TY - JOUR
T1 - A SARIMA time series forecasting for dengue cases for reporting to Yangon Region, Myanmar
AU - Aung, Soe Htet
AU - Kyaw, Aye Mon Mon
AU - Phuanukoonnon, Suparat
AU - Jittamala, Podjanee
AU - Soonthornworasiri, Ngamphol
N1 - Publisher Copyright:
© 2024, Mahidol University - ASEAN Institute for Health Development. All rights reserved.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - SARIMA model
KW - Yangon Region
KW - dengue forecasting
KW - epidemiological surveillance
KW - outbreak prediction
UR - http://www.scopus.com/inward/record.url?scp=85186464505&partnerID=8YFLogxK
U2 - 10.55131/jphd/2024/220114
DO - 10.55131/jphd/2024/220114
M3 - Article
AN - SCOPUS:85186464505
SN - 2673-0774
VL - 22
SP - 184
EP - 196
JO - Journal of Public Health and Development
JF - Journal of Public Health and Development
IS - 1
ER -