Epidemiological Analysis of Dengue Incidence in Doroluman and Greenfield, Arakan, Cotabato: Focus on Peak Outbreak Months (2021–2025)

This study analyzed the epidemiological patterns of dengue incidence in Barangay Greenfield and Doroluman, Arakan, Cotabato, from 2021 to 2025 using a retrospective descriptive epidemiological research design. Secondary data were obtained from the Municipal Health Office of Arakan and analyzed through frequency counts, percentage distributions, a Mann-Kendall time-series trend test with Sen’s slope estimation, chi-square tests of association and goodness-of-fit, and comparative analysis. The study aimed to determine the annual distribution of dengue cases, identify the monthly distribution and peak outbreak months, examine seasonal trends, and compare dengue incidence between the two barangays. Findings revealed a total of 191 dengue cases during the five-year period, with Barangay Greenfield recording 127 cases (64.55%) and Barangay Doroluman recording 64 cases (35.45%), a difference confirmed as statistically significant by chi-square testing (p < .001). Dengue incidence increased from 2021 to 2024 at an estimated average rate of 8.75 cases per year (Sen’s slope, combined series), reaching its highest level in 2024 before declining in 2025; the Mann-Kendall test indicated a moderate-to-strong positive monotonic trend (Kendall’s τ = 0.70) that did not reach conventional statistical significance given the limited five-year series (p = .130). Monthly distribution showed that cases occurred throughout the year but peaked during the rainy season, particularly in July and August, a seasonal concentration confirmed by chi-square goodness-of-fit testing (p < .001). Comparative analysis indicated that Barangay Greenfield consistently experienced a higher dengue burden than Barangay Doroluman. The findings support the Epidemiologic Triad Theory and Disease Ecology Theory, emphasizing the influence of environmental conditions on dengue transmission. Continuous surveillance, timely vector control, environmental management, and community participation are recommended to reduce future dengue outbreaks.

dengue incidence, epidemiological analysis, seasonal trends, vector control, Barangay Greenfield & Doroluman.

1. Achee, N. L., Gould, F., Perkins, T. A., Reiner, R. C., Morrison, A. C., Ritchie, S. A., Gubler, D. J., Teyssou, R., & Scott, T. W. (2021). A critical assessment of vector control for dengue prevention. PLoS Neglected Tropical Diseases, 15(1), e0008931. https://doi.org/10.1371/journal.pntd.0008931 [Google Scholar] [Crossref]

2. Brady, O. J., Gething, P. W., Bhatt, S., Messina, J. P., Brownstein, J. S., Hoen, A. G., Moyes, C. L., Farlow, A. W., Scott, T. W., & Hay, S. I. (2022). Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Neglected Tropical Diseases, 16(4), e0010364. https://doi.org/10.1371/journal.pntd.0010364 [Google Scholar] [Crossref]

3. Campbell, K. M., Lin, C. D., Iamsirithaworn, S., & Scott, T. W. (2023). The complex relationship between weather variability and dengue transmission in tropical regions. Pathogens, 12(4), 563. https://doi.org/10.3390/pathogens12040563 [Google Scholar] [Crossref]

4. Department of Health. (2021–2025). Dengue surveillance reports 2021–2025. Department of Health, Philippines. [Google Scholar] [Crossref]

5. Liu-Helmersson, J., Brännström, Å., Sewe, M. O., Semenza, J. C., & Rocklöv, J. (2021). Estimating past, present, and future trends in the global distribution and abundance of the dengue vector Aedes aegypti under climate change scenarios. Frontiers in Public Health, 9, 729437. https://doi.org/10.3389/fpubh.2021.729437 [Google Scholar] [Crossref]

6. Local Health Unit–Arakan. (2025). Dengue case records, 2021–2025. Municipal Health Office, Arakan, Cotabato. [Google Scholar] [Crossref]

7. Lowe, R., Stewart-Ibarra, A. M., Petrova, D., García-Díez, M., Borbor-Cordova, M. J., Mejía, R., Regato, M., & Rodó, X. (2021). Climate services for health: Predicting the evolution of dengue risk using climate variability and surveillance data. The Lancet Planetary Health, 5(6), e332–e341. https://doi.org/10.1016/S2542-5196(21)00043-1 [Google Scholar] [Crossref]

8. Messina, J. P., Brady, O. J., Golding, N., Kraemer, M. U. G., Wint, G. R. W., Ray, S. E., Pigott, D. M., Shearer, F. M., Johnson, K., Earl, L., & Hay, S. I. (2022). The current and future global distribution and population at risk of dengue. Nature Microbiology, 7(5), 714–726. https://doi.org/10.1038/s41564-022-01076-5 [Google Scholar] [Crossref]

9. Murray, N. E. A., Quam, M. B., & Wilder-Smith, A. (2023). Epidemiology of dengue: Past, present, and future prospects. Clinical Epidemiology, 15, 145–159. https://doi.org/10.2147/CLEP.S391208 [Google Scholar] [Crossref]

10. Nguyen, H. T., Rocklöv, J., Wilder-Smith, A., & Tozan, Y. (2023). Dengue epidemiology, climate change, and the challenges for public health in endemic regions: A systematic review. International Journal of Environmental Research and Public Health, 20(9), 5603. https://doi.org/10.3390/ijerph20095603 [Google Scholar] [Crossref]

11. Sangkawibha, N., Phumee, A., Yanola, J., & Somboon, P. (2022). Seasonal variation and climatic factors associated with dengue incidence in tropical regions: A systematic review. Tropical Medicine and Infectious Disease, 7(10), 280. https://doi.org/10.3390/tropicalmed7100280 [Google Scholar] [Crossref]

12. World Health Organization. (2024). Dengue and severe dengue. World Health Organization. retrieved June 20, 2026, from https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue [Google Scholar] [Crossref]

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