Tehran University of Medical Sciences Journal of Arthropod-Borne Diseases 2322-1984 10 2 2016 05 05 222 237 EN Afshin Ostovar Epidemiology and Biostatistics Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Ali Akbar Haghdoost Research Center for Modeling in Health, Institute for Future Studies in Health, Kerman University of Medical Sciences, Kerman, Iran Abbas Rahimiforoushani Epidemiology and Biostatistics Department, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Ahmad Raeisi Malaria Control Office of MOH and ME, Tehran University of Medical Sciences, Tehran, Iran Reza Majdzadeh Knowledge Utilization Research Center, Tehran University of Medical Sciences, Tehran, Iran 2015 10 14 2015 10 14 Background: The Malaria Early Warning System is defined as the use of prognostic variables for predicting the occurrence of malaria epidemics several months in advance. The principal objective of this study was to provide a malaria prediction model by using meteorological variables and historical malaria morbidity data for malaria-en­demic areas in south eastern Iran. Methods: A total of 2002 locally transmitted microscopically confirmed malaria cases, which occurred in the Minab district of Hormozgan Province in Iran over a period of 6 years from March 2003 to March 2009, were analysed. Meteorological variables (the rainfall, temperature, and relative humidity in this district) were also assessed. Monthly and weekly autocorrelation functions, partial autocorrelation functions, and cross-correlation graphs were examined to explore the relationship between the historical morbidity data and meteorological variables and the number of cases of malaria. Having used univariate auto-regressive integrated moving average or transfer function models, significant predictors among the meteorological variables were selected to predict the number of monthly and weekly malaria cases. Ljung-Box statistics and stationary R-squared were used for model diagnosis and model fit, respec­tively. Results: The weekly model had a better fit (R2= 0.863) than the monthly model (R2= 0.424). However, the Ljung-Box statistic was significant for the weekly model. In addition to autocorrelations, meteorological variables were not significant, except for different orders of maximum and minimum temperatures in the monthly model. Conclusions: Time-series models can be used to predict malaria incidence with acceptable accuracy in a malaria early-warning system. The applicability of using routine meteorological data in statistical models is seriously limited. https://jad.tums.ac.ir/index.php/jad/article/view/153 https://jad.tums.ac.ir/index.php/jad/article/download/153/138