Spatial modelling to identify high-risk zones for the transmission of cutaneous leishmaniasis in hyperendemic urban environments: A case study of Mashhad, Iran

Abstract

Spatial modelling was employed to identify high-risk zones for the transmission of cutaneous leishmaniasis in hyperendemic urban environments, focusing on Mashhad, Iran. Data analysis from 3033 CL patients (2016–2020) integrated socio-demographic, environmental, and geological factors using negative binomial regression and the technique for order of preference by similarity to ideal solution (TOPSIS) model. Findings indicate that 42.8% of the study area, affecting 20% of Mashhad's population, is at heightened risk due to factors such as high illiteracy rates, dense populations, poor built environment quality, and specific geological conditions. The model achieved an area under the curve (AUC) of 0.83, signifying strong discrimination, with Kappa statistics (KNO = 0.60, K standard = 0.56) showing substantial agreement. These insights can be used to inform targeted surveillance and effective disease control strategies.