Mathematical Model of Dengue Transmission Dynamics with Adaptive Human Behavior
DOI:
https://doi.org/10.5614/cbms.2025.8.1.7Keywords:
dengue, behavioral change, reproduction number, sensitivity analysisAbstract
Dengue fever, a viral disease spread by Aedes mosquitoes, is a significant public health issue in tropical and subtropical regions. Behavioral adaptations in response to perceived infection risks can significantly reduce disease incidence and prevalence through the adoption of control measures. However, most existing models developed to assess the mitigation of dengue only implicitly account for this adaptive behavior within the dynamics of disease transmission. In this paper, we propose a mathematical model that explicitly incorporates adaptive human behavior in response to community infection levels into the transmission dynamics of dengue and investigates how this behavior affects transmission. Analytical results of the model reveal that the diseasefree equlibrium is locally asymptotically stable when the basic reproduction number (R0) is less than 1. The model parameters are calibrated using daily dengue case data from the 2015 outbreak in Kaohsiung City, Taiwan, resulting in a calculated basic reproduction number (R0) of 1.42. Sensitivity analysis indicates that to reduce the reproduction number, efforts should focus on reducing mosquito-human contact, controlling
the mosquito population, and improving hospital treatment. Numerical simulations demonstrate that positive behavioral changes in response to increasing infection levels significantly reduce dengue cases when selfprotective
and vector control measures are effectively implemented. Our results emphasize the importance of enhancing these behavioral changes to achieve a substantial reduction in dengue incidence. This highlights the critical role of reporting disease prevalence, educating individuals on effective dengue mitigation strategies, and ensuring access to resources necessary for high-efficacy self-protection and vector control measures. By promoting awareness and providing support for control measures such as mosquito repellents, bed nets, insecticide-treated curtains, and community clean-up drives to eliminate mosquito breeding sites, governments can significantly enhance the effectiveness of dengue control programs.
References
Bhatt, S., Gething, P.W., Brady, O.J., Messina, J.P., Farlow, A.W., Moyes, C.L., Drake, J.M., Brownstein, J.S., Hoen, A.G., Sankoh, O. and Myers, M.F., The global distribution and burden of dengue, Nature, 496(7446), pp. 504-507, 2013.
Gould, E., Pettersson, J., Higgs, S., Charrel, R. and De Lamballerie, X., Emerging arboviruses: Why today?, One Health, 4, pp. 1-13, 2017.
World Health Organization, Dengue and severe dengue, 2023. https://www.who.int/news-room/fact-sheets/detail/dengue-andsevere-dengue, Accessed on March 17, 2023.
Gubler, D.J., Ooi, E.E., Vasudevan, S. and Farrar, J., Dengue and dengue hemorrhagic fever, CABI, 2014.
Wilder-Smith, A., Vannice, K.S., Hombach, J., Farrar, J. and Nolan, T., Population perspectives and World Health Organization recommendations for CYD-TDV dengue vaccine, The Journal of Infectious Diseases, 214(12), pp. 1796-1799, 2016.
Dusfour, I., Vontas, J., David, J.P., Weetman, D., Fonseca, D.M., Corbel, V., Raghavendra, K., Coulibaly, M.B., Martins, A.J., Kasai, S. and Chandre, F., Management of insecticide resistance in the major Aedes vectors of arboviruses: Advances and challenges, PLoS Neglected Tropical Diseases, 13(10), p. e0007615, 2019.
Moyes, C.L., Vontas, J., Martins, A.J., Ng, L.C., Koou, S.Y., Dusfour, I., Raghavendra, K., Pinto, J., Corbel, V., David, J.P. and Weetman, D., Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans, PLoS Neglected Tropical Diseases, 11(7), p. e0005625, 2017.
Suwannapong, N., Tipayamongkholgul, M., Bhumiratana, A., Boonshuyar, C., Howteerakul, N. and Poolthin, S., Effect of community participation on household environment to mitigate dengue transmission in Thailand, Trop. Biomed., 31(1), pp. 149-158, 2014.
Rugkua, R. and Rungsihirunrat, K., Implementation of a larval and pupal source reduction program (LSRP) for the prevention and control of dengue haemorrhagic fever (DHF) in a community in Krabi Province, Thailand, J. Health Res., 27(4), pp. 225-232, 2013.
Vaughan, E., Contemporary perspectives on risk perceptions, health-protective behaviors, and control of emerging infectious diseases, International Journal of Behavioral Medicine, 18(2), pp. 83-87, 2011.
Funk, S., Salathe, M. and Jansen, V.A., Modelling the influence of human behaviour on the spread of infectious diseases: a review, Journal of the Royal Society Interface, 7(50), pp. 1247-1256, 2010.
Perra, N., Balcan, D., Goncalves, B. and Vespignani, A., Towards a characterization of behavior-disease models, PloS One, 6(8), p. e23084, 2011.
Pant, B., Safdar, S., Santillana, M. and Gumel, A.B., Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics in the United States, Bulletin of Mathematical Bology, 86(8), p. 92, 2024.
Zhi, S., Niu, H.T., Su, Y.H. and Han, X., Influence of human behavior on COVID-19 dynamics based on a reaction?diffusion model, Qualitative Theory of Dynamical Systems, 22(3), p. 113, 2023.
Fenichel, E.P., Castillo-Chavez, C., Ceddia, M.G., Chowell, G., Parra, P.A.G., Hickling, G.J., Holloway, G., Horan, R., Morin,
B., Perrings, C. and Springborn, M., Adaptive human behavior in epidemiological models, Proceedings of the National Academy of Sciences, 108(15), pp. 6306-6311, 2011.
Zheng, T.T. and Nie, L.F., Modelling the transmission dynamics of two-strain Dengue in the presence awareness and vector control, Journal of Theoretical Biology, 443, pp. 82-91, 2018.
Abidemi, A. and Peter, O.J., Host-vector dynamics of dengue with asymptomatic, isolation and vigilant compartments: insights from modelling, The European Physical Journal Plus, 138(3), pp. 1-22, 2023.
Aldila, D., Optimal control for dengue eradication program under the media awareness effect, International Journal of Nonlinear Sciences and Numerical Simulation, 24(1), pp. 95-122, 2023.
Liao, C.M., Huang, T.L., Cheng, Y.H., Chen, W.Y., Hsieh, N.H., Chen, S.C. and Chio, C.P., Assessing dengue infection risk in the southern region of Taiwan: implications for control, Epidemiology & Infection, 143(5), pp. 1059-1072, 2015.
Ellis, A.M., Garcia, A.J., Focks, D.A., Morrison, A.C. and Scott, T.W., Parameterization and sensitivity analysis of a complex simulation model for mosquito population dynamics, dengue transmission, and their control, The American Journal of Tropical Medicine and Hygiene, 85(2), p. 257-264, 2011.
Aldila, D., Ndii, M.Z., Anggriani, N., Tasman, H. and Handari, B.D., Impact of social awareness, case detection, and hospital capacity on dengue eradication in Jakarta: a mathematical model approach, Alexandria Engineering Journal, 64, pp. 691-707, 2023.
Chen, S.C. and Hsieh, M.H., Modeling the transmission dynamics of dengue fever: implications of temperature effects, Science of the Total Environment, 431, pp. 385-391, 2012.
Nuraini, N., Soewono, E. and Sidarto, K.A., Mathematical model of dengue disease transmission with severe DHF compartment, Bulletin of the Malaysian Mathematical Sciences Society, 30(2), 2007.
Xue, L., Ren, X., Magpantay, F., Sun, W. and Zhu, H., Optimal control of mitigation strategies for dengue virus transmission, Bulletin of Mathematical Biology, 83(2), p. 8, 2021.
Misra, A.K., Sharma, A., and Li, J., A Mathematical model for control of vector borne diseases through media campaigns, Discrete & Continuous Dynamical Systems-Series B, 18(7), pp. 1909-1927, 2013.
Mishra, A. and Gakkhar, S., The effects of awareness and vector control on two strains dengue dynamics, Applied Mathematics and Computation, 246, pp. 159-167, 2014.
Van den Driessche, P. and Watmough, J., Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, 180(1-2), pp. 29-48, 2002.
May, R.M., Stability and complexity in model ecosystems (Vol. 6). Princeton University Press, 2001.
Wang, S.F., Chang, K., Loh, E.W., Wang, W.H., Tseng, S.P., Lu, P.L., Chen, Y.H. and Chen, Y.M.A., Consecutive large dengue outbreaks in Taiwan in 2014?2015, Emerging Microbes & Infections, 5(1), pp. 1-3, 2016.
Center for Disease Control (Taiwan), Taiwan national infectious disease statistics system for dengue virus surveillance, Taiwan CDC: Taiwan, 2016. http://nidss.cdc.gov.tw/en/SingleDisease.aspx?dc=1&dt=2&disease=061, Accessed on October 1, 2023.
Taiwan Open Data. (n.d.), Daily confirmed dengue case data, Taiwan Government Open Database [Online], 2023. https://data.gov.tw/dataset/21025, Accessed on October 1, 2023.
Cheng, Y.C., Lee, F.J., Hsu, Y.T., Slud, E.V., Hsiung, C.A., Chen, C.H., Liao, C.L., Wen, T.H., Chang, C.W., Chang, J.H. and Wu, H.Y., Real-time dengue forecast for outbreak alerts in Southern Taiwan, PLoS Neglected Tropical Diseases, 14(7), p. e0008434, 2020.
MacroTrends, Taiwan life expectancy: 1950-2024. (n.d.), 2023. https://www.macrotrends.net/global-metrics/countries/TWN/taiwan/life-expectancy, Accessed on October 1, 2023.
CEIC data, Taiwan population: Taiwan Area: Kaohsiung City, 2018. https://www.ceicdata.com/en/taiwan/population/population-taiwan-area-kaohsiung-city, Accessed on October 1, 2023.
Abidemi, A., Fatoyinbo, H.O., Asamoah, J.K.K. and Muni, S.S., Evaluation of the efficacy of Wolbachia intervention on dengue burden in a population: a mathematical insight, In 2022 International Conference on Decision Aid Sciences and Applications (DASA), IEEE, pp. 1618-1627, 2022.
Taghikhani, R., Sharomi, O. and Gumel, A.B., Dynamics of a two-sex model for the population ecology of dengue mosquitoes in the presence of Wolbachia, Mathematical Biosciences, 328, p. 108426, 2020.
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