Unveiling Eco-Epidemiological Risk Assessment through Bayesian Spatio-Temporal SPDE-INLA Approach

Mukhsar Mukhsar; Ida Usman; Asrul Sani; La Gubu; Muzuni Muzuni; Ruslan Majid; I Putu Sudayasa; Fahmiati Fahmiati

doi:10.20956/j.v22i3.49930

Authors

Mukhsar Mukhsar Statistics Department, Faculty of Mathematics and Natural Sciences Halu Oleo University
Ida Usman Department of Physics Faculty of Mathematics and Natural Sciences Halu Oleo University, Kendari-Indonesia
Asrul Sani
La Gubu Department of Mathematics Faculty of Mathematics and Natural Sciences Halu Oleo University, Kendari-Indonesia
Muzuni Muzuni Department of Biology Faculty of Mathematics and Natural Sciences Halu Oleo University, Kendari-Indonesia
Ruslan Majid Public Health Department Faculty of Public health Halu Oleo University, Kendari-Indonesia
I Putu Sudayasa
Fahmiati Fahmiati Department of Chemistry Faculty of Mathematics and Natural Sciences Halu Oleo University, Kendari-Indonesia

DOI:

https://doi.org/10.20956/j.v22i3.49930

Keywords:

Bayesian, DHF, INLA, ,Relative Risk, SPDE

Abstract

Dengue hemorrhagic fever (DHF) remains a major public health burden in tropical areas. The DHF driven by nonlinear interactions, vector dynamics, and population density. Characterizing spatio-temporal risk heterogeneity is critical to targeted intervention. We analyzed dengue risk in Kendari using a Bayesian Stochastic Partial Differential Equation (SPDE) via Integrated Nested Laplace Approximation (INLA). DHF monthly data from 2022–2024 were integrated with geospatial information to estimate relative risk and spatial risk contours. Model performance was compared with Generalized Linear Models (GLM), Intrinsic Conditional Autoregressive (ICAR), and second order Random Walk (RW2). Kendari Barat and Kendari districts emerged as primary hotspots, while Kadia, Mandonga, Baruga, Kambu, and Wua-Wua were high-risk districts. Abeli, Poasia, and Puuwatu districts exhibited moderate risk. Lalodati, Soropia, and Sampara districts showed lower risk. Risk contours revealed clusters concentrated in the urban core and along major corridors, highlighting the influence of settlement density and spatial connectivity. The SPDE–INLA model outperformed GLM, ICAR, and RW2 in capturing spatial structure and improving predictive accuracy. High resolution risk estimates supported specific district including intensified source reduction before and during peak rainfall, selective fogging, larval control, community education, microclimate monitoring, and early case screening in high-risk areas.

References

1. Achee N.L., Gould F., Perkins T.A., Reiner R.C., Morrison A.C., Ritchie S.A., et al., 2015. A Critical Assessment of Vector Control for Dengue Prevention. PLoS Negl Trop Dis 9. https://doi.org/10.1371/journal.pntd.0003655.

2. Alto B.W., Zhang X., Mei H., Nie P., Hu X., Feng J., 2025. Future Climate Predicts Range Shifts and Increased Global Habitat Suitability for 29 Aedes Mosquito Species. https://doi.org/10.3390/insects.

3. Asnita Yani, 2024. The Influence of Environmental Factors on the Development of Dengue Fever. The International Science of Health Journal 2:116–23. https://doi.org/10.59680/ishel.v2i4.1569.

4. Belmont J.., Martino S., Illian J., Rue H.,2-24. Spatio-temporal occupancy models with INLA. Methods Ecol Evol, 15, 2087–100. https://doi.org/10.1111/2041-210X.14422.

5. Clarotto L., Allard D., Romary T., Desassis N., 2024. The SPDE approach for spatio-temporal datasets with advection and diffusion. Spat Stat, 62. https://doi.org/10.1016/j.spasta.2024.100847.

6. Cortés R.R., Thanjangreed W., Chertenko T., 2025. Relationship Between Environmental Sanitation and Dengue Hemorrhagic Fever Incidents. Journal of Health Innovation and Environmental Education, 2,43–51. https://doi.org/10.37251/jhiee.v2i1.1736.

7. Feng F., Ma Y., Zhao Y., Liu Z., Zhang R., Wan Z., 2025. Assessment of Global Dengue Transmission Risk Under Future Climate Scenarios. Earths Future,13. https://doi.org/10.1029/2025EF006154.

8. Gubler D.J., 2011. Dengue, Urbanization and globalization: The unholy trinity of the 21 st century. Trop Med Health, 39, 3–11. https://doi.org/10.2149/tmh.2011-S05.

9. Ipa M., Hermawan A., Yunarko R., Ramadhani T., Hidajat M.C., Hendarwan H., et al., 2026. Urban-rural disparities in self-reported dengue infection: A comprehensive analysis of the 2023 Indonesian health survey. Glob Transit, 8, 10–21. https://doi.org/10.1016/j.glt.2025.08.003.

10. Jaya I.G.N.M., Folmer H., 2024. High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia. Mathematics, 12. https://doi.org/10.3390/math12182899.

11. Lindgren F., Rue H., Lindström J., 2011. An explicit link between Gaussian fields and Gaussian Markov random fields. The stochastic partial differential equation approach. vol. 73.

12. Lindgren F., 2015. Bayesian Spatial Modelling with R-INLA. vol. 63.

13. Liu-Helmersson J., Rocklöv J., Sewe M., Brännström Å., 2019. Climate change may enable Aedes aegypti infestation in major European cities by 2100. Environ Res, 172:693–9. https://doi.org/10.1016/j.envres.2019.02.026.

14. Mamenun, Koesmaryono Y., Sopaheluwakan A., Hidayati R., Dasanto B.D., Aryati R., 2024. Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia. Insects, 15. https://doi.org/10.3390/insects15050366.

15. Muhamad F.T., Azizah R., 2023. The Impact of Environmental and Behavioral Factors on the Incidence of Dengue Hemorrhagic Fever in Indonesia: Meta-analysis. Poltekita : Jurnal Ilmu Kesehatan, 17:762–70. https://doi.org/10.33860/jik.v17i3.3133.

16. Mukhsar, Tenriawaru A., Wibawa G.N.A., Abapihi B., Ahmad S.W., Sudayasa I.P., 2025. Bayesian Stochastic INLA Application to the SIR-SI Model for Investigating Dengue Transmission Dynamics. Intelligent Automation and Soft Computing, 40:177–93. https://doi.org/10.32604/iasc.2025.058884.

17. Mukhsar, Wibawa G.N.A., Tenriawaru A., Usman I., Firihu M.Z., Variani V.I., et al., 2023. Stochastic Bayesian Runge-Kutta method for dengue dynamic mapping. MethodsX, 10. https://doi.org/10.1016/j.mex.2022.101979.

18. Nakase T., Giovanetti M., Obolski U., Lourenço J., 2023. Global transmission suitability maps for dengue virus transmitted by Aedes aegypti from 1981 to 2019. Sci Data, 10. https://doi.org/10.1038/s41597-023-02170-7.

19. Rubino C., Di Maria C., Abbruzzo A., Bono G., Garofalo G., Milisenda G., et al., 2025. Derivative-based spatial mediation with INLA-SPDE. Spat Stat, 66. https://doi.org/10.1016/j.spasta.2025.100885.

20. Rulli, M.C., D’Odorico P., Galli N., John R.S., Muylaert R.L., Santini M., et al., 2025. Land Use Change and Infectious Disease Emergence. Reviews of Geophysics, 63. https://doi.org/10.1029/2022RG000785.

21. Salim M.F., Satoto T.B.T., Danardono, 2025. Understanding local determinants of dengue: a geographically weighted panel regression approach in Yogyakarta, Indonesia. Trop Med Health , 53. https://doi.org/10.1186/s41182-025-00734-4.

22. Saldaña F., Stollenwerk N., Van Dierdonck J.B., Aguiar M., 2024. Modeling spillover dynamics: understanding emerging pathogens of public health concern. Sci Rep, 14. https://doi.org/10.1038/s41598-024-60661-y.

23. Sani A., Abapihi B., Mukhsar, Tosepu R., Usman I., Rahman G.A., 2023. Bayesian temporal, spatial and spatio-temporal models of dengue in a small area with INLA. International Journal of Modelling and Simulation, 43:939–51. https://doi.org/10.1080/02286203.2022.2139108.

24. Sureshkumar S., Shekhar S., 2025. Impact of urban heat island effect on dengue incidence: a remote sensing approach using thermal and high-resolution optical imagery. BMC Public Health, 25. https://doi.org/10.1186/s12889-025-23763-4.