Mathematical Model for Dengue Transmission: A Comparative Study from the Perspective of Urban and Rural Areas in Bangladesh
DOI:
https://doi.org/10.53799/4hgatn90Keywords:
Dengue transmission, parameter estimation, sensitivity analysis, urban-rural epidemiology.Abstract
Dengue, spread by Aedes mosquitoes (vector) is one of the major public health concerns in Bangladesh, with transmission patterns differing notably between urban and rural areas. This study develops a SEIR-SEI compartmental model to capture dengue dynamics, integrating human (Susceptible, Exposed, Infectious, Recovered) and mosquito (Susceptible, Exposed, Infectious) populations. Using daily case of infected and death data from Directorate General of Health Services (DGHS) within the time interval from January 2024 to March 2025, we estimate key parameters, including transmission $(\beta_1,\beta_2)$ and recovery (r) rates, via least squares optimization to fit observed trends in urban (city-corporation) and rural (outside-city-corporation) regions. A novel contribution is the comparative analysis of urban-rural transmission dynamics, revealing disparities that guide region specific interventions. Sensitivity analysis of the basic reproduction number $(R_0)$ identifies critical drivers of outbreaks, while stability analysis of the disease-free equilibrium informs control efficacy. Our findings advocate for targeted strategies, such as intensified urban vector control and rural awareness campaigns, to mitigate dengue’s spread. This model enhances data-driven public health planning, supporting effective outbreak mitigation in Bangladesh.References
World Health Organization, “Dengue and severe dengue,” 2024. Available: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. Accessed: October 15, 2024.
S. Sharmin, E. Viennet, K. Glass, and D. Harley, “The emergence of dengue in Bangladesh: Epidemiology, challenges, and future control strategies,” Journal of Infectious Diseases, vol. 218, supplement 2, pp. S155–S164, 2018.
Directorate General of Health Services, “Dengue Case Dashboard, Bangladesh,” 2024. Available: https://dashboard.dghs.gov.bd/pages/heoc_dengue_v1.php. Accessed: August 4, 2025.
The World Bank, “Urban population percent of total, Bangladesh,” 2024. Available: https://tradingeconomics.com/bangladesh/urban-population-percent-of-total-wb-data.html. Accessed: August 4, 2025.
MacroTrends, “Bangladesh population 1950–2025,” 2024. Available: https://www.macrotrends.net/global-metrics/countries/bgd/bangladesh/population. Accessed: August 4, 2025.
S. Banu, W. Hu, C. Hurst, and S. Tong, “Dengue transmission in the Asia-Pacific region: Impact of climate change and socio-environmental factors,” Tropical Medicine & International Health, vol. 19, no. 5, pp. 585–595, 2014.
P. Dhar-Chowdhury, C. E. Haque, and S. M. Driedger, “Dengue fever in rural Bangladesh: A case study of social and environmental factors,” EcoHealth, vol. 15, no. 2, pp. 356–368, 2018.
R. Ross, The Prevention of Malaria. John Murray, 1911.
N. T. J. Bailey, The Mathematical Theory of Infectious Diseases and Its Applications. Griffin, 1975.
L. Esteva and C. Vargas, “Analysis of a dengue disease transmission model,” Mathematical Biosciences, vol. 150, pp. 131–151, 1998. DOI: https://doi.org/10.1016/S0025-5564(98)10003-2
D. A. Focks, E. Ha Spa, and K. O. Kloter, “A simulation model of the epidemiology of urban dengue fever: Literature analysis, model development, preliminary validation, and samples of simulation results,” American Journal of Tropical Medicine and Hygiene, vol. 53, no. 5, pp. 489–506, 1995. DOI: https://doi.org/10.4269/ajtmh.1995.53.489
A. Tran and M. Raffy, “On the dynamics of dengue epidemics from large-scale remotely sensed data: A mathematical model,” Mathematical Biosciences, vol. 183, no. 2, pp. 163–176, 2003.
A. Pandey, A. Mubayi, and J. Medlock, “Comparing vector-host and SIR models for dengue transmission,” Mathematical Biosciences, vol. 246, no. 2, pp. 252–259, 2013. DOI: https://doi.org/10.1016/j.mbs.2013.10.007
E. A. Newton and P. Reiter, “A model of the transmission of dengue fever with an evaluation of the impact of ultra-low volume insecticide applications on dengue epidemics,” American Journal of Tropical Medicine and Hygiene, vol. 47, no. 6, pp. 709–720, 1992. DOI: https://doi.org/10.4269/ajtmh.1992.47.709
N. M. Ferguson, C. A. Donnelly, and R. M. Anderson, “Transmission dynamics and epidemiology of dengue: Insights from age-structured data,” Philosophical Transactions of the Royal Society B, vol. 354, no. 1384, pp. 757–768, 1999. DOI: https://doi.org/10.1098/rstb.1999.0428
P. Pongsumpun, R. M. O. Garcia, and I. M. Tang, “Transmission of dengue hemorrhagic fever in an age-structured population,” Mathematical and Computer Modelling, vol. 37, pp. 111–122, 2003. DOI: https://doi.org/10.1016/S0895-7177(03)00111-0
Centers for Disease Control and Prevention, “Dengue: Clinical and epidemiological information,” 2024. Available: https://www.cdc.gov/dengue/index.html. Accessed: August 4, 2025.
American Mosquito Control Association, “Aedes aegypti lifespan and behavior,” 2024. Available: https://doi.org/10.2987/moco-32-01-1-11.1. Accessed: August 4, 2025. DOI: https://doi.org/10.2987/moco-32-01-1-11.1
A. Moser, B. Smith, and C. Jones, “Transmission dynamics and behavior of Aedes aegypti in urban environments,” Journal of Tropical Diseases, vol. 58, no. 3, pp. 123–134, 2024.
R. Armstrong, L. Richards, and E. King, “Dengue virus incubation periods and its effect on transmission models,” Epidemiological Studies, vol. 15, no. 4, pp. 312–321, 2023.
D. J. Gubler and P. Reiter, “The impact of climate change on the spread of dengue fever,” International Journal of Climate Change and Health, vol. 9, no. 1, pp. 45–56, 2025.
S. Valdes, P. Gomez, and D. Ortega, “Mosquito lifespan and its role in the transmission of dengue,” Vector-Borne Diseases, vol. 42, no. 2, pp. 89–96, 2023.
B. Hasan, “A study on numerical modeling of vector-borne diseases,” Ph.D. dissertation, Swinburne University of Technology, 2018. Available: https://researchbank.swinburne.edu.au/file/491f6158-d102-40fe-8835-f6e3c0e01575/1/bushra_hasan_thesis.pdf.
P. van den Driessche and J. Watmough, “Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission,” Mathematical Biosciences, vol. 180, pp. 29–48, 2002. DOI: https://doi.org/10.1016/S0025-5564(02)00108-6
MacroTrends, “Bangladesh life expectancy 1950–2025,” 2024. Available: https://www.macrotrends.net/global-metrics/countries/bgd/bangladesh/life-expectancy. Accessed: August 4, 2025.
N. G. Reich, J. Lessler, D. A. T. Cummings, and R. Brookmeyer, “Estimating absolute and relative case fatality ratios from infectious disease surveillance data,” Biometrics, vol. 68, no. 2, pp. 598–606, 2012. DOI: https://doi.org/10.1111/j.1541-0420.2011.01709.x
MathWorks, “MATLAB Optimization Toolbox: lsqcurvefit,” 2019. Available: https://www.mathworks.com/help/optim/ug/lsqcurvefit.html. Accessed: August 4, 2025.
G. Chowell et al., “Transmission dynamics and control of severe acute respiratory syndrome,” Science, vol. 300, pp. 196–198, 2007.
Downloads
Published
Data Availability Statement
Data can be shared on reasonable request to the corresponding author.
Issue
Section
License
Copyright (c) 2026 AIUB Journal of Science and Engineering (AJSE)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
AJSE contents are under the terms of the Creative Commons Attribution License. This permits anyone to copy, distribute, transmit and adapt the work non-commercially provided the original work and source is appropriately cited.
