Abstract:
Injury is a truly global health issue with massive societal and economic consequences.
In this study, Negative-Binomial integer-valued generalised autoregressive
conditional heteroscedasticity (NB-INGARCH) and autoregressive integrated
moving average (ARIMA) techniques have been compared and used to
build models for both Mpumalanga and Gauteng monthly injury mortality data.
The best model was chosen using the root mean square error (RMSE). The best
model is the one with the lowest RMSE value. The ARIMA(1, 1, 1) × (1, 1, 1)12
model had the lowest RMSE, making it the most suitable model for both MP
and GP monthly injury mortality data. The results identified ARIMA(1, 1, 1) ×
(1, 1, 1)12 as an appropriate model for predicting Mpumalanga and Gauteng
monthly injury mortality with the lowest root mean square error. ARIMA(1, 1, 1)×
(1, 1, 1)12 model is applied to forecast the injury mortality for the next two years.
Furthermore, the forecasted results of ARIMA(1, 1, 1) × (1, 1, 1)12 model show
a decrease of injury mortality in 2020 as compared to 2019. A multifaceted
approach to reduce injury mortality is needed. Regulating alcohol sales and
raising alcohol prices prevent all forms of violence, while improving drinking
environments prevent youth violence. A Graduated Driver Licensing system
could benefit the youth driver population to reduce transport accidents.
