Showing 5 results for Time Series
J Hasanzadeh, F Najafi, M Moradinazar,
Volume 11, Issue 1 (6-2015)
Abstract
The time series is a collection of observation data that are arranged according to time. The main purpose of setting up a time series is to predict future values. The first step in time series data is graphed. Using graphs can provide general information such as uptrend or downtrend, seasonal patterns, periodic presence, and outliers in time series graphs. After graphing the data, if a good forecast is required, stationary data can be used. Differencing or decomposition methods can be used to make the data stationary. Then, a correlogram can be used to identify the order moving average and autoregressive model. The parameters of the model are examined using T-test. If the parameters are significant and the residue is independence, the predicted values can be evaluated using the mean absolute percentage error.
K Jafari, M Karami, A Soltanian, N Esmailnasab,
Volume 12, Issue 2 (8-2016)
Abstract
Background and Objectives: Syndromic surveillance systems are used to early detection of outbreaks. The purpose of this study was to determine the feasibility of clinical and non-clinical data sources used in influenza syndromic surveillance in Zanjan.
Methods: In this time series study, clinical and non-clinical data related to influenza like illness (ILI) as a potential data source of syndromic surveillance systems, including the number of missed school days collected from 12 schools and the data of over the counter (OTC) drug sale obtained from 15 pharmacies selected randomly in Zanjan during 2014 were used. We used the line plot and moving average chart to explore trends and detect potential explainable patterns of data sources. The autocorrelation function and cross correlation function besides corresponding graphs were used to assess the feasibility of school absenteeism and OTC sale in timely detection of influenza outbreaks.
Results: Line plots indicated the presence of explainable patterns and the effect of the day of the week. The cross correlation value was 0.5 and cross correlogram revealed the similarity of both data sources in this study.
Conclusion: Our findings indicated the feasibility of influenza data sources, including school absenteeism and OTC, as potential data sources of syndromic surveillance systems.
S Sharifi, M Karami, N Esmailnasab, Gh Rooshanaei, Farsan,
Volume 12, Issue 4 (2-2017)
Abstract
Background and Objectives: Cardiac diseases are a major cause of death in Iran. The number of deaths from cardiac diseases can be reduced through controlling air pollution. The aim of this study was to determine the relationship between increased air pollution and mortality from respiratory and cardiac diseases in Tehran.
Methods: The average daily concentrations of five pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and particulate matter less than 10 microns (PM10) were collected from 8 stations in Tehran, Iran. Then, their effects on the number of daily deaths due to cardiovascular and respiratory diseases were calculated using time series and Poisson GLARMA model (generalized linear autoregressive moving average). The climatic elements such as mean, maximum, and minimum temperature and daily humidity were considered as confounding factors.
Results: After adjustment for potential confounding variables of the final model of the pollutants, the mean daily ozone level (P = 0.02) and particulate matters less than 10 microns (P <0.001) had a significant correlation with the number of daily deaths.
Conclusion: According to the results of this study that addressed the relationship between air pollutants and death using new statistical methods, it is necessary to take more effective measures to control ozone and particulate matters less than 10 microns to reduce the mortality of heart and respiratory diseases in Tehran.
Mt Shakeri , R Yousefi, M Gholian Aval , M Salari, M Amini, A Hamedi,
Volume 16, Issue 4 (3-2021)
Abstract
Background and Objectives: Investigation of child mortality is one of the most important strategies for improving children's health. The purpose of this study was to investigate the age distribution, trends, and projections of mortality in children under 5 years old in Khorasan Razavi province.
Methods: The study population included under-5 mortality data from Khorasan Razavi Province during 2012-2017 extracted from the Causes and Mortality Classification System of Vice-Chancellery of Health, Mashhad University as well as five universities and faculties. Cause of mortality was classified according to the ICD10 codes. Data were controlled using the ANACod software. Descriptive statistics methods and autoregressive integrated moving average (ARIMA) modeling were applied to explore the mortality trend during the time of study using the Minitab.15 and STATA16.
Results: According to the results, the highest mortality rate for children under five was in 2014 and the lowest in 2017. Using the differencing method, the data were stabilized. Finally, the ARIMA model (1,1,2) was identified as a suitable model using the MINITAB software.
Conclusion: The mortality rate of children under five has declined sharply in the last four years in Khorasan Razavi Province. It is predicted that this reduction will continue according to fitted model. However, we are still far from mortality rates in developed and some developing countries; therefore, efforts should be made to reduce the under-five mortality rate by increasing the level of health services, the awareness level of families, and improving maternal and childbirth care.
N Rajabi, R Fadaei, A Khazeni, J Ramezanpour, S Nasiri Esfahani, Gh Yadegarfar,
Volume 17, Issue 3 (12-2021)
Abstract
Background and Objectives: Due to the importance of cutaneous leishmaniasis, the national leishmaniasis project began in 2007 in Iran. The aim of the present study was to evaluate community interventions in changes in the incidence of cutaneous leishmaniasis in Isfahan Province from 2002 to 2018: an Interrupted time series regression analysis.
Materials and Methods: The present study was a repeated cross-sectional study. The incidence and 95% confidence interval were used to describe the disease trend. Data were entered into the Excel and analyzed using STATA14 software at a significance level of 5%. Intermittent time series regression analysis was used to evaluate community interventions in changes of leishmaniasis incidence.
Results: from 2002 to 2018, the data of 43,904 patients with leishmaniasis was registered in Isfahan Health Centers. The mean (standard deviation) age of the patients was 23.99 (19.03) years. The incidence had a decreasing trend after the interventions in all affiliated cities and the whole province.
Conclusion: The preventive intervention programs of the provincial health center have been rather successful and have reduced the incidence of the disease in the years after the intervention, so that despite the large number of confounding and influential factors regarding this disease, preventive intervention programs have led to disease control according to the reported annual incidence.
