Forecasting incidence of infectious diarrhea using Random forest in Jiangsu province, China

Abstract

Background: Infectious diarrhea caused considerable disease burden around the world. An accurate forecast of infectious diarrhea epidemic based on predictive models is crucial for public health authorities. We aimed to develop an optimal random forest（RF）model to predict infectious diarrhea incidence using meteorological factors in Jiangsu province, China. Methods: A RF model was constructed to predict infectious diarrhea incidence in Jiangsu province base on the morbidity during 2012-2017 and synchronous meteorological data, then compared with classical ARIMA and ARIMAX model to evaluate the model prediction effect. Results: The RF model was established including atmosphere pressure, precipitation, relative humidity and their lagged terms, one to four weeks lagged morbidity and time variable as predictors. Meanwhile, a univariate model ARIMA(1,0,1)(1,0,0) 52 (AIC=-575.92, BIC=-558.14) and a multivariable model ARIMAX(1,0,1)(1,0,0) 52 with precipitation and its one-week lagged term as external variables (AIC=-578.58, BIC=-578.13) were developed as benchmark models. Predictive values of RF model matched the real morbidity very well, with a mean absolute percentage (MAPE) about 20%. The performance of the multivariate ARIMAX model was similar to that of the univariate ARIMA model. Both of them performed much worse than the RF model, with a MAPE as high as near to 30%. Conclusions: In brief, RF model well integrated autocorrelation and seasonality of morbidity. It also comprehensively combined meteorological factors and their hysteresis effects. This model well fitted the dynamic of infectious diarrhea epidemic and achieved ideal predication accuracy. It could be used to predict the epidemic level, and has a good prospect of practical application.