ArticleGlobal health action2025
Spatial epidemiology of dengue and chikungunya in Karnataka using GIS-based analysis.
Article in Global health action, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neutrophil-to-lymphocyte ratio for the clinical management of dengue: a systematic review and meta-analysis.Frontiers in epidemiology · 2026Pooled it
- Detection of novelFrontiers in microbiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDengue and chikungunya are major vector-borne diseases with significant public health concerns. Understanding their spatial distribution is crucial for effective disease control and prevention strategies.
objectiveTo analyse the spatial patterns and trends of dengue and chikungunya in Karnataka from 2021 to 2024 using GIS-based methods to identify high-risk districts.
methodsSpatial autocorrelation analysis using Moran's I and cluster analysis was performed to examine the spatial distribution of dengue and chikungunya incidence rates using GIS-based mapping.
resultsA total of 38,229 dengue cases and 8,094 chikungunya cases were recorded during this period, with the highest incidence rates varying across districts. The highest dengue IR in 2024 was recorded in Chikkamagaluru (0.58/1000), Mandya (0.47/1,000), followed by Udupi, while Vijayapura reported the highest chikungunya IR in 2022 (0.17/1,000). Moran's I result revealed moderate positive autocorrelation for dengue in 2021 and 2023, indicating significant clustering of cases. By comparison, chikungunya exhibited negative spatial autocorrelation in 2023 and a shift to positive in 2024, reflecting a transition from dispersed to more clustered patterns.
conclusionGIS-based surveillance enhances early outbreak detection, resource allocation, and targeted vector control. Spatial autocorrelation techniques support predictive modelling for other infectious diseases. Strengthening interdisciplinary collaboration, community engagement, and spatial analytics in public health policies supports disease mitigation and urban planning. Globally, integrating spatial epidemiology into surveillance systems can improve outbreak prediction, optimise resources, and guide data-driven policy. These findings highlight the need for GIS-based frameworks and international cooperation to address emerging disease threats effectively.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.