Time series based forecasting of ankle and foot soft tissue injuries in Mongolia via Sarima and changepoint aware modelsels
Keywords:
Ankle injury, Foot injury, Time-series analysis, SARIMA, Seasonal variation, Injury epidemiologyAbstract
Objective: Ankle and foot soft tissue injuries impose a substantial burden on emergency healthcare services, particularly in low- and middle-income countries where injury surveillance systems remain limited. This study aimed to investigate temporal trends and seasonal patterns of ankle and foot soft tissue injuries in Mongolia and to forecast future incidence using time-series models. Methods: Emergency department records from the National Trauma and Orthopedic Research Center of Mongolia (2014–2023) were retrospectively analyzed. Monthly ankle and foot soft tissue injury cases were aggregated and evaluated using descriptive statistics and chi-square tests. Seasonal autoregressive integrated moving average (SARIMA) models were applied to assess temporal trends and seasonal variation. Model performance and forecast accuracy were evaluated using AIC, BIC, residual diagnostics, and MAPE. Results: A total of 45,237 cases were identified during the study period. The majority of injuries occurred among males (54.5%) and residents of Ulaanbaatar (86.0%) (p < 0.001). Clear seasonal variation was observed, with injury peaks during winter and late spring. Among the candidate models, SARIMA (2,1,10)(1,1,1)12 demonstrated the best fit. Forecast validation showed acceptable predictive accuracy (MAPE = 19.43%). Projections for 2024–2025 indicated a relatively stable injury burden with persistent seasonal fluctuations. Conclusions: Ankle and foot soft tissue injuries in Mongolia demonstrate distinct temporal and seasonal patterns. SARIMA modeling provides a practical approach for forecasting injury trends and may facilitate seasonal injury surveillance, targeted prevention programs, and evidence-based healthcare resource allocation.
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