Forecasting daily emergency ambulance service demand using biometeorological indexes.

This study aims to study the effectiveness of using biometeorological indexes in the development of a daily emergency ambulance service demand forecast system for Taipei City, Taiwan, compared to typical weather factors. Around 370,000 emergency ambulance service patient records were aggregated into a daily emergency ambulance service demand time series as the study's dependent variable. To assess the effectiveness of biometeorological indexes in making a 1 to 7-day forecast of daily emergency ambulance service demand, five forecast models were developed to make the comparison. The model with average temperature as the only predictor performed the best consistently from 1 to 7-day forecasts. The models with net effective temperature and apparent temperature as their only predictors ranked second and third, respectively. It is surprising that the model with both average temperature and relative humidity as predictors only ranked fourth. The unexpected outperformance of average temperature over net effective temperature and apparent temperature in forecasting daily emergency ambulance service demand suggested the need to develop updated locational-specific biometeorological indexes so that the benefit of the indexes can be fully utilized. Although adopting popular biometeorological indexes that are already available would be cheap and convenient, the benefit from these general indexes may not be guaranteed.