A district-level spatio-temporal trends and forecasting study of dengue in India, 2010–2024
Varun Singh, Namita Trivedi, Amit Kumar Ukey, Rohini RuhilBackground and objectives
Dengue imposes a substantial and evolving public health burden in India, yet comprehensive district-level longitudinal analyses remain limited in integrating spatial clustering, typological classification, and forecasting within a unified framework. This study examined 15 y national and district-level trends, spatial clustering, and short-term projections of dengue across 794 districts in 28 States and 8 Union Territories of India, 2010–2024.
Methods
Annual district-level dengue case data (2010–2024) were obtained from the National Centre for Vector Borne Disease Control. Spatial autocorrelation was assessed using Global Moran’s I, and local clustering was identified using local indicators of spatial association (LISA). Principal component analysis (PCA) using mean annual burden, coefficient of variation and annual growth rate, followed by hierarchical clustering (Ward’s linkage) classified districts into four typological groups (k=4). Autoregressive integrated moving average (ARIMA) models were applied for national-level forecasting.
Results
Dengue cases in India increased from 21,312 in 2010 to 221,225 in 2024, peaking at 268,915 cases in 2023. During COVID-19 pandemic, dengue cases declined with an estimated surveillance deficit of approximately 146,539 cases in 2020 alone. The top 10 States accounted for ∼73.8% of cases, with West Bengal contributing 15.0%. Significant positive spatial autocorrelation was observed (Moran’s I = 0.200, P <0.001), and 18 persistent and 18 emerging hotspots identified. ARIMA (2,1,2) forecasts of ∼250,608 cases [95% prediction interval: 117,933–383,283] suggest a sustained elevated burden under current model assumption through 2025-29.
Interpretation and conclusions
Dengue in India increased during 2010–2024 and shows evidence of spatial structuring rather than random distribution. Districts group into four epidemiological profiles, ARIMA projections suggest sustained burden through 2025–2029, highlighting the need for geographically targeted, data-driven control strategies; however, forecasts should be interpreted as indicative given the limited validation window.