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Objective: To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average(SARIMA) model,Methods: The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model,Besides,information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016,Moreover,various SARIMA models were assessed and the best one was selected,Then,the model’s fitness was evaluated based on normality of the residuals’ distribution,correspondence between the fitted and real amounts,and calculation of Akaike Information Criteria(AIC) and Bayesian Information Criteria(BIC),Results: The study results indicated that SARIMA model(4,1,4)(0,1,0)(12) in general and SARIMA model(4,1,4)(0,1,1)(12) in below and above 15 years age groups could appropriately predict the disease trend in the study area,Moreover,temperature with a three-month delay(lag3) increased the disease trend,rainfall with a four-month delay(lag4) decreased the disease trend,and rainfall with a nine-month delay(lag9) increased the disease trend,Conclusions: Based on the results,leishmaniasis follows a descending trend in the study area in case drought condition continues,SARIMA models can suitably measure the disease trend,and the disease follows a seasonal trend.
Objective: To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in the south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Methods: The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model, Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016, Moreover, various SARIMA models were assessed and the best one was chosen, Then, the model’s fitness was evaluated on normality of the residuals’ distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC), Results: The study results indicated that SARIMA model (4,1,4) , 1,0) (12) in general and SARIMA model (4,1,4) (0,1,1) (12) in below and above 15 years age groups could suitably predict the disease trend in the study area, Moreover, temperature with a four-month delay (lag4) increased the disease trend, and rainfall with a nine-month delay (lag3) increased the disease trend, Conclusions: Based on the results, leishmaniasis follows a descending trend in the study area in case drought condition continues, SARIMA models can suitably measure the disease trend, and the disease follows a seasonal trend.