Forecasting Road Traffic Injuries in North-Eastern Iran: The Effects of COVID-19 and Time Series Analysis (2009–2023)

Toktam Akbari Khalaj,1,* Taiebe Kenarangi,2 Vahid Fakoor,3 Vahid Ghavami,4 Ali Yazdani,5 Morteza Lotfi,5 Negar Sangsefidi4,* 1Department of Statistics, Emergency Medical Services, Mashhad University of Medical Sciences, Mashhad, Iran; 2Department of Public Health and Social Work, Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, Australia; 3Department of Statistics, Faculty of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran; 4Department of Biostatistics, Social Determinants of Health Research Center, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran; 5Department of Prehospital Emergency, Emergency Medical Services, Mashhad University of Medical Sciences, Mashhad, Iran*These authors contributed equally to this workCorrespondence: Negar Sangsefidi, Email [email protected]: Road Traffic Injuries (RTI) represent a major public health issue in the current century. Examining accident data is essential for developing effective strategies and measures aimed at minimizing these incidents and protecting lives.Methods: This study investigates the impact of the COVID-19 pandemic on RTI in northeast Iran, particularly in Mashhad, a city characterized by a high number of pilgrims and suburban commuters. We employed Seasonal Autoregressive Integrated Moving Average (ARIMA) models to analyze time‑series data collected between 2009 and 2023. The dataset included RTI records from official national sources. The seasonal ARIMA approach was selected to capture seasonal variations, long‑term trends, and the effects of pandemic‑related mobility restrictions on RTI patterns. All analyses were performed by statistical R software version 4.5.1.Results: The average monthly RTI decreased from 2385 (95% CI: 2055– 2794) in 2019 to 2035 (95% CI: 1707– 2362) in 2020. This reduction of approximately 14.8% was statistically significant (p < 0.05), highlighting the impact of pandemic-related traffic restrictions on road traffic injuries in Iran. The study employed detailed time series analysis, including seasonal ARIMA modeling, to forecast future RTI trends, which is crucial for informing effective prevention strategies.Conclusion: The analysis reveals seasonal patterns in RTI data, confirming the presence of non-stationarity. The results indicate a projected upward trend in RTI incidence, driven largely by increased traffic volumes in the post-pandemic period. These findings highlight the urgent need for targeted interventions to address this rising burden. Overall, the study offers valuable insights for policymakers, supporting more effective strategies to improve road safety and help mitigate the ongoing public health challenge posed by RTIs.Keywords: emergency medical services, COVID-19, time series, road traffic injuries