Modeling and forecasting of the under-five mortality rate in Kermanshah province in Iran: a time series analysis / 한국역학회지

OBJECTIVES: The target of the Fourth Millennium Development Goal (MDG-4) is to reduce the rate of under-five mortality by two-thirds between 1990 and 2015. Despite substantial progress towards achieving the target of the MDG-4 in Iran at the national level, differences at the sub-national levels should be taken into consideration. METHODS: The under-five mortality data available from the Deputy of Public Health, Kermanshah University of Medical Sciences, was used in order to perform a time series analysis of the monthly under-five mortality rate (U5MR) from 2005 to 2012 in Kermanshah province in the west of Iran. After primary analysis, a seasonal auto-regressive integrated moving average model was chosen as the best fitting model based on model selection criteria. RESULTS: The model was assessed and proved to be adequate in describing variations in the data. However, the unexpected presence of a stochastic increasing trend and a seasonal component with a periodicity of six months in the fitted model are very likely to be consequences of poor quality of data collection and reporting systems. CONCLUSIONS: The present work is the first attempt at time series modeling of the U5MR in Iran, and reveals that improvement of under-five mortality data collection in health facilities and their corresponding systems is a major challenge to fully achieving the MGD-4 in Iran. Studies similar to the present work can enhance the understanding of the invisible patterns in U5MR, monitor progress towards the MGD-4, and predict the impact of future variations on the U5MR.

Autoregressive integrated moving average model and circle distribution analysis of stroke mortality in Tianjin / 中华流行病学杂志

To develop a model for forecasting the mortality of stroke in Tianjin,China.The time series of stroke mortality from 1999 Jan.to 2006 Dec.in Tianjin city were subjected.Circle distribution analysis was used to verify the trend of time concentration.Multiple seasonal autoregressive integrated moving average model [ARIMA (p,d,q) (P,D,Q)s],based on model identification,estimation and verification of parameter,and analysis of the fitting of model,was established.Most of the deaths from stroke occurred in January and had a cycle of 12 months.An AR/MA model (0,1,0)×(0,1,1)12 was established(1-B)(1-B12) lnxt=0.001+(1-0.537 B12)εt.Conclusion: ARIMA & Circle Distribution analysis is an important tool for stroke mortality analysis.Potentially it has a high practical value on the surveillance,forecasting and prevention of stroke mortality.

Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever / 中华流行病学杂志

R2) of the two models were 0.801,0.872 respectively. The fitting efficacy of the ARIMA-GRNN combination model was better than the single ARIMA, which had practical value in the research on time series data such as the incidence of scarlet fever.

Applications of ARIMA model on predictive incidence of influenza / 第三军医大学学报

Objective To explore the application of auto regressive integrated moving average (ARIMA) and establish a predictive model for influenza to forecast the dynamic trend in order to develop the prevention policy scientifically. Methods Samples which caught influenza from 2002 Jan to 2006 Jun in Chongqing city were subjected. SPSS was used to fit ARIMA model,and Q statistic was used to verify the applicability of the model. Results The model of ARIMA(1,1,1) was established. The statistic of Q was smaller than ?2_?(m), verifying the applicability of this model. Conclusion The ARIMA model can be used to analyze the influenza incidence and make a short-term prediction.