Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method

ABSTRACT

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)