Original Articles
Vol. 21 No. 1 (2025)
Bayesian modelling of dengue incidence with climatic drivers: comparing fixed-effects, nonlinear and dynamic approaches
Publisher's note
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.
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.
Published: 2 February 2026
0
Views
0
Downloads
Authors
Climatic variability plays a critical role in shaping dengue transmission dynamics, yet empirical findings remain inconsistent across studies. Divergent conclusions regarding the associations of temperature, relative humidity, wind speed, air pressure, precipitation, number of rainy days, and sunshine duration with dengue incidence often stem from unmodelled interactions and methodological limitations. To address these challenges, this study applies a Bayesian modelling framework to examine the associations between climatic drivers and dengue incidence in Bandung City, Indonesia, using monthly data from 2016 to 2024. We compared fixedeffects, nonlinear and dynamic modelling approaches to evaluate both the direction and magnitude of these associations while addressing overdispersion and potential multicollinearity among predictors. Our findings highlight temperature and relative humidity as the primary climatic variables associated with temporal variations in dengue incidence, with effects manifesting most strongly at a two-month lag. These results underscore the importance of adopting robust Bayesian modeling frameworks to support early warning systems and inform evidence-based public health interventions for dengue control.
Downloads
Download data is not yet available.
Citations
Abdullah N, Dom N, Salleh S, Salim H, Precha N. 2022. The association between dengue case and climate: a systematic review and meta-analysis. One Health 15:100452.
Ahmad B, Ciupac-Ulici M, Beju DG. 2021. Economic and non-economic variables affecting fraud in European countries. Risks 9:119.
Al-Manji A, Wahaibi A, Al-Azri M, Chan M. 2025. Predicting mosquito-borne disease outbreaks using Poisson and negative binomial models: a comparative study. J Infect Public Health 18:102906.
Azhar K, Marina R, Anwar A. 2017. A prediction model of dengue incidence using climate variability in Denpasar city. Health Sci J Indones 8:68–73.
Bandung City. 2025. Bandung city in figures 2024. Bandung: Bandung City.
Blangiardo M, Cameletti M. 2015. Spatial and spatio-temporal Bayesian models with R-INLA. Chennai: John Wiley& Sons.
Campbell K, Lin C, Iamsirithaworn S, Scott T. 2013. The complex relationship between weather and dengue virus transmission in Thailand. Am J Trop Med Hyg 89:1066–80.
Colón-González F, Lake I, Bentham G. 2011. Climate variability and dengue fever in warm and humid Mexico. Am J Trop Med Hyg 84:757–763.
Descloux E, Mangeas M, Eugène C, Lengaigne M, Leroy A, Tehei T, Lamballerie X. 2012. Climate-based models for understanding and forecasting dengue epidemics. PLoS Negl Trop Dis 6:e1470.
Earnest A, Tan S, Wilder-Smith A. 2012. Meteorological factors and El Niño Southern Oscillation are independently associated with dengue infections. Epidemiol Infect 140:1244–51.
Figueredo M, Monteiro R, Silva A, Fontoura J, Silva A, Alves C. 2023. Analysis of the correlation between climatic variables and dengue cases in the city of Alagoinhas/BA. Sci Rep 13:7512.
Gharbi M, Quenel P, Gustave J, Cassadou S, Ruche G, Girdary L, Marrama L. 2011. Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors. BMC Infect Dis 11:166.
Gómez R, Kim J, Hong K, Jang J, Kisiju T, Kim S, Chun B. 2022. Association between climate factors and dengue fever in Asuncion, Paraguay: a generalized additive model. Int J Environ Res Public Health 19:12192.
Hu W, Clements A, Williams G, Tong S, Mengersen K. 2011. Spatial patterns and socioecological drivers of dengue fever transmission in Queensland, Australia. Environ Health Perspect 120:260–6.
Jaya IGNM, Andriyana Y, Tantular B, Pangastuti S, Kristiani F. 2025. Spatiotemporal dengue forecasting for sustainable public health in Bandung, Indonesia: a comparative study of classical, machine learning, and Bayesian models. Sustainability 17:6777.
Jaya IGNM, Folmer H. 2020. Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia. J Geogr Syst 22:105–142.
Jaya IGNM, Folmer H. 2024. Does the inclusion of spatiotemporally confounded covariates improve the forecasting accuracy of spatiotemporal models? A simulation study of univariate and causal forecasting models. Geogr Syst 0:1–40.
Karasinghe N, Peiris S, Jayathilaka R, Dharmasena T. 2024. Forecasting weekly dengue incidence in Sri Lanka: modified autoregressive integrated moving average modeling approach. PLoS ONE 19:e0299953.
Kirk D, Straus S, Childs ML, Harris M, Couper L, Davies TJ, Mordecai E. 2024. Temperature impacts on dengue incidence are nonlinear and mediated by climatic and socioeconomic factors: a meta-analysis. PLoS Clim 3:e0000152.
Lu X, Teh S, Tay C, Kassim N, Fam P, Soewono E. 2025. Application of multiple linear regression model and long short-term memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables. Infect Dis Model 10:240–56.
Majeed S, Akram W, Sufyan M, Abbasi A, Riaz S, Faisal S, Kucher O. 2025. Climate change: a major factor in the spread of Aedes aegypti (Diptera: Culicidae) and its associated dengue virus. Insects 16:513.
Martheswaran T, Hamdi H, Al-Barty A, Zaid A, Das B. 2022. Prediction of dengue fever outbreaks using climate variability and Markovchain Monte Carlo techniques in a stochastic susceptible-infected-removed model. Sci Rep 12:5459.
Martínez-Bello D, López-Quílez A, Torres-Priet A. 2017. Bayesian dynamic modeling of time series of dengue disease case counts. PLoS Negl Trop Dis 11:e0005696.
Monintjaa T, Arsin A, Amiruddin R, Syafar M. 2021. Analys is of temperature and humidity on dengue hemorrhagic fever in Manado municipality. Gac Sanit 35:S330–3.
Morin C, Comrie A, Ernst K. 2013. Climate and dengue transmission: evidence and implications. Environ Health Perspect 121:1264–73.
Ramspek C, Steyerberg E, Riley R, Rosendaal F, Dekkers O, Dekker F, Diepen M. 2021. Prediction or causality? A scoping review of their conflation within current observationaresearch. Eur J Epidemiol 36:889–898.
Roslan M, Ngui R, Marzuki M, Vythilingam I, Shafie A, Musa S, Sulaiman W. 2022. Spatial dispersal of Aedes albopictus mosquitoes captured by the modified stickyovitrap in Selangor, Malaysia. Geospat Health 17:1–11.
Silva ST, Gabrick EC, Protachevicz PR, Iarosz KC, Caldas IL, Batista AM, Kurth J. 2025. When climate variables improve the dengue forecasting: a machine learning approach. Eur Phys J Spec Top 234:555–69.
Wang Y, Chong KC, Ren C. 2024. Impact of compound warm and wet events on dengue fever infection in South and Southeast Asian countries. Environ Res 263:120091.
WHO. 2024. Dengue and severe dengue. World Health Organization. Accessed August 2025. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue
Yu X, Wang X, Tang S. 2025. Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis. Sci Rep 15:15311.
How to Cite
Bayesian modelling of dengue incidence with climatic drivers: comparing fixed-effects, nonlinear and dynamic approaches. (2026). Geospatial Health, 21(1). https://doi.org/10.4081/gh.2025.1461
Copyright (c) 2026 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
