Biometeorological Modelling and Forecasting of Monthly Ambulance Demand for Hong Kong

Introduction Given the aging population in Hong Kong and the ever rising demand for emergency ambulance services, this study aimed to examine the effects of seasonality and weather on the demand for emergency ambulance services in Hong Kong. The feasibility of using time series models and selected weather factors to forecast average daily ambulance demand over a month was also assessed. Methods Monthly statistics for ambulance demand from 1998 to 2007 were obtained for analysing the effects of seasonality and weather on the demand for emergency ambulance services in Hong Kong. The effectiveness of weather factors in forecasting ambulance demand was also examined by comparing the performance of the autoregressive integrated moving average (ARIMA) model against other commonly used models. Results The lowest temperatures during cooler months were found to be negatively associated with average daily ambulance demand (adj-R2=0.38), while the average amount of cloud cover and highest temperatures were found to be positively associated with average daily ambulance demand during hotter months (adj-R2=0.34). When the analysis was stratified spatially by ambulance command units, Hong Kong Island had the highest adj-R2 during cool and hot months, reported at 0.55 and 0.46 respectively. With the inclusion of average temperature, the ARIMA models outperformed other models for both short- and long-term predictions. Conclusions Our findings suggest that weather factors, especially temperature, are significantly related to and useful for predicting ambulance demand.