Technol 9:100525. DOI: https://doi.org/10.1016/j.atech.2024.100525
Lee D, Sarran C. 2015. Controlling for unmeasured confounding and spatial misalignment in long‐term air pollution and health studies. Environmetrics 26:477-87. DOI: https://doi.org/10.1002/env.2348
Lee D, Lawson A. 2016. Quantifying the spatial inequality and temporal trends in maternal smoking rates in Glasgow. Ann Appl Stat 10:1427–46. DOI: https://doi.org/10.1214/16-AOAS941
Lian Q, Sun W, Dong W. 2023. Hierarchical spatial-temporal neural network with attention mechanism for traffic flow forecasting. Appl Sci. 1:9729. DOI: https://doi.org/10.3390/app13179729
Louis VR, Phalkey R, Horstick O, Ratanawong P, Wilder-Smith A, Tozan Y, Dambach P. 2014. Modeling tools for dengue risk mapping - a systematic review. Int J Health Geogr 13:1–15. DOI: https://doi.org/10.1186/1476-072X-13-50
Martheswaran TK, Hamdi H, Barty A Al, Zaid AA, Das B. 2022. Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible ‑ infected ‑ removed model. Sci Rep 12:1–17. DOI: https://doi.org/10.1038/s41598-022-09489-y
Moreno-Madriñán MJ, Crosson WL, Eisen L, Estes SM, Estes MG, Hayden M, Hemmings SN, Irwin DE, Lozano-Fuentes S, Monaghan AJ, et al. 2014. Correlating remote sensing data with the abundance of pupae of the dengue virus mosquito vector, Aedes Aegypti, in Central Mexico. ISPRS Int J Geo-Information 3:732–49. DOI: https://doi.org/10.3390/ijgi3020732
Muhsoni FF. 2015. Penginderaan Jaun (Remote Sensing). Bangkalan-Madura: UTMPRESS. Available from: https://msp.trunojoyo.ac.id/wp-content/uploads/2018/07/2015_Penginderaan-Jauh_Firman-Farid-Muhsoni.pdf.
Mukhopadhyay S, Ogutu JO, Bartzke G, Dublin HT, Piepho HP. 2019. Modelling spatio-temporal variation in sparse rainfall data using a hierarchical Bayesian regression model. J Agric Biol Environ Stat 24:369–393. DOI: https://doi.org/10.1007/s13253-019-00357-3
Rasjid A, Yudhastuti R, Notobroto HB, Hartono R. 2019. Climate change: an overview of the prevalence of dengue hemorrhagic fever in the South Sulawesi Province of Indonesia. Indian J Public Heal Res Dev 10:1982–6. DOI: https://doi.org/10.5958/0976-5506.2019.02143.0
Silitonga P, Bustamam A, Muradi H, Mangunwardoyo W, Dewi BE. 2021. Comparison of dengue predictive models developed using artificial neural network and discriminant analysis with small dataset. Appl Sci 11:1–16. DOI: https://doi.org/10.3390/app11030943
Solís-Navarro M, Vargas-De-León C, Gúzman-Martínez M, Corzo-Gómez J. 2022. A Bayesian prediction spatial model for confirmed dengue cases in the State of Chiapas, Mexico. Hindawi J Trop Med 2022:1–13. DOI: https://doi.org/10.1155/2022/1971786
Song J, Liu J, Zhang X, Chen X, Shang Y, Gao F. 2024. Spatio-Temporal fluctuation analysis of ecosystem service values in northeast china over long time series: based on Bayesian hierarchical modeling. Land 13:1–20. DOI: https://doi.org/10.3390/land13060833
Suhet. 2015. SENTINEL-2: user handbook. Hoersch B, editor. European Space Agency.
Sukarna S, Notodiputro KA, Sartono B. 2023. Comparison between binomial GLMM and binomial GMET for temporary unemployment in West Java, Indonesia. Adv Comput Sci Res 109:198-209. DOI: https://doi.org/10.2991/978-94-6463-332-0_22
Sukarna, Wahyuni MS, Syam R. 2021. Comparison of Bayesian Spatio-temporal Models of Tuberculosis in Makassar, Indonesia. J Phys Conf Ser 2123012048. DOI: https://doi.org/10.1088/1742-6596/2123/1/012048
Supangat U, Badriah DL, Mamlukah M, Suparman R. 2023. Faktor-Faktor yang Berhubungan dengan Kematian Kasus Demam Berdarah di Kota Tasikmalaya 2022. J Heal Res Sci 3:63–71. DOI: https://doi.org/10.34305/jhrs.v3i01.764
Susanti, Suharyo. 2017. Hubungan Lingkungan Fisik dengan Keberadaan Jentik Aedes pada Area Bervegetasi Pohon Pisang. Unnes J Public Heal 6:271–7. DOI: https://doi.org/10.15294/ujph.v6i4.15236
WHO. 2004. Disease Outbreak News: 2004 - Indonesia. WHO Homepage., forthcoming. [accessed 2024 Nov. 1]. https://www.who.int/emergencies/disease-outbreak-news/item/2004_05_11a-en.
WHO. 2024. Disease Outbreak News: Dengue - Global situation. WHO Homepage., forthcoming. [accessed 2024 Nov. 1]. https://www.who.int/emergencies/disease-outbreak-news/item/2024-DON518.
Yu H-L, Lee C-H, Chien L-C. 2016. A spatiotemporal dengue fever early warning model accounting for nonlinear associations with hydrological factors : a Bayesian maximum entropy approach. Stoch Environ Res Risk Assess 30:2127–41. DOI: https://doi.org/10.1007/s00477-016-1328-1
Ziemann A, Fairchild G, Conrad J, Manore C, Parikh N, Del Valle S, Generous N. 2018. Predicting dengue incidence in Brazil using broad-scale spectral remote sensing imagery. Int Geosci Remote Sens Symp 2018:2076–8. DOI: https://doi.org/10.1109/IGARSS.2018.8518771
Supporting Agencies
Beasiswa Pendidikan Indonesia (BPI), Penelitian Disertasi Doktor (PDD)How to Cite
A Bayesian spatiotemporal Poisson conditional autoregressive model for dengue haemorrhagic fever in Indonesia integrating satellite-generated environmental data. (2025). Geospatial Health, 20(2). https://doi.org/10.4081/gh.2025.1379
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