ArticleGeoHealth2025
Remote Sensing and GIS-Based Study to Predict Risk Zones for Mosquito-Borne Diseases in Cuttack District, Odisha, India.
Article in GeoHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- An integrated GIS and remote-sensing framework for district-level dengue risk assessment in northern India: development of a spatial Dengue Risk Index and assessment of its spatial concordance with reported incidence.Tropical medicine and health · 2026Article
- Mapping malaria risk using geospatial and AHP approaches in Nsanje District, Malawi.Epidemiology and infection · 2026Article
- Regression Analysis of the Main Environmental Risk Factors for Mountainous Subtype of Zoonotic VL in China.Journal of tropical medicine · 2026Article
- Dengue Fever Resurgence in Iran: An Integrative Review of Causative Factors and Control Strategies.Tropical medicine and infectious disease · 2025Review
- Remote Sensing and GIS-Based Study to Predict Risk Zones for Mosquito-Borne Diseases in Cuttack District, Odisha, India.GeoHealth · 2025Article
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5 authors.
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Abstract
Tropical and sub-tropical regions mostly provide favorable conditions for the spread of vector-borne diseases especially those transmitted by mosquito vectors. Viral disease outbreaks such as dengue, chikungunya, Japanese encephalitis; and parasitic diseases such as malaria, and filariasis; are common health problems caused by mosquitos. According to the World Health Organization (WHO), globally around 40% of people are at high risk of mosquito-borne diseases (MBD) (WHO Fact-sheets, 2020, https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases). In the present study, Remote Sensing methods integrated with Geographic Information System (GIS) have been used to predict the risk zones for MBD in Cuttack, a district of Odisha, the eastern state of India. The findings of this study could be utilized to develop and implement MBD control and prevention strategies in identified high-risk areas. Under this study, the Landsat-8 multispectral temporal images from 2018 to 2021 were used to identify and demarcate the water-logged areas and sites favorable for mosquito breeding. The goal is to identify risk zones for MBD using different indices such as Normalized Difference Water Index, Normalized Difference Moisture Index, Normalized Difference Vegetation Index, and Land Surface Temperature by evaluating water, moisture, vegetation, and temperature parameters. Applying Arc GIS software analysis models,we found 11,730.07 Ha. as a high-risk zone, 28,053.99 Ha. as a medium-risk zone, and 12,669.69 Ha. as a low-risk zone. This study has the potential to enable informed decision-making and proactive mosquito-borne disease prevention.
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