Abstract

Background: Meningitis is one of the most disturbing infectious diseases due to mortality, morbidity and its ability to cause epidemic. Objectives: The current study aimed to detect and remove explainable patterns of fever and neurological symptoms as suspected meningitis occurred in Hamadan province, West of Iran. Materials and Methods: Monthly and daily data of suspected cases of meningitis of Iranian national surveillance system from 21st March 2010 to 20th March 2013 were used. explainable patterns of syndrome were identified using autocorrelation and partial autocorrelation functions, mean differences and nonparametric Mann-Kendall statistics. Besides moving average (MA) smoothing methods, Holt-Winters (HW) exponential smoothing and the Poisson regression model were used to remove such patterns. Results: The study findings indicated the presence of explainable patterns including day-of-the-week (DOW), weekend, holiday effects, seasonality and temporal trend in the syndromic data of fever and neurological symptoms. Overall, HW exponential and regression method had better performances to remove explainable patterns. Conclusions: Addressing and removing explainable patterns of syndromic data on meningitis is necessary to timely and accurately detection of meningitis epidemics. It was concluded that decomposition methods had better performance compared to the model based ones. © 2016, Infectious Diseases and Tropical Medicine Research Center.