Prediction of Trends and Bioclimatic Factors Influencing the Monthly Incidence of Zoonotic Cutaneous Leishmaniasis Using Arima and Sarima Time Series Models in Maraveh Tappeh County, Golestan Province, Iran

Abstract

Background: Zoonotic cutaneous leishmaniasis (ZCL) is a significant vector-borne disease in northeastern Iran, strong­ly affected by climatic conditions. Maraveh Tappeh County in Golestan Province is an endemic area with considerable annual case numbers. This study aimed to predict monthly ZCL trends and identify key bioclimatic factors influencing disease occurrence using ARIMA and SARIMA time series models.
Methods: This analytical cross-sectional study used monthly confirmed ZCL case data from 2003 to 2018, obtained from the Maraveh Tappeh County Health Center. Climatic variables, including temperature indices, relative humidity indices, total monthly precipitation and number of rainy days, were collected from the local meteorological office. Sta­tionarity was assessed using the Augmented Dickey–Fuller test and autocorrelation patterns were evaluated through ACF and PACF plots. ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal AutoRegressive Integrated Moving Average) models were developed, with the optimal model selected based on AIC and BIC criteria. Cross-correlation analysis examined associations between climatic variables and ZCL incidence at lags of 0–5 months.
Results: A total of 1,301 ZCL cases were reported over the 16 years, with marked monthly and seasonal variability. Incidence peaked in November and reached its lowest level in June. The ARIMA (2,0,2)–SARIMA (0,0,1)₁₂ model demonstrated the best predictive performance. Significant positive correlations were observed between ZCL incidence and relative humidity, precipitation and number of rainy days at short lags (0–2 months), while inverse associations appeared at longer lags (5 months) (p<0.05).
Conclusion: Relative humidity and precipitation are key drivers of ZCL dynamics in Maraveh Tappeh. Incorporating SARIMA models into surveillance systems may improve outbreak prediction and support timely prevention and control strategies.