Objective:To predict the monthly incidence of hemorrhagic fever with renal syndrome (HFRS) in Hebei Province by using the generalized additive model (GAM).Methods:The incidence data of HFRS in Hebei from 2006 to 2020 were collected, and the correlation coefficients between meteorological factors and the monthly incidence of HFRS in Hebei were analyzed by Spearman's correlation, and the meteorological factors were lagged by 0-6 orders, and those with the largest absolute values of the correlation coefficients were screened to be included in the multifactorial GAM to evaluate the effects of meteorological factors.Results:The monthly incidence of HFRS had the strongest correlation with monthly mean air temperature at lag order 2, monthly mean wind speed at lag order 0, monthly mean sunshine at lag order 4, monthly mean precipitation at lag order 2 and monthly mean humidity at lag order 1, which were diagnosed by the variance inflation factor and included in the multifactorial GAM, and the results showed significant differences among the factors (all P<0.001), and they showed non-linear relationships with the monthly incidence of HFRS. Mean monthly temperature was an important factor influencing HFRS incidence. Mean monthly air temperature, mean monthly sunshine and mean monthly wind speed were negatively associated with HFRS incidence, whereas mean monthly precipitation and mean monthly humidity were positively associated with HFRS incidence. Conclusions:There was a complex non-linear relationship between meteorological factors and the incidence of HFRS. GAM incorporated with lagged meteorological factors can be used to predict the incidence of HFRS in Hebei.

Objective:To predict the monthly incidence of hemorrhagic fever with renal syndrome (HFRS) in Hebei Province by using the generalized additive model (GAM).Methods:The incidence data of HFRS in Hebei from 2006 to 2020 were collected, and the correlation coefficients between meteorological factors and the monthly incidence of HFRS in Hebei were analyzed by Spearman's correlation, and the meteorological factors were lagged by 0-6 orders, and those with the largest absolute values of the correlation coefficients were screened to be included in the multifactorial GAM to evaluate the effects of meteorological factors.Results:The monthly incidence of HFRS had the strongest correlation with monthly mean air temperature at lag order 2, monthly mean wind speed at lag order 0, monthly mean sunshine at lag order 4, monthly mean precipitation at lag order 2 and monthly mean humidity at lag order 1, which were diagnosed by the variance inflation factor and included in the multifactorial GAM, and the results showed significant differences among the factors (all P<0.001), and they showed non-linear relationships with the monthly incidence of HFRS. Mean monthly temperature was an important factor influencing HFRS incidence. Mean monthly air temperature, mean monthly sunshine and mean monthly wind speed were negatively associated with HFRS incidence, whereas mean monthly precipitation and mean monthly humidity were positively associated with HFRS incidence. Conclusions:There was a complex non-linear relationship between meteorological factors and the incidence of HFRS. GAM incorporated with lagged meteorological factors can be used to predict the incidence of HFRS in Hebei.