Malaria continues to pose a significant public health challenge in India, particularly in the North East Region (NER), which presents a multifaceted epidemiological landscape. Malaria control and prevention programs demonstrate greater efficiency and cost-effectiveness when they target hotspot regions. This study is aimed to explore spatiotemporal clusters of malaria incidence at the district level across NER of India. Reported malaria case data from 2011 to 2019 and advanced geospatial analytical techniques were used for the analysis. The Local Moran's I statistic was employed to conduct cluster and outlier analysis of malaria. The presence of local clustering was examined using Getis-Ord Gi* statistics to determine the intensity of hotspots and coldspots at the district level. Further, Self-Organizing Maps (SOM), an artificial neural network was employed to identify the malaria disease clusters and the recorded clusters were exported to the Geographic Information System (GIS) environment to generate the malaria hotspot cluster map. The overall malaria incidence decreased consistently from 2011 to 2019. The results revealed the presence of significant and persistent malaria hotspots, predominantly concentrated in the Meghalaya, Mizoram, and Tripura states. These findings underscore the critical need for targeted malaria control interventions in these areas. By pinpointing the hotspot regions, this study provides a valuable framework for public health officials to deploy resources more effectively, aiming to mitigate the malaria burden and move towards elimination goals.

 

Source: https://doi.org/10.1016/j.actatropica.2025.107783