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Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis
Author(s) -
Tohidinik Hamid Reza,
Mohebali Mehdi,
Mansournia Mohammad Ali,
Niakan Kalhori Sharareh R.,
AliAkbarpour Mohsen,
Yazdani Kamran
Publication year - 2018
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/tmi.13079
Subject(s) - incidence (geometry) , southern iran , relative humidity , climatology , statistics , mathematics , geography , meteorology , geology , art , geometry , literature
Objectives 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. Methods 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. Results 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). Conclusions 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.