Predicting the Spread of Dengue (Break-Bone Fever) Using the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) Model

Althea Marie  Alcaraz; Angelica Battung

doi:10.65141/jessah.v2i2.n4

Authors

Althea Marie Alcaraz
Angelica M. Battung Roxas Stand-Alone Senior High School, Roxas, Isabela, 3320, Philippines1

DOI:

https://doi.org/10.65141/jessah.v2i2.n4

Keywords:

dengue fever, SEIRS Model, epidemiological modeling, disease forecasting, public health

Abstract

Dengue fever is a recurring public health concern in tropical countries like the Philippines. This study focused on predicting future dengue fever incidence in a particular municipality in the Philippines from 2025 to 2030, since it is a persistent public health challenge in the country. The Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) model was utilized for the prediction. The model was built using historical dengue case data (2019–2024), which considered seasonal variations from Manuel A. Roxas District Hospital and population data from the local government. Complementary quantitative analyses, including Welch’s T-test, ANOVA, and Tukey’s HSD Test, were conducted to validate seasonal variability and parameter trends at the municipal level. The resulting predictions would inform and improve public health strategies for mitigating future dengue outbreaks. The study confirms that dengue cases are significantly higher during the wet season. Statistical analysis revealed significant variations in dengue spread across both years and seasons, with the two factors interacting in a meaningful way. The SEIRS model forecasts a stable pattern, driven by natural immunity, from 2026 to 2030, reflecting a model-based behavior of below 20 monthly cases. This forecast provides crucial, actionable insights for hospitals, clinics, and the Local Government Unit (LGU), enabling them to strategically plan and optimize resource allocation for timely and targeted public health actions.

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