Background: The increasing number of patients suffering from cutaneous leishmaniasis in Poldokhtar County during the last 10 years and technological advances in data generation has increased the necessity to produce the predicting models of disease prevalence in the region. Therefore, climatic variables were used in this study to predict the cutaneous leishmaniasis.
Materials and Methods: In this study using regression model, the relationship between number of patients with cutaneous leishmaniasis and climatic signals were taken simultaneously and with one, two, three and four months of regression lag.
Results: Totally there was a significant relationship between January patients with January NINO1 climatic signal, March patients with February PNA climatic signal, April patients with March AAMM climatic signal, May patients with April AO climatic signal, and August patients with June TSA climatic signal, at 5% significance level. Furthermore, there was a significant relationship between February patients with January NINO1 climatic signal, at 10% significance level.
Conclusion: This investigation showed that use of climatic signals with lags for predicting the disease was better than simultaneous application of signals and disease. Correlation between statistics relating to diseases during the entire period and PDO signal with 2 months of lag was obtained as 84.53. In addition, results indicated that approximately during half of the months in a year, there was a good correlation between prevalence of cutaneous leishmaniasis and the climatic signals and thus enabling us to discover prevalence of cutaneous leishmaniasis using the climatic signals.