Journal of vector borne diseases · 2026
Forecasting dengue incidence in Dakshina Kannada, Karnataka, India using time series analysis.
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Mohana Navya, Dmello Mackwin Kenwood, Kharvi Suresha, Dsouza Neevan
Аннотация
BACKGROUND OBJECTIVES: Dengue fever is a significant public health challenge in India. The 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 district of Karnataka, India 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. Data was collected in Microsoft 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 1836 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) 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.
METHODS: The study used retrospective data from January 1 2019 to April 30 2024, and covered 288 locations in the Dakshina Kannada. Data was collected in Microsoft 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 1836 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) 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.