Comparison of different predictive models on HFMD based on weather factors in Zibo city, Shandong Province, China

Abstract

The early identification and prediction of Hand-foot-and-Mouth diseases (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regionsd due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010~2019 and used Generalized Additive Model (GAM) to evaluate effects of weather factors on HFMD cases. Then, GAM, Support Vectors Regression (SVR) and Random Forest Regression (RFR) models are used to compare predictive results. Annual average incidence was 129.72/100,000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases, and based on predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM> RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help the public health agencies implement prevention and control measures in advance.

Analysis of A SARIMA-XGBoost Model for Hand, Foot and Mouth Disease in Xinjiang, China

“…To fit the nonlinear correlations between the number of patients and meteorological factors, the machine learning algorithm has displayed significant advantages over traditional statistical models 22 . Therefore, many machine learning methods have been applied to predict the number of HFMD cases, such as long and short-term memory networks 23,24 , random forest 25 , recurrent neural network 26 , and support vector regression 27 . However, past studies have not considered meteorological factors influencing time and space, making most models not generalizable to a specific location or time.…”

Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China

“…To capture the nonlinear correlations between patient numbers and meteorological factors, machine learning algorithms have shown significant advantages over traditional statistical models [ 22 ]. Therefore, many machine learning methods have been applied to predict the number of HFMD cases, such as long- and short-term memory networks [ 23 , 24 ], random forest [ 25 ], recurrent neural network [ 26 ], and support vector regression [ 27 ]. However, past studies have not considered the influence of meteorological factors on both time and space, rendering most models non-generalizable to specific locations or times.…”