Mathematical Assessment of Dengue Control Interventions in Cebu City, Philippines
DOI:
https://doi.org/10.37134/jsml.vol13.2.9.2025Keywords:
Dengue, Behavioral Change, Reproduction Number, Sensitivity analysisAbstract
Dengue remains a significant public health challenge in the Philippines, with Cebu City experiencing recurrent outbreaks. In this study, we utilized a modified SIR-SI dengue model to understand the transmission dynamics in Cebu City and to assess the impact of dengue control interventions. Parameters in the model were estimated using weekly reported dengue data obtained from the Philippines’ electronic Freedom of Information (eFOI) portal for the 20th to 52nd weeks of 2022. Our results indicate a substantial reduction in the reproduction number from 3.58, observed without any control measures, to 1.16 with the control interventions. Despite this reduction, the reproduction number remains above the critical threshold of 1, indicating a continued risk of outbreaks. Sensitivity analysis identified the mosquito biting rate and transmission rate as key parameters influencing dengue transmission. This suggests the need to enhance control interventions by focusing on reducing human-mosquito contact and lowering the mosquito population. We simulated dengue reduction scenarios with respect to two control measures: self-protective measures and vector control. The results reveal that implementing self-protective measures alone reduced hospitalized cases by 15.42%, while vector control measures led to a 49.38% reduction. Combining both strategies resulted in a substantial 98.6% reduction in hospitalized cases. These findings imply that the control interventions implemented in Cebu City during the 2022 dengue outbreak effectively reduced hospitalized cases (health-seeking individuals) by approximately 98.6%. To further reduce the number of infected individuals, the City government and regional health officials must enhance existing control measures by focusing on reducing human-mosquito contact and lowering the mosquito population through enhanced awareness programs.
Downloads
References
Andraud M, Hens N, Marais C, Beutels P. (2012). Dynamic epidemiological models for dengue transmission: a systematic review of structural approaches. PloS One, 7(11), e49085. doi:10.1371/journal.pone.0049085
Bhatt S, Gething P, Brady O, Messina J, Farlow A, Moyes C, Drake J, Brownstein J, Hoen A, Sankoh O, Myers M, George D, Jaenisch T, Wint G, Simmons C, Scott T, Farrar J, Hay S. (2013). The global distribution and burden of dengue. Nature, 496(7446), 504-507. doi:10.1038/nature12060
Carvalho SA, da Silva SO, Charret IDC. (2019). Mathematical modeling of dengue epidemic: control methods and vaccination strategies. Theory in Biosciences, 138(2), 223-239. doi:10.1007/s12064-019-00273-7
Brito da Cruz AMC, Rodrigues HS. (2021). Personal protective strategies for dengue disease: Simulations in two coexisting virus serotypes scenarios. Mathematics and Computers in Simulation, 188, 254-267. doi:10.1016/j.matcom.2021.04.002
Dayap JA, Rabajante JF. (2025). Mathematical model of dengue transmission dynamics with adaptive human behavior. Communication in Biomathematical Sciences, 8(1), 93-109. doi:10.5614/cbms.2025.8.1.7
de los Reyes AA, Escaner JML. (2018). Dengue in the Philippines: model and analysis of parameters affecting transmission. Journal of Biological Dynamics, 12(1), 894-912. doi:10.1080/17513758.2018.1535096
Emmanuel S, Sathasivam S, Hasmadi NHI, Mohamad Nasir NH, Velavan M. (2024). Estimating upsurge of HIV cases in Malaysia by using Heun’s predictor-corrector method. Journal of Science and Mathematics Letters, 12(1), 43-52. doi:10.37134/jsml.vol12.1.6.2024
Harapan H, Michie A, Sasmono RT, Imrie A. (2020). Dengue: A mini review. Viruses, 12(8), 829. doi:10.3390/v12080829
Heesterbeek JAP, Dietz K. (1996). The concept of Ro in epidemic theory. Statistica Neerlandica, 50(1), 89-110. doi:10.1111/j.1467-9574.1996.tb01482.x
Heesterbeek JA, Roberts, MG. (2007). The type-reproduction number T in models for infectious disease control. Mathematical Biosciences, 206(1), 3-10. doi:10.1016/j.mbs.2004.10.013
Herdicho FF, Hakim NA, Fatmawati F, Alfiniyah C, Akanni JO. (2025). Mathematical model of dengue hemorrhagic fever spread with different levels of transmission risk. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 19(3), 1649-1666. doi:10.30598/barekengvol19iss3pp1649-1666
Oluwafemi TJ, Azuaba E, Bako D, Dayap J. (2024). Global stability and sensitivity analysis of malaria, dengue and typhoid triple infection. Journal of Applied Sciences and Environmental Management, 28(2), 543-549. doi:10.4314/jasem.v28i2.27
Ong EP, Obeles AJT, Ong BAG, Tantengco OAG. (2022). Perspectives and lessons from the Philippines' decades-long battle with dengue. The Lancet regional health. Western Pacific, 24, 100505. doi:10.1016/j.lanwpc.2022.100505
Rather IA, Parray HA, Lone JB, Paek WK, Lim J, Bajpai VK, Park Y H. (2017). Prevention and Control Strategies to Counter Dengue Virus Infection. Frontiers in Cellular and Infection Microbiology, 7, 336. doi:10.3389/fcimb.2017.00336
Rejuso AM, Sampayan SC, Petallo KV, Pulaw FA, Rodriguez KC, Rosales ZG, Mumtaz M, Saber NI, Salonga EL, Sente AR, Silvela AB, Krishna KR, Pepito ZP, Masalunga MC, Banaay AL. (2024). Spatiotemporal analysis of dengue cases in Cebu City from year 2015 to 2022. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 417-424. doi:10.5194/isprs-archives-XLVIII-4-W8-2023-417-2024
Undurraga EA, Edillo FE, Erasmo JNV, Alera MTP, Yoon IK, Largo FM, Shepard DS. (2017). Disease burden of dengue in the Philippines: Adjusting for underreporting by comparing active and passive dengue surveillance in Punta Princesa, Cebu City. The American Journal of Tropical Medicine and Hygiene, 96(4), 887-898. doi:10.4269/ajtmh.16-0488
van den Driessche P. (2017). Reproduction numbers of infectious disease models. Infectious Disease Modelling, 2(3), 288-303. doi:10.1016/j.idm.2017.06.002
van den Driessche P, Watmough J. (2002). Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical biosciences, 180, 29-48. doi:10.1016/s0025-5564(02)00108-6
Wahid I, Ishak H, Hafid A, Fajri M, Sidjal S, Nurdin A, Naisyah TA, Sudirman R, Hasan H, Yusuf, M, Bachtiar I, Hawley W, Rosenberg R, Lobo N. (2019). Integrated vector management with additional pre-transmission season thermal fogging is associated with a reduction in dengue incidence in Makassar, Indonesia: Results of an 8-year observational study. Plos Neglected Tropical Diseases, 13(8), e0007606. doi:10.1371/journal.pntd.0007606
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Jonecis Dayap, Gemma Amazona, Audrey Verano, Carla Jean Ybañez
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
