N Md Taib, A Atil, R Dapari, M R Hassan, P D Kunasagran, S M I R M Zali, Z A Azhar, S S S A Rahim
{"title":"在沙巴州哥打京那巴鲁建立登革热时间序列预测模型。","authors":"N Md Taib, A Atil, R Dapari, M R Hassan, P D Kunasagran, S M I R M Zali, Z A Azhar, S S S A Rahim","doi":"","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Dengue is a major public health issue, with 3,900,000 people living in 129 dengue-endemic countries globally facing a risk of contracting dengue fever. Dengue incidence in Sabah is among the highest in Malaysia. In 2022, Kota Kinabalu District reported 22% of the total number of dengue cases in Sabah. The objective of this study was to develop a prediction model for dengue incidence using meteorological, entomological, and environmental parameters in Kota Kinabalu, Sabah.</p><p><strong>Materials and methods: </strong>An ecological study was conducted from 2016 to 2021 using the dengue database and meteorological data. The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. The model was fitted based on the reported weekly incidence of dengue from 2016 to 2020 and validated using data collected between January and December 2021.</p><p><strong>Results: </strong>SARIMA (1,1,1) (1,1,0)52 with the external regressor maximal temperature, Aedes index, and vacant lot were the models with minimal measurement errors, as indicated by the Mean Absolute Error (MAE) values of 3.04, Root Mean Squared Error (RMSE) of 4.43, and Akaike Information Criterion (AIC) of 1354.82.</p><p><strong>Conclusions: </strong>The predicted values in 2021 accurately forecasted the capability to serve as an early warning system for proactive dengue measures. This information is deemed valuable to healthcare administrators for enhancing the level of preparedness.</p>","PeriodicalId":39388,"journal":{"name":"Medical Journal of Malaysia","volume":"80 2","pages":"161-167"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Developing a time series prediction modelling for dengue in Kota Kinabalu, Sabah.\",\"authors\":\"N Md Taib, A Atil, R Dapari, M R Hassan, P D Kunasagran, S M I R M Zali, Z A Azhar, S S S A Rahim\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Dengue is a major public health issue, with 3,900,000 people living in 129 dengue-endemic countries globally facing a risk of contracting dengue fever. Dengue incidence in Sabah is among the highest in Malaysia. In 2022, Kota Kinabalu District reported 22% of the total number of dengue cases in Sabah. The objective of this study was to develop a prediction model for dengue incidence using meteorological, entomological, and environmental parameters in Kota Kinabalu, Sabah.</p><p><strong>Materials and methods: </strong>An ecological study was conducted from 2016 to 2021 using the dengue database and meteorological data. The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. The model was fitted based on the reported weekly incidence of dengue from 2016 to 2020 and validated using data collected between January and December 2021.</p><p><strong>Results: </strong>SARIMA (1,1,1) (1,1,0)52 with the external regressor maximal temperature, Aedes index, and vacant lot were the models with minimal measurement errors, as indicated by the Mean Absolute Error (MAE) values of 3.04, Root Mean Squared Error (RMSE) of 4.43, and Akaike Information Criterion (AIC) of 1354.82.</p><p><strong>Conclusions: </strong>The predicted values in 2021 accurately forecasted the capability to serve as an early warning system for proactive dengue measures. This information is deemed valuable to healthcare administrators for enhancing the level of preparedness.</p>\",\"PeriodicalId\":39388,\"journal\":{\"name\":\"Medical Journal of Malaysia\",\"volume\":\"80 2\",\"pages\":\"161-167\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical Journal of Malaysia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Journal of Malaysia","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
Developing a time series prediction modelling for dengue in Kota Kinabalu, Sabah.
Introduction: Dengue is a major public health issue, with 3,900,000 people living in 129 dengue-endemic countries globally facing a risk of contracting dengue fever. Dengue incidence in Sabah is among the highest in Malaysia. In 2022, Kota Kinabalu District reported 22% of the total number of dengue cases in Sabah. The objective of this study was to develop a prediction model for dengue incidence using meteorological, entomological, and environmental parameters in Kota Kinabalu, Sabah.
Materials and methods: An ecological study was conducted from 2016 to 2021 using the dengue database and meteorological data. The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. The model was fitted based on the reported weekly incidence of dengue from 2016 to 2020 and validated using data collected between January and December 2021.
Results: SARIMA (1,1,1) (1,1,0)52 with the external regressor maximal temperature, Aedes index, and vacant lot were the models with minimal measurement errors, as indicated by the Mean Absolute Error (MAE) values of 3.04, Root Mean Squared Error (RMSE) of 4.43, and Akaike Information Criterion (AIC) of 1354.82.
Conclusions: The predicted values in 2021 accurately forecasted the capability to serve as an early warning system for proactive dengue measures. This information is deemed valuable to healthcare administrators for enhancing the level of preparedness.
期刊介绍：
Published since 1890 this journal originated as the Journal of the Straits Medical Association. With the formation of the Malaysian Medical Association (MMA), the Journal became the official organ, supervised by an editorial board. Some of the early Hon. Editors were Mr. H.M. McGladdery (1960 - 1964), Dr. A.A. Sandosham (1965 - 1977), Prof. Paul C.Y. Chen (1977 - 1987). It is a scientific journal, published quarterly and can be found in medical libraries in many parts of the world. The Journal also enjoys the status of being listed in the Index Medicus, the internationally accepted reference index of medical journals. The editorial columns often reflect the Association''s views and attitudes towards medical problems in the country. The MJM aims to be a peer reviewed scientific journal of the highest quality. We want to ensure that whatever data is published is true and any opinion expressed important to medical science. We believe being Malaysian is our unique niche; our priority will be for scientific knowledge about diseases found in Malaysia and for the practice of medicine in Malaysia. The MJM will archive knowledge about the changing pattern of human diseases and our endeavours to overcome them. It will also document how medicine develops as a profession in the nation. We will communicate and co-operate with other scientific journals in Malaysia. We seek articles that are of educational value to doctors. We will consider all unsolicited articles submitted to the journal and will commission distinguished Malaysians to write relevant review articles. We want to help doctors make better decisions and be good at judging the value of scientific data. We want to help doctors write better, to be articulate and precise.
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