Ecological analysis of Cutaneous leishmaniasis incidence and climatic variability in Kashan and Aran - Bidgol, Iran (2014–2024)

Background

Cutaneous leishmaniasis (CL) is a major neglected tropical disease whose transmission is strongly influenced by climatic and environmental conditions. This study investigated the temporal associations between climatic variables and CL incidence in Kashan and Aran and Bidgol counties, central Iran, from 2014 to 2024.

Methods

Monthly CL surveillance data and meteorological variables were obtained from the regional health and meteorological organizations. Pearson correlation analysis was used to assess associations among climatic variables, while seasonal autoregressive integrated moving average models with exogenous variables (SARIMAX) were employed to evaluate the temporal relationships between climatic factors and monthly CL incidence.

Results

Annual rainfall was strongly positively correlated with maximum and minimum relative humidity (r = 0.80 and r = 0.75, respectively), negatively correlated with sunshine duration (r = −0.73), and strongly positively correlated with minimum and mean temperature (r = 0.99). CL incidence showed little correlation with annual rainfall (r = 0.01) and a weak negative correlation with minimum temperature (r = −0.23), whereas the long-term trend exhibited a moderate positive correlation with disease incidence (r = 0.38). The selected SARIMAX models yielded MA (1) coefficients ranging from 0.38 to 0.45 and AR (1) coefficients ranging from 0.58 to 0.62. In the fitted models, annual rainfall had a negative regression coefficient, whereas temperature, relative humidity, and sunshine duration had positive coefficients, indicating their associations with temporal variation in CL incidence.

Conclusion

Climatic variability, particularly temperature and humidity, was associated with temporal changes in CL incidence in Kashan and Aran and Bidgol counties. However, these ecological associations should not be interpreted as causal effects, as CL transmission is influenced by multiple factors, including vector ecology, reservoir dynamics, human behavior, and control interventions. SARIMAX modeling may provide a useful framework for characterizing temporal patterns and supporting surveillance planning in endemic areas.