Comparison of model performance and goodness-of-fit for support vector regression (SVR) model, step-down linear regression model (Linear), gradient boosted regression tree model (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression algorithm and generalized additive model (GAM) by the means of root-mean-square error (RMSE) and R-squared, respectively.

Two prediction periods were considered: 1) data corresponding to the period between the 41st to 53rd weeks (the last 12 weeks) in 2014 was used to validate the models; 2) data corresponding to the period between the 35th to 46th weeks which covers the outbreak in dengue incidence in 2014 was used to validate the models. Results are presented for five cities with a high risk of dengue infection, and the other cities in Guangdong province.