Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Department of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Trop Med Int Health. 2018 Aug;23(8):860-869. doi: 10.1111/tmi.13079. Epub 2018 Jun 11.
To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model.
The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence.
SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (β = -0.02), rainy days at a lag of 2 months (β = -0.09) and relative humidity at a lag of 8 months (β = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26).
Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision.
利用季节性自回归综合移动平均（SARIMA）模型预测伊朗法尔斯省东部地区的动物源性皮肤利什曼病（ZCL）的发生情况，并评估气候变量对疾病发病率的影响。
采用 Box-Jenkins 方法拟合 2004 年至 2015 年 ZCL 发病率的 SARIMA 模型。然后，该模型用于预测 2016 年 ZCL 病例数。最后，我们评估了气象变量（降雨量、降雨日数、温度、日照时数和相对湿度）与 ZCL 发病率的关系。
SARIMA(2,0,0)(2,1,0)12 是预测法尔斯省东部地区 ZCL 发病率的首选模型（验证均方根误差，RMSE=0.27）。结果表明，一个月内的 ZCL 发病率可以通过前 1 个月和前 2 个月的病例数，以及前 12 个月和前 24 个月的病例数来估计。将滞后 2 个月的降雨量（β=-0.02）、滞后 2 个月的降雨日数（β=-0.09）和滞后 8 个月的相对湿度（β=0.13）作为外部回归量纳入 SARIMA 模型可以提高预测模型的预测能力（P 值均＜0.05）。后者是预测 ZCL 病例的最佳气候变量（验证 RMSE=0.26）。
时间序列模型可作为预测伊朗法尔斯省 ZCL 趋势的有用工具，从而可用于公共卫生规划。将气象变量引入模型中可能会提高模型的精度。
