Prediction model of the number of street voluntary blood donors

Objective To explore the relationship between climate factors and the number of street voluntary blood donors in Beijing and develop a reliable predictive model, so as to provide reference for donor recruitment. Methods The data of weather and the number of street blood donors from January 2018 to October 2019 were collected to formulate generalized additive model(GAM) and autoregressive integrated moving average model(ARIMA), and the predicative accuracy of the two models was assessed using data from November to December 2019. Results GAM indicated that the number of donors decreased when the wind force was 4 to 5 (95%CI: 0.805, 0.995), and the number on weekends and official holidays was 1.562 (95% CI: 1.510, 1.617) and 1.779 (95%CI: 1.035, 3.055) times that of the working day respectively. The number of blood donors increased with the elevation of temperature until 25℃, then declined with temperature increasing slowly. The two-day predictive accuracy of GAM and ARIMA was 92.14% and 90.55%, with overall accuracy at (84.46±11.12)% and (87.65±9.3)%, respectively. Conclusion Considering official holiday, strong wind and temperature, etc, the ARIMA model runs stable overall, while GAM is good at short-term prediction. The comprehensive use of two predictive models is helpful in guiding the recruitment of blood donors.