Abstract
BACKGROUND AND OBJECTIVES: The present study aimed to examine the dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis (CL) in Biskra province, the largest focus of CL in Algeria, recording every year the highest incidence of CL in the country. The goal was to find the relationship between climate factors and CL incidence and identify the best model to estimate the variability among future CL cases. METHODS: We carried out a time series analysis based on the Box-Jenkins method to fit an autoregressive moving average (ARMA) model incorporating climate factors to the monthly recorded CL cases in Biskra province from 2000 to 2014. RESULTS: An ARMA (3,3) model incorporating temperature at a lag of 5 months and relative humidity was appropriate for forecasting the monthly data for CL between 2000 and 2009 in Biskra province. Temperature had a higher effect followed by relative humidity. The model was used for predicting monthly CL cases from January 2010 to December 2014; the predictions matched the recorded data. CONCLUSIONS: ARMA models produce reliable models for prediction of CL cases provided that climate variables are available. The models could assist public health services in preparing for the future. This is an optimistic finding for forecasting CL by means of a surveillance system using climate information.