Ecological Study Links Climate Variability to Leishmaniasis Incidence in Central Iran

An ecological study of monthly cutaneous leishmaniasis surveillance data from Kashan and Aran and Bidgol counties in central Iran found that climatic variability was associated with changes in disease incidence between 2014 and 2024. Researchers used temperature, rainfall, relative humidity and sunshine-duration data alongside seasonal time-series models. The findings describe temporal associations rather than causal relationships, and the authors caution that transmission is influenced by several biological, environmental and social factors.

The study, reported in BMC Infectious Diseases, obtained monthly cutaneous leishmaniasis surveillance data from regional health organizations and meteorological variables from regional meteorological organizations. Investigators used Pearson correlation analysis to examine relationships among climatic variables and disease incidence. They then applied seasonal autoregressive integrated moving average models with exogenous variables, or SARIMAX, to evaluate temporal relationships between monthly incidence and climatic factors.

Rainfall was strongly associated with other climatic measures over the study period. Annual rainfall was positively correlated with maximum relative humidity (r = 0.80) and minimum relative humidity (r = 0.75), negatively correlated with sunshine duration (r = −0.73), and very strongly positively correlated with minimum and mean temperature (r = 0.99). Cutaneous leishmaniasis incidence itself had little correlation with annual rainfall (r = 0.01) and a weak negative correlation with minimum temperature (r = −0.23). The long-term trend in incidence showed a moderate positive correlation with disease occurrence (r = 0.38).

The selected SARIMAX models included moving-average coefficients ranging from 0.38 to 0.45 and autoregressive coefficients ranging from 0.58 to 0.62. In the fitted models, annual rainfall had a negative regression coefficient, while temperature, relative humidity and sunshine duration had positive coefficients. These coefficients indicated associations with temporal variation in cutaneous leishmaniasis incidence, with the models linking climatic factors to the observed time patterns rather than demonstrating that any single variable directly produces disease changes. The contrast between the minimal direct correlation of rainfall with incidence and its negative model coefficient also shows that simple pairwise association and time-series modeling captured different aspects of the data.

The authors emphasize that the ecological associations cannot be interpreted as proof that climate directly causes changes in cutaneous leishmaniasis. Transmission may also depend on vector ecology, reservoir dynamics, human behavior and disease-control interventions. They present SARIMAX as a possible framework for characterizing temporal patterns and supporting surveillance planning in endemic areas. The study’s scope was limited to monthly surveillance and meteorological data from Kashan and Aran and Bidgol counties during 2014–2024, so its conclusions concern relationships observed in that region and period. The analysis was ecological rather than a test of individual-level risk, and it did not evaluate the effects of a clinical intervention.

Subject of Research: Ecological associations between climate variability and cutaneous leishmaniasis incidence in central Iran

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

Article References: Delavari, M., Moradi, A., Niknejad, H., Arghavani, A., Modabber, A. H., & Arani, M. H. (2026). Ecological analysis of Cutaneous leishmaniasis incidence and climatic variability in Kashan and Aran – Bidgol, Iran (2014–2024). BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14311-5

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14311-5

Keywords: cutaneous leishmaniasis, climate variability, ecological analysis, Iran, SARIMAX, surveillance, temperature, humidity, rainfall, time-series modeling, Ecological, analysis