{"title":"建立单变量时间序列模型用于预测那空四淡府的登革出血热病例","authors":"Siriluk Sintupachee, Onuma Ruksachol, Manit Pollar, Suppawan Promprao","doi":"10.37268/mjphm/vol.23/no.2/art.1911","DOIUrl":null,"url":null,"abstract":"Dengue Hemorrhagic Fever (DHF) is a prominent cause of hospitalization and death in Thailand, especially in the south. Epidemiological modeling was used to estimate the trend of outbreak tendencies based on epidemic data. The goal of this work was to create a realistic model for estimating DHF occurrences using monthly data from the department of disease control, ministry of public health, Thailand from 2010 to 2019. SARIMA model with the Box-Jenkins approach was conducted to forecast dengue incidence using the previous data. Bayesian Information Criteria (BIC), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to determine their accuracy. The result shows that the SARIMA(2,0,1)(1,0,0)12 model fits the Nakhon Si Thammarat pandemic data the best. Their accuracy had the smallest BIC, MAPE, and RMSE yielding 9.64, 848.743, and 214.661, respectively. The DHF ARIMA model is necessary and may be used to forecast the incidence of DHF in other locations as well as help in the development of public health initiatives to prevent and treat the condition.","PeriodicalId":38537,"journal":{"name":"Malaysian Journal of Public Health Medicine","volume":"55 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DEVELOPMENT OF THE UNIVARIATE TIME SERIES MODEL FOR FORECASTING DENGUE HEMORRHAGIC FEVER CASES IN NAKHON SI THAMMARAT\",\"authors\":\"Siriluk Sintupachee, Onuma Ruksachol, Manit Pollar, Suppawan Promprao\",\"doi\":\"10.37268/mjphm/vol.23/no.2/art.1911\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Dengue Hemorrhagic Fever (DHF) is a prominent cause of hospitalization and death in Thailand, especially in the south. Epidemiological modeling was used to estimate the trend of outbreak tendencies based on epidemic data. The goal of this work was to create a realistic model for estimating DHF occurrences using monthly data from the department of disease control, ministry of public health, Thailand from 2010 to 2019. SARIMA model with the Box-Jenkins approach was conducted to forecast dengue incidence using the previous data. Bayesian Information Criteria (BIC), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to determine their accuracy. The result shows that the SARIMA(2,0,1)(1,0,0)12 model fits the Nakhon Si Thammarat pandemic data the best. Their accuracy had the smallest BIC, MAPE, and RMSE yielding 9.64, 848.743, and 214.661, respectively. The DHF ARIMA model is necessary and may be used to forecast the incidence of DHF in other locations as well as help in the development of public health initiatives to prevent and treat the condition.\",\"PeriodicalId\":38537,\"journal\":{\"name\":\"Malaysian Journal of Public Health Medicine\",\"volume\":\"55 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Malaysian Journal of Public Health Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37268/mjphm/vol.23/no.2/art.1911\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Malaysian Journal of Public Health Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37268/mjphm/vol.23/no.2/art.1911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DEVELOPMENT OF THE UNIVARIATE TIME SERIES MODEL FOR FORECASTING DENGUE HEMORRHAGIC FEVER CASES IN NAKHON SI THAMMARAT
Dengue Hemorrhagic Fever (DHF) is a prominent cause of hospitalization and death in Thailand, especially in the south. Epidemiological modeling was used to estimate the trend of outbreak tendencies based on epidemic data. The goal of this work was to create a realistic model for estimating DHF occurrences using monthly data from the department of disease control, ministry of public health, Thailand from 2010 to 2019. SARIMA model with the Box-Jenkins approach was conducted to forecast dengue incidence using the previous data. Bayesian Information Criteria (BIC), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to determine their accuracy. The result shows that the SARIMA(2,0,1)(1,0,0)12 model fits the Nakhon Si Thammarat pandemic data the best. Their accuracy had the smallest BIC, MAPE, and RMSE yielding 9.64, 848.743, and 214.661, respectively. The DHF ARIMA model is necessary and may be used to forecast the incidence of DHF in other locations as well as help in the development of public health initiatives to prevent and treat the condition.
期刊介绍:
Malaysian Journal of Public Health Medicine (MJPHM) is the official Journal of Malaysian Public Health Physicians’ Association. This is an Open-Access and peer-reviewed Journal founded in 2001 with the main objective of providing a platform for publication of scientific articles in the areas of public health medicine. . The Journal is published in two volumes per year. Contributors are welcome to send their articles in all sub-discipline of public health including epidemiology, biostatistics, nutrition, family health, infectious diseases, health services research, gerontology, child health, adolescent health, behavioral medicine, rural health, chronic diseases, health promotion, public health policy and management, health economics, occupational health and environmental health.