A Mathematical Model of Dengue Fever Transmission Dynamics Incorporating Climate Variability and Human Mobility in Indonesia
Abstract
Dengue Fever remains a significant public health issue in Indonesia, with frequent outbreaks exacerbated by varying climatic conditions and human mobility. Understanding the dynamics of its transmission is critical to developing effective control strategies. This study aims to develop a mathematical model that incorporates climate variability and human mobility to assess the transmission dynamics of Dengue Fever in Indonesia. The model utilizes a compartmental framework, where the population is divided into susceptible, infected, and recovered individuals. The impact of climate factors such as temperature and rainfall, along with human mobility patterns, is integrated through differential equations. The study uses historical epidemiological data from the Indonesian Ministry of Health, alongside climate data from the Indonesian Meteorological Agency and human mobility data derived from mobile phone usage and transportation systems. Numerical simulations are conducted to predict the effects of climate variability and mobility on Dengue Fever outbreaks. Results indicate that both climate change and human mobility significantly influence the frequency and intensity of outbreaks, with certain regions being more vulnerable to epidemic peaks. The study concludes that incorporating environmental and social factors into epidemiological models can enhance the accuracy of Dengue Fever predictions and inform targeted intervention strategies.
Full text article
References
Abbasi, E. (2025). The impact of climate change on travel-related vector-borne diseases: A case study on dengue virus transmission. Travel Medicine and Infectious Disease, 65, 102841. https://doi.org/https://doi.org/10.1016/j.tmaid.2025.102841
Abdullah, N., Mohamad, W. M. W., Ahmad, T., & Bakar, S. A. (2025). Simulation and analysis of dengue transmission dynamics using advanced fuzzy arithmetic. Ecological Modelling, 510, 111341. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2025.111341
Agboka, K. M., Ngángá, A. M., Sokame, B. M., Baleba, S. S. B., Landmann, T., Abdel-Rahman, E. M., Tanga, C. M., & Diallo, S. (2025). Climate-driven potential for tularemia in East Africa: skill testing and ecological consistency of a transferred risk model. Spatial and Spatio-Temporal Epidemiology, 55, 100756. https://doi.org/https://doi.org/10.1016/j.sste.2025.100756
Akbar, K. F., Nishida, D., & Sitopu, J. W. (2024). Mathematical Biology?: Modeling the Dynamics of Ecosystems and Biodiversity. 1(October), 308–316. https://doi.org/10.70177/scientia.v1i6.1586
Al-Manji, A., Al Wahaibi, A., Al-Azri, M., & Chan, M. F. (2025). Predicting mosquito-borne disease outbreaks using poisson and negative binomial models: A comparative study. Journal of Infection and Public Health, 18(11), 102906. https://doi.org/https://doi.org/10.1016/j.jiph.2025.102906
Aldila, D., Chávez, J. P., Chukwu, C. W., Fathiyah, A. Y., Puspita, J. W., Setio, K. A. D., Fuady, A., & Kamalia, P. Z. (2024). Unraveling dengue dynamics with data calibration from Palu and Jakarta: Optimizing active surveillance and fogging interventions. Chaos, Solitons & Fractals, 189, 115729. https://doi.org/https://doi.org/10.1016/j.chaos.2024.115729
Aljabali, A. A. A., Obeid, M. A., El-Tanani, M., Mishra, V., Mishra, Y., & Tambuwala, M. M. (2024). Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics. Gene, 905, 148174. https://doi.org/https://doi.org/10.1016/j.gene.2024.148174
Andrade, R., White, S. M., & Cobbold, C. A. (2025). Incorporating adult age into mosquito population models: Implications for predicting abundances in changing climates. Journal of Theoretical Biology, 604, 112084. https://doi.org/https://doi.org/10.1016/j.jtbi.2025.112084
Aristianti, F., & Phase, M. I. (2025). Benefits and Risks of Community Use E-Wallet as an Alternative Transaction. 1(December 2024), 232–241. https://doi.org/10.70177/scientia.v1i6.1476
Aryanti, W. E., & Phase, M. I. (2025). Analysis of Indonesian Sharia Bank Marketing Strategy in Facing Financial Industry Competition. 1(December 2024), 217–231. https://doi.org/10.70177/scientia.v1i6.1490
Bentaleb, D., & Amine, S. (2025). Stochastic extension of a two-strain SIR model with non-monotone incidence functions: Analysis of stochastic thresholds and disease dynamics. Results in Physics, 76, 108396. https://doi.org/https://doi.org/10.1016/j.rinp.2025.108396
Bhattacharyya, J., & Roelke, D. L. (2025). Wolbachia-based mosquito control: Environmental perspectives on population suppression and replacement strategies. Acta Tropica, 262, 107517. https://doi.org/https://doi.org/10.1016/j.actatropica.2024.107517
Bhowmick, S., Irwin, P., Lopez, K., Fritz, M. L., & Smith, R. L. (2025). A weather-driven mathematical model of Culex population abundance and the impact of vector control interventions. Ecological Informatics, 89, 103163. https://doi.org/https://doi.org/10.1016/j.ecoinf.2025.103163
Carvalho, R. L., Anjos, D., Harmange, C., Pinter, A., Faust, C., Streicker, D., Lorenz, C., Prist, P. R., & Metzger, J. P. (2025). Unpacking the risks of zoonotic and vector-borne pathogen transmission to humans in the context of environmental change. One Earth, 8(8), 101348. https://doi.org/https://doi.org/10.1016/j.oneear.2025.101348
Drouin, A., Balenghien, T., Durand, B., Aranda, C., Bennouna, A., Bouattour, A., Boubidi, S. C., Conte, A., Delacour, S., Goffredo, M., Himmi, O., L’Ambert, G., Schaffner, F., & Chevalier, V. (2025). Modelling the population dynamics of Rift Valley fever virus mosquito vectors in the western Mediterranean Basin. Ecological Modelling, 502, 111013. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2024.111013
Fauzi, I. S., Nuraini, N., Ayu, R. W. S., Wardani, I. B., & Rosady, S. D. N. (2025). Seasonal pattern of dengue infection in Singapore: A mechanism-based modeling and prediction. Ecological Modelling, 501, 111003. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2024.111003
Haque, S., Mengersen, K., Barr, I., Wang, L., Yang, W., Vardoulakis, S., Bambrick, H., & Hu, W. (2024). Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. Environmental Research, 249, 118568. https://doi.org/https://doi.org/10.1016/j.envres.2024.118568
Helo Sarmiento, J., Melo, O., Ortiz-Alvarado, L., Pantoja Vallejos, C., & Reyes-Mandujano, I. F. (2023). Economic impacts associated with the health effects of climate change in South America: a scoping review. The Lancet Regional Health - Americas, 26, 100606. https://doi.org/https://doi.org/10.1016/j.lana.2023.100606
Iqbal, K., Ahmad, O., & Vandika, A. Y. (2024). Quantum Computing and Its Implications for Complex System Analysis. 1(October), 238–247. https://doi.org/10.70177/scientia.v1i5.1579
Islam, M. S., Shahrear, P., Saha, G., Ataullha, M., & Rahman, M. S. (2024). Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach. Computers in Biology and Medicine, 178, 108707. https://doi.org/https://doi.org/10.1016/j.compbiomed.2024.108707
Jang, G., Seo, J., & Lee, H. (2025). Analyzing the impact of COVID-19 on seasonal infectious disease outbreak detection using hybrid SARIMAX-LSTM model. Journal of Infection and Public Health, 18(7), 102772. https://doi.org/https://doi.org/10.1016/j.jiph.2025.102772
Jibon, M. J. N., Ruku, S. M. R. P., Islam, A. R. M. T., Khan, M. N., Mallick, J., Bari, A. B. M. M., & Senapathi, V. (2024). Impact of climate change on vector-borne diseases: Exploring hotspots, recent trends and future outlooks in Bangladesh. Acta Tropica, 259, 107373. https://doi.org/https://doi.org/10.1016/j.actatropica.2024.107373
Jie, O. C., Zulkepli, N. F. S., Gobithaasan, R. U., Kasihmuddin, M. S. M., Naeeim, N. S. A., Noorani, M. S. M., & Musa, K. I. (2025). Comparative stability analysis of mixed clustering algorithms for Malaysian dengue epidemiology using topological descriptors. Acta Tropica, 270, 107769. https://doi.org/https://doi.org/10.1016/j.actatropica.2025.107769
Kaye, A. R., Obolski, U., Sun, L., Hart, W. S., Hurrell, J. W., Tildesley, M. J., & Thompson, R. N. (2024). The impact of natural climate variability on the global distribution of Aedes aegypti: a mathematical modelling study. The Lancet Planetary Health, 8(12), e1079–e1087. https://doi.org/https://doi.org/10.1016/S2542-5196(24)00238-9
Knoblauch, S., Mukaratirwa, R. T., Pimenta, P. F. P., de A Rocha, A. A., Yin, M. S., Randhawa, S., Lautenbach, S., Wilder-Smith, A., Rocklöv, J., Brady, O. J., Biljecki, F., Dambach, P., Jänisch, T., Resch, B., Haddawy, P., Bärnighausen, T., & Zipf, A. (2025). Urban Aedes aegypti suitability indicators: a study in Rio de Janeiro, Brazil. The Lancet Planetary Health, 9(4), e264–e273. https://doi.org/https://doi.org/10.1016/S2542-5196(25)00049-X
Lamwong, J., & Pongsumpun, P. (2025a). A fractional derivative model of the dynamic of dengue transmission based on seasonal factors in Thailand. Journal of Computational and Applied Mathematics, 457, 116256. https://doi.org/https://doi.org/10.1016/j.cam.2024.116256
Lamwong, J., & Pongsumpun, P. (2025b). Atangana-Baleanu fractional optimal control for dengue dynamics with stability analysis. Computers in Biology and Medicine, 194, 110476. https://doi.org/https://doi.org/10.1016/j.compbiomed.2025.110476
Li, N., Ruan, S., & Tian, H. (2025). Interactions between zoonotic pathogens and infectious disease spread: Why understanding mechanisms and modelling matters more than ever. Biosafety and Health. https://doi.org/https://doi.org/10.1016/j.bsheal.2025.07.008
Lu, X., Teh, S. Y., Tay, C. J., Abu Kassim, N. F., Fam, P. S., & 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. Infectious Disease Modelling, 10(1), 240–256. https://doi.org/https://doi.org/10.1016/j.idm.2024.10.007
Meher, M. M., Afrin, M., Bayazid, A. Al, Islam, M. S., & Ali, M. Z. (2025). Deciphering the impact of heat wave in the global surge of infectious diseases. Hygiene and Environmental Health Advances, 15, 100135. https://doi.org/https://doi.org/10.1016/j.heha.2025.100135
Naaly, B. Z., Marijani, T., Isdory, A., & Ndendya, J. Z. (2024). Mathematical modeling of the effects of vector control, treatment and mass awareness on the transmission dynamics of dengue fever. Computer Methods and Programs in Biomedicine Update, 6, 100159. https://doi.org/https://doi.org/10.1016/j.cmpbup.2024.100159
Navarro Valencia, V. A., Díaz, Y., Pascale, J. M., Boni, M. F., & Sanchez-Galan, J. E. (2023). Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction number R0 for the Republic of Panama in the 1999-2022 period. Heliyon, 9(4), e15424. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e15424
Nisar, K. S., Farman, M., Abdel-Aty, M., & Ravichandran, C. (2024). A review of fractional order epidemic models for life sciences problems: Past, present and future. Alexandria Engineering Journal, 95, 283–305. https://doi.org/https://doi.org/10.1016/j.aej.2024.03.059
Panja, M., Chakraborty, T., Nadim, S. S., Ghosh, I., Kumar, U., & Liu, N. (2023). An ensemble neural network approach to forecast Dengue outbreak based on climatic condition. Chaos, Solitons & Fractals, 167, 113124. https://doi.org/https://doi.org/10.1016/j.chaos.2023.113124
Rahman, A. R., Munir, T., Fazal, M., Cheema, S. A., & Bhayo, M. H. (2025). Climatic determinants of monkeypox transmission: A multi-national analysis using generalized count mixed models. Journal of Virological Methods, 332, 115076. https://doi.org/https://doi.org/10.1016/j.jviromet.2024.115076
Rocklöv, J., Semenza, J. C., Dasgupta, S., Robinson, E. J. Z., Abd El Wahed, A., Alcayna, T., Arnés-Sanz, C., Bailey, M., Bärnighausen, T., Bartumeus, F., Borrell, C., Bouwer, L. M., Bretonnière, P.-A., Bunker, A., Chavardes, C., van Daalen, K. R., Encarnação, J., González-Reviriego, N., Guo, J., … Lowe, R. (2023). Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond. The Lancet Regional Health - Europe, 32, 100701. https://doi.org/https://doi.org/10.1016/j.lanepe.2023.100701
Safaei, S., Derakhshan-sefidi, M., & Karimi, A. (2025). Wolbachia: A bacterial weapon against dengue fever- a narrative review of risk factors for dengue fever outbreaks. New Microbes and New Infections, 65, 101578. https://doi.org/https://doi.org/10.1016/j.nmni.2025.101578
San Miguel, T. V., Da Re, D., & Andreo, V. (2024). A systematic review of Aedes aegypti population dynamics models based on differential equations. Acta Tropica, 260, 107459. https://doi.org/https://doi.org/10.1016/j.actatropica.2024.107459
Segala, F. V., Guido, G., Stroffolini, G., Masini, L., Cattaneo, P., Moro, L., Motta, L., Gobbi, F., Nicastri, E., Vita, S., Iatta, R., Otranto, D., Locantore, P., Occa, E., Putoto, G., Saracino, A., & Di Gennaro, F. (2025). Insights into the ecological and climate crisis: Emerging infections threatening human health. Acta Tropica, 262, 107531. https://doi.org/https://doi.org/10.1016/j.actatropica.2025.107531
Sutanto, H., & Ansharullah, B. A. (2025). The role of artificial intelligence for dengue prevention, control, and management: A technical narrative review. Acta Tropica, 268, 107741. https://doi.org/https://doi.org/10.1016/j.actatropica.2025.107741
Tu, N. M., Lan, T. T., & Vandika, A. Y. (2024). The Application of Artificial Intelligence in Quantum Mechanics?: Challenges and Opportunities. 1(December), 278–287. https://doi.org/10.70177/scientia.v1i6.1583
Wang, C.-X., Xiu, L.-S., Hu, Q.-Q., Lee, T.-C., Liu, J., Shi, L., Zhou, X.-N., Guo, X.-K., Hou, L., & Yin, K. (2023). Advancing early warning and surveillance for zoonotic diseases under climate change: Interdisciplinary systematic perspectives. Advances in Climate Change Research, 14(6), 814–826. https://doi.org/https://doi.org/10.1016/j.accre.2023.11.014
Wang, Y., Zhao, S., Wei, Y., Li, K., Jiang, X., Li, C., Ren, C., Yin, S., Ho, J., Ran, J., Han, L., Zee, B. C., & Chong, K. C. (2023). Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study. Infectious Disease Modelling, 8(3), 645–655. https://doi.org/https://doi.org/10.1016/j.idm.2023.05.008
Woldegerima, W. A., & Ugwu, C. L. J. (2025). Bayesian hierarchical modeling of Mpox in the African region (2022–2024): Addressing zero-inflation and spatial autocorrelation. Infectious Disease Modelling, 10(4), 1575–1591. https://doi.org/https://doi.org/10.1016/j.idm.2025.07.011
Yang, T., Du, X., Li, J., Zhang, T., Wang, Y., & Wang, L. (2025). Modeling transmission dynamics and socio-economic determinants of scarlet fever in Chengdu, China: An integrated SEIAR and machine learning approach. Epidemics, 52, 100844. https://doi.org/https://doi.org/10.1016/j.epidem.2025.100844
Zitzmann, C., Adia, N. A. B., Shah, P. S., & Manore, C. (2025). Opportunities in multiscale modeling of mosquito-borne flaviviruses. BioSystems, 257, 105593. https://doi.org/https://doi.org/10.1016/j.biosystems.2025.105593
Authors
Copyright (c) 2025 Ela Laelasari, Charlotte Harris, Rit Som

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.