Abstract :

Dengue was recognized by the World Health Organization (WHO) in 2023 as a prominent arthropod-borne viral illness and poses a significant global health threat. This study focuses on Region XI in the Philippines, where dengue is alarmingly prevalent, and in forecasting dengue cases, it employs the ARIMA model. The study, spanning from January 2019 to August 2023, integrates univariate exploratory data analysis and statistical methods to understand the patterns and trends of dengue cases. The ARIMA model, specifically (0,0,1) with a nonzero mean, the candidate model, was identified and utilized from RStudio through coefficient tests of AIC values. The forecasting results predict a decline in dengue cases over the next 16 months. The normality test of residuals and Q-Q plot affirm the model’s reliability. A significant test using the two-sample t-test demonstrates a substantial difference between actual and forecasted values. In conclusion, this study provides crucial insights for public health planning, community intervention, and future research. The ARIMA model’s successful application emphasizes the need for refined dengue control and mitigation strategies in Region XI. The results underscore the urgency of collective efforts to minimize dengue transmission and address the challenges posed by this prevalent and impactful disease.

Keywords :

References :

1. Abualamah, W. A., Akbar, N. A., Banni, H. S., & Bafail, M. A. (2021). Forecasting the morbidity and mortality of dengue fever in KSA: A time series analysis (2006–2016). Journal of Taibah University Medical Sciences, 16, 448–455.
2. Daele, S. V., & Janssenswillen, G. (2023). Identifying the Steps in an Exploratory Data Analysis: A Process-Oriented Approach. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops. ICPM 2022. Lecture Notes in Business Information Processing, 468. Springer, Cham. https://doi.org/10.1007/978-3-031-27815-0_38
3. Department of Health. (2020). DOH Reports Steady Decline in Dengue Cases. Retrieved on 31 Jul 2021 from https://doh.gov.ph/doh-press-release/doh-reportssteady-decline-in-dengue-cases.
4. Dagoumas, A. (2021). Mathematical Modelling of Contemporary Electricity Markets. Elsevier Academic Press. https://www.everand.com/book/492741322/Mathematical-Modelling-of-Contemporary-Electricity-Markets

5. Dhamodharavadhani, S., & Rathipriya, R. (2020). Forecasting Dengue Incidence Rate in Tamil Nadu Using ARIMA Time Series Model. In Machine Learning for Healthcare (1st ed., pp. 16). Chapman and Hall/CRC. https://doi.org/10.1201/9780429330131

6. Montemayor, M. T (2022, July 6). DOH Logs 51,622 Dengue Cases, 58% in 2021. Govt. Philippines on July 4, 2022. Retrieved from https://reliefweb.int/report/philippines/doh-logs-51622-dengue-cases-58-2021
7. Ong, E. P., Obeles, A. J. T., Ong, B. A. G., & Tantengco, O. A. G. (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

8. Philippine Statistics Authority. (2021). Highlights of the Philippine Population 2020 Census of Population and Housing (2020 CPH). Retrieved on 16 Sep 2021 from https://psa.gov.ph/content/highlights-philippine-population-2020-census-population-and-housing-2020-cph

9. Reich, Y., & Subrahmanian, E. (2022). Documenting design research by structured multilevel analysis: Supporting the diversity of the design research community of practice. Design Science, 8, E3. doi:10.1017/dsj.2021.28
10. Singh, S., Sundram, B. M., Rajendran, K., Law, K. B., Aris, T., Ibrahim, H., … Gil, B. S. (2020). Forecasting monthly confirmed dengue cases in Malaysia using ARIMA models. Journal of Infection in Developing Countries, 14(9), 971-976.
11. Soto-Ramírez, N., Odeku, J., Foxe, C., et al. (2020). Applying time series modeling to assess the dynamics and forecast monthly reports of abuse, neglect, and/or exploitation involving a vulnerable adult. Arch Public Health, 78, 53. https://doi.org/10.1186/s13690-020-00431-0
12. Stolerman, L. M., Maia, P. D., & Kutz, J. N. (2019). Forecasting dengue fever in Brazil: An assessment of climate conditions. PLOS ONE, 14, e0220106. https://doi.org/10.1371/journal.pone.0220106
13. Sutriyawan, A., Herdianti, H., Cakranegara, P. A., Lolan, Y. P., & Sinaga, Y. (2022). Predictive Index Using Receiver Operating Characteristic and Trend Analysis of Dengue Hemorrhagic Fever Incidence. Open Access Macedonian Journal of Medical Sciences, 10, 681–687.
14. Thiruchelvam, L., Dass, S. C., Asirvadam, V. S., et al. (2021). Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models. Scientific Reports, 11, 5873. https://doi.org/10.1038/s41598-021-84176-y
15. World Health Organization. (2023, March 17). Dengue and severe dengue. https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue