Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model

Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Hao

doi:10.1016/j.annepidem.2014.10.015

Cited by 59 publications

(34 citation statements)

References 24 publications

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“…As epidemiologically monthly time series often contains noticeable seasonal and cyclical fluctuations, 16 hence in this study we constructed a seasonal ARIMA (SARIMA) method to model our data. In this model, the seasonality of TB incidence data was deemed as predictors and monthly TB incidence data as the response variable.…”

Section: Establishment Of Sarima Methodsmentioning

confidence: 99%

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

Wang

Ren

et al. 2020

IDR

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Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China. Methods: We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects. Results: In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead. Conclusion: The ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy.

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Section: Establishment Of Sarima Methodsmentioning

confidence: 99%

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

Wang

Ren

et al. 2020

IDR

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“…Additionally, time series forecasting methods have been used in various fields, including power and energy (Liu et al, 2012), finance (Qiu and Song, 2016) and traffic-flow (Zhang et al, 2015). Existing studies have adopted several metrics such as the National Emergency Department Overcrowding Score (Hoot and Aronsky, 2006) and ambulance diversion to measure and address emergency department overcrowding.…”

Section: Original Researchmentioning

confidence: 99%

Forecasting hourly emergency department arrival using time series analysis

Choudhury

Urena

2020

British Journal of Healthcare Management

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Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

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“…Another model is the Simple Seasonal Exponential Smoothing, characterized through level and season parameters [7]; this is appropriate for a time series with no trend and a seasonal effect constant over time and is equivalent with an ARIMA(0, 1, (1, p, p+1))(0, 1, 0) with restrictions among MA parameters (p is the number of periods in a seasonal interval -for monthly data p = 12). The model is described through the equations: …”

Section: A Theoretical Backgroundmentioning

confidence: 99%

Time series analysis in predicting the oro-maxillo-facial traumas

Dascălu

Zegan

Cernei

et al. 2015

2015 E-Health and Bioengineering Conference (EHB)

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A subject of high interest in epidemiology is to register monthly and yearly the prevalence of certain diseases, in order to make prognosis regarding the expected number of new patients. It is suitable to solve these sorts of problems by treating these monthly reports as time series and making forecasts through the modelling methods of Time Series Analysis. Our paper propose this approach in making forecasts about the expected number of 4 types of oro-maxillo-facial traumas at patients from Iasi, during a period of 8 months (May 2015 -December 2015) based on 11 years records made within the Oro-Maxillo-Facial Surgery Clinic, "Sf. Spiridon" Emergency Hospital Iasi. The forecasts were made in SPSS 16.0, using the Expert Modeler module, and the best suitable models found were Simple Seasonal Exponential Smoothing and Simple Exponential Smoothing. The paper presents the results of this analysis and the predicted values, with 95% confidence intervals. (Abstract)

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Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model

Cited by 59 publications

References 24 publications

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

Forecasting hourly emergency department arrival using time series analysis

Time series analysis in predicting the oro-maxillo-facial traumas

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