Related Experiment Video
Updated: Apr 8, 2026

Measurement of the Hand Transmitted Vibration of the Human Hand Arm System During Operation of a Hand Tractor
Published on: June 16, 2021
Work-related accidents among the Iranian population: a time series analysis, 2000-2011
Masoud Karimlou1, Masoud Salehi, Mehdi Imani
1Department of Biostatistics and Computer, University of Social Welfare & Rehabilitation Sciences , Tehran, Iran.
Background:
Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world.
Objectives:
To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011.
Methods:
In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box-Jenkins modeling to develop a time series model of the total number of accidents.
Results:
There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE).
Conclusions:
The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection.
Related Concept Videos
Hypothesis Test for Test of Independence
H0: The two variables (factors)...
Hazard Rate
Hazard Ratio
For example, in a clinical trial...
Statistical Methods for Analyzing Epidemiological Data
Determination of Expected Frequency
Relative Risk

