Assessing flood-induced malaria risk using integrated geospatial modelling and epidemiological indicators in Kanyakumari District, India

Floods are a recurring hazard in tropical coastal regions and can indirectly increase the risk of vector-borne diseases such as malaria by altering hydrological and environmental conditions. This study develops an integrated framework to assess flood-induced malaria vulnerability in Kanyakumari District, Tamil Nadu, India, by combining flood susceptibility mapping, malaria vulnerability analysis, and multi-year epidemiological data.

Flood susceptibility was mapped using a Fuzzy Analytic Hierarchy Process-based approach incorporating topographic, hydrological, and land-use factors. Malaria vulnerability zones were delineated using a multi-criteria framework integrating environmental, exposure, and accessibility variables. Model outputs were validated using Receiver Operating Characteristic curve analysis with independent flood inventory and malaria case datasets. Approximately 28% of the district falls within High to Very High flood susceptibility zones, primarily along river corridors, floodplains, and the southern coastal region. Malaria vulnerability is highly localized, with High and Very High vulnerability zones covering about 2% of the area but showing strong spatial overlap with flood-prone lowlands. Malaria cases (2020–2024) are largely concentrated within Moderate to Very High vulnerability zones, indicating a strong spatial association between flood exposure and disease occurrence. The models demonstrated high predictive performance, with area under the curve values of 0.882 for flood susceptibility and 0.812 for malaria vulnerability.

The findings indicate a transition from widespread malaria occurrence to spatially concentrated, flood-linked hotspots in Kanyakumari District. Integrating flood hazard and disease vulnerability assessments can support targeted surveillance, post-flood response planning, and climate-resilient public health interventions. The proposed framework offers a useful approach for improving disaster risk reduction and health system preparedness in coastal regions.