Paper Information

Journal:   ARCHIVES OF CLINICAL INFECTIOUS DISEASES   JULY 2016 , Volume 11 , Number 3; Page(s) 0 To 0.
 
Paper: 

TEMPORAL PATTERNS OF MENINGITIS IN HAMADAN, WESTERN IRAN: ADDRESSING AND REMOVING EXPLAINABLE PATTERNS

 
 
Author(s):  PAZHOUHI HASTYAR, KARAMI MANOUCHEHR*, ESMAILNASAB NADER, MOGHIMBEIGI ABBAS, FARIADRAS MOHAMMAD
 
* MODELING OF NON-COMMUNICABLE DISEASES RESEARCH CENTER AND DEPARTMENT OF BIOSTATISTICS AND EPIDEMIOLOGY, SCHOOL OF PUBLIC HEALTH, HAMADAN, I.R. IRAN
 
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.

 
Keyword(s): TREND, OUTBREAK, SMOOTHING, MENINGITIS, SURVEILLANCE
 
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