Predicting number of traumas using the seasonal time series model

Authors

1 Department of Statistics, Ilam Branch, Islamic Azad University, Ilam, Iran

2 Psychosocial Injuries Research Center, Ilam University of Medical Sciences; Clinical Research Development Unit, Mostafa Khomeini Hospital, Ilam University of Medical Sciences, Ilam, Iran

3 Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran

4 Department of Pediatrics, Ilam University of Medical Sciences, Ilam, Iran

5 Department of Surgery, Ilam University of Medical Sciences, Ilam, Iran

Abstract

Background: Road accidents and casualties resulted are among the current challenges of human societies, which have imposed a high cost on the economies of countries. Objectives: Prediction of accidents caused by driving incidents helps planners achieve a suitable model to reduce the occurrence of traumas resulted from the driving accidents. Materials and Methods: In this study, a seasonal time series model was used for predicting the number of road accident traumas. Data related to the patients referring to Imam Khomeini Hospital in Ilam Province were evaluated from March 2012 to June 2017. Results: The results showed that during November and October in 2015 and 2016, we had the highest number of accidents due to high traffic during New Year's Vacation, summer trips, and religious pilgrimages including the Arbaeen. Moreover, the results depicted that the seasonal Arima model was effective in predicting the number of traumas due to accidents. Furthermore, forecasting the model showed an ascending trend in the number of accidents in the following 3 years. Conclusion: The number of accident traumas in the forthcoming months can be predicted through time series models. Of course, these models can be used by managers as appropriate tools for traffic analysis. Furthermore, the increasing trend in the number of traumas indicates that serious consideration for planning and managing such events seems necessary for the administrators in the near future.

Keywords

References

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Archives of Trauma Research
Volume 8, Issue 1 - Serial Number 1
January 2019
Pages 23-27

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History

  • Receive Date: 09 November 2020
  • Accept Date: 09 November 2020

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