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

• Main Authors: Cui, F. (Author), Hu, Y.C (Author), Li, C.Y (Author), Li, X.J (Author), Liu, L.L (Author), Qi, C. (Author), Wang, L. (Author), Zhu, Y.C (Author)
• Format: Article
• Language: English
• Published: Cambridge University Press 2022
• Subjects: Generalised additive model; hand-foot-and-mouth diseases; predict; random forest regression; support vectors regression
• Online Access: View Fulltext in Publisher

 The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions 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 the generalised additive model (GAM) to evaluate the 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. The 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 the 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 public health agencies implement prevention and control measures in advance. Copyright © The Author(s), 2021. Published by Cambridge University Press