{"title":"利用时间序列分析预测2024-2026年卡纳塔克邦Dakshina Kannada地区登革热发病率。","authors":"Navya Mohana, Mackwin Kenwood Dmello, Suresha Kharvi, Neevan Dsouza","doi":"10.4103/jvbd.jvbd_29_25","DOIUrl":null,"url":null,"abstract":"<p><strong>Background objectives: </strong>Dengue fever is a significant public health challenge in India. This threat has been amplified by rapid urbanization. This study analyzes the spatiotemporal patterns of dengue transmission, the influence of climate on dengue transmission, and predict future trends of dengue incidence in Dakshina Kannada from 2024 to 2026.</p><p><strong>Methods: </strong>The study used retrospective data from January 1, 2019, to April 30, 2024, and covered 288 locations in the Dakshina Kannada district of Karnataka. Data was collected in Excel and analyzed using Jamovi 2.3.28 for descriptive statistics. Time series analysis was performed in R version 4.4.0, while spatiotemporal clusters were identified using SaTScan V10.1.2 and visualized in QGIS version 3.30.0. Multivariable linear regression was conducted to identify climate factors affecting dengue cases. ARIMA models were employed for predictive forecasting of future dengue cases.</p><p><strong>Results: </strong>A total of 1,836 recorded dengue cases was retrieved from the Health Management Information System (HMIS) at the district level. The study identified significant spatiotemporal clusters of dengue cases, with the primary cluster occurring from May 1, 2022, to April 30, 2024. Climatic factors, particularly rainfall and temperature, showed significant correlations with dengue incidence. The ARIMA (3,1,1) (1,0,0) [12] model demonstrated robust forecasting capability for dengue cases, indicating a continuing upward trend, which appears to be influenced by seasonal patterns.</p><p><strong>Interpretation conclusion: </strong>Dengue transmission in Dakshina Kannada is significantly influenced by climatic factors such as temperature, rainfall, and humidity. The ARIMA-based predictive modeling forecasted increased dengue cases in the coming years. These findings show the need for targeted public health interventions in identified hotspot areas, along with continuous climate-based surveillance to support timely and effective dengue control measures.</p>","PeriodicalId":17660,"journal":{"name":"Journal of Vector Borne Diseases","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Forecasting dengue incidence in Dakshina Kannada, Karnataka (2024-2026) using Time Series Analysis.\",\"authors\":\"Navya Mohana, Mackwin Kenwood Dmello, Suresha Kharvi, Neevan Dsouza\",\"doi\":\"10.4103/jvbd.jvbd_29_25\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background objectives: </strong>Dengue fever is a significant public health challenge in India. This threat has been amplified by rapid urbanization. This study analyzes the spatiotemporal patterns of dengue transmission, the influence of climate on dengue transmission, and predict future trends of dengue incidence in Dakshina Kannada from 2024 to 2026.</p><p><strong>Methods: </strong>The study used retrospective data from January 1, 2019, to April 30, 2024, and covered 288 locations in the Dakshina Kannada district of Karnataka. Data was collected in Excel and analyzed using Jamovi 2.3.28 for descriptive statistics. Time series analysis was performed in R version 4.4.0, while spatiotemporal clusters were identified using SaTScan V10.1.2 and visualized in QGIS version 3.30.0. Multivariable linear regression was conducted to identify climate factors affecting dengue cases. ARIMA models were employed for predictive forecasting of future dengue cases.</p><p><strong>Results: </strong>A total of 1,836 recorded dengue cases was retrieved from the Health Management Information System (HMIS) at the district level. The study identified significant spatiotemporal clusters of dengue cases, with the primary cluster occurring from May 1, 2022, to April 30, 2024. Climatic factors, particularly rainfall and temperature, showed significant correlations with dengue incidence. The ARIMA (3,1,1) (1,0,0) [12] model demonstrated robust forecasting capability for dengue cases, indicating a continuing upward trend, which appears to be influenced by seasonal patterns.</p><p><strong>Interpretation conclusion: </strong>Dengue transmission in Dakshina Kannada is significantly influenced by climatic factors such as temperature, rainfall, and humidity. The ARIMA-based predictive modeling forecasted increased dengue cases in the coming years. These findings show the need for targeted public health interventions in identified hotspot areas, along with continuous climate-based surveillance to support timely and effective dengue control measures.</p>\",\"PeriodicalId\":17660,\"journal\":{\"name\":\"Journal of Vector Borne Diseases\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Vector Borne Diseases\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.4103/jvbd.jvbd_29_25\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Vector Borne Diseases","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.4103/jvbd.jvbd_29_25","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}
Forecasting dengue incidence in Dakshina Kannada, Karnataka (2024-2026) using Time Series Analysis.
Background objectives: Dengue fever is a significant public health challenge in India. This threat has been amplified by rapid urbanization. This study analyzes the spatiotemporal patterns of dengue transmission, the influence of climate on dengue transmission, and predict future trends of dengue incidence in Dakshina Kannada from 2024 to 2026.
Methods: The study used retrospective data from January 1, 2019, to April 30, 2024, and covered 288 locations in the Dakshina Kannada district of Karnataka. Data was collected in Excel and analyzed using Jamovi 2.3.28 for descriptive statistics. Time series analysis was performed in R version 4.4.0, while spatiotemporal clusters were identified using SaTScan V10.1.2 and visualized in QGIS version 3.30.0. Multivariable linear regression was conducted to identify climate factors affecting dengue cases. ARIMA models were employed for predictive forecasting of future dengue cases.
Results: A total of 1,836 recorded dengue cases was retrieved from the Health Management Information System (HMIS) at the district level. The study identified significant spatiotemporal clusters of dengue cases, with the primary cluster occurring from May 1, 2022, to April 30, 2024. Climatic factors, particularly rainfall and temperature, showed significant correlations with dengue incidence. The ARIMA (3,1,1) (1,0,0) [12] model demonstrated robust forecasting capability for dengue cases, indicating a continuing upward trend, which appears to be influenced by seasonal patterns.
Interpretation conclusion: Dengue transmission in Dakshina Kannada is significantly influenced by climatic factors such as temperature, rainfall, and humidity. The ARIMA-based predictive modeling forecasted increased dengue cases in the coming years. These findings show the need for targeted public health interventions in identified hotspot areas, along with continuous climate-based surveillance to support timely and effective dengue control measures.
期刊介绍：
National Institute of Malaria Research on behalf of Indian Council of Medical Research (ICMR) publishes the Journal of Vector Borne Diseases. This Journal was earlier published as the Indian Journal of Malariology, a peer reviewed and open access biomedical journal in the field of vector borne diseases. The Journal publishes review articles, original research articles, short research communications, case reports of prime importance, letters to the editor in the field of vector borne diseases and their control.
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