A SARIMA time series forecasting for dengue cases for reporting to Yangon Region, Myanmar
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Issued Date
2024-01-01
Resource Type
ISSN
26730774
eISSN
26511258
Scopus ID
2-s2.0-85186464505
Journal Title
Journal of Public Health and Development
Volume
22
Issue
1
Start Page
184
End Page
196
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Public Health and Development Vol.22 No.1 (2024) , 184-196
Suggested Citation
Aung S.H., Kyaw A.M.M., Phuanukoonnon S., Jittamala P., Soonthornworasiri N. A SARIMA time series forecasting for dengue cases for reporting to Yangon Region, Myanmar. Journal of Public Health and Development Vol.22 No.1 (2024) , 184-196. 196. doi:10.55131/jphd/2024/220114 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/97524
Title
A SARIMA time series forecasting for dengue cases for reporting to Yangon Region, Myanmar
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Dengue fever is a significant public health challenge in Myanmar, which requires accurate monitoring to mitigate its impact. The study aimed to develop a forecasting model for dengue cases in Myanmar’s Yangon region using historical data from January 2002 to December 2022, with the objective of enhancing epidemiological surveillance and outbreak management. This retrospective observational study examines dengue cases in Yangon from January 2002 to December 2022, employing Seasonal Autoregressive Integrated Moving Average (SARIMA) models for predictive analysis. The most accurate model identified was SARIMA (2,0,1) (1,1,1) 12, with an AIC (Akaike Information Criterion) of 206.19 and MAPE (Mean Absolute Percentage Error) of 1.47%. According to the model, a peak in dengue cases was expected in July 2023, with an estimated 451 cases between January and December that year. Spatial variations in dengue incidence across Yangon’s townships emphasize the need for targeted interventions. While the SARIMA model is valuable, it would also be important to consider many other risk factors like climate, migration patterns, virus characteristics, and socioecological factors to improve forecasting accuracy. These findings can aid public health policymakers in preventing and managing dengue outbreaks in Myanmar. However, additional research is needed to incorporate additional risk factors into the model to comprehensively understand dengue epidemiology and improve forecasting accuracy.