| ORIGINAL ARTICLE |
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| Year : 2021 | Volume
: 14
| Issue : 10 | Page : 463-470 |
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Prediction of malaria cases in the southeastern Iran using climatic variables: An 18-year SARIMA time series analysis
Hamid Reza Tohidinik1, Hossein Keshavarz2, Mehdi Mohebali2, Mandana Sanjar3, Gholamreza Hassanpour2
1 HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
2 Center for Research of Endemic Parasites of Iran (CREPI); Department of Medical Parasitology & Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
3 Department of Medical Parasitology & Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Correspondence Address:
Gholamreza Hassanpour
Center for Research of Endemic Parasites of Iran (CREPI); Department of Medical Parasitology & Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran
Iran
Hossein Keshavarz
Center for Research of Endemic Parasites of Iran (CREPI); Department of Medical Parasitology & Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran
Iran
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/1995-7645.329008
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Objective: To predict future trends in the incidence of malaria cases in the southeast of Iran as the most important area of malaria using Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and to check the effect of meteorological variables on the disease incidence.
Methods: SARIMA method was applied to fit a model on malaria incidence from April 2001 to March 2018 in Sistan and Baluchistan province in southeastern Iran. Climatic variables such as temperature, rainfall, rainy days, humidity, sunny hours and wind speed were also included in the multivariable model as covariates. Then, the best fitted model was adopted to predict the number of malaria cases for the next 12 months.
Results: The best-fitted univariate model for the prediction of malaria in the southeast of Iran was SARIMA (1,0,0)(1,1,1)12 [Akaike Information Criterion (AIC)=307.4, validation root mean square error (RMSE)=0.43]. The occurrence of malaria in a given month was mostly related to the number of cases occurring in the previous 1 (p=1) and 12 (P=1) months. The inverse number of rainy days with 8-month lag (β=0.329 2) and temperature with 3-month lag (β=-0.002 6) were the best predictors that could improve the predictive performance of the univariate model. Finally, SARIMA (1,0,0)(1,1,1)12 including mean temperature with a 3-month lag (validation RMSE=0.414) was selected as the final multivariable model.
Conclusions: The number of malaria cases in a given month can be predicted by the number of cases in the prior 1 and 12 months. The number of rainy days with an 8-month lag and temperature with a 3-month lag can improve the predictive power of the model. |
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