Fig 1.
Study design of meteorological factors and tick density affect the SFTS transmission.
SD = Sunshine duration, RH = Relative humidity, AT = Average temperature, PRCP = 24-hour precipitation, WS = Wind speed, TD = Tick density, and GCV = generalized cross-validation score; GAM: Generalized additive model; MMDM: Multi-population and multi-route dynamic model of SFTS.
Fig 2.
Land use map of each SFTS case location in Jiangsu Province from 2017 to 2019.
http://www.globallandcover.com/defaults.html?type=data&src=/Scripts/map/defaults/browse.html&head=browse&type=data (Map source).
Table 1.
Transmission routes of infection in 537 SFTS patients in Jiangsu Province, 2018–2020.
Fig 3.
MMDM fitting result of SFTS incidence of 2017–2019.
MMDM: Multi-population and multi-route dynamic model of SFTS.
Table 2.
Simulation of interventions for SFTS by the multi-population and multi-route dynamics model.
Fig 4.
Correlation analysis between no time lagging meteorological factors and tick density.
AP = Air pressure; SD = Sunshine duration; RH = Relative humidity; AT = Average temperature; PRCP = 24-hour precipitation; WS = Wind speed; TD = Tick density. Correlation coefficient (r) greater than 0.7 indicates a strong correlation between the two.
Fig 5.
Correlation analysis between time lagging meteorological factors and tick density.
AP = Air pressure; SD = Sunshine duration; RH = Relative humidity; AT = Average temperature; PRCP = 24-hour precipitation; WS = Wind speed; TD = Tick density. Correlation coefficient (r) greater than 0.7 indicates a strong correlation between the two.
Table 3.
Optimal generalized additive models built with time lags and non-time lags.
Fig 6.
Non-linear relationship between SFTS incidence and different transmissibility with meteorological factors and tick density in Jiangsu Province.
Part A: SFTS incidence and different infection coefficients with meteorological factors and tick density in no time lag GAM; A1: Plots of non-linear relationship with factors associated with reported incidence; A2: Plot of non-linear relationship with factors associated with the infection coefficient of human-to-human; A3: Plot of non-linear relationship with factors associated with the infection coefficient of environment-to-human; A4: Plot of non-linear relationship with factors associated with the infection coefficient of tick-to-human; A5: Plot of non-linear relationship with factors associated with the infection coefficient of animal-to-human. Part B: SFTS incidence and different infection coefficients with meteorological factors and tick density in time lag GAM; B1: Plots of non-linear relationship with factors associated with reported incidence; B2: Plot of non-linear relationship with factors associated with the infection coefficient of human-to-human; B3: Plot of non-linear relationship with factors associated with the infection coefficient of environment-to-human; B4: Plot of non-linear relationship with factors associated with the infection coefficient of tick-to-human; B5: Plot of non-linear relationship with factors associated with the infection coefficient of animal-to-human).
Fig 7.
Comparison of reported data of SFTS incidence and different infection coefficients with predicted GAM data without time lag in Zhejiang Province, 2016.
A: Reported incidence and GAM simulated incidence; B: Calculated human-to-human infection coefficient and the GAM simulated infection coefficient, β1:Infection coefficient of human-to-human; βw1:Infection coefficient of environment-to-human; β21:Infection coefficient of tick-to-human; β31:Infection coefficient of animal-to-human).
Fig 8.
GAM predicts the incidence of SFTS, infection coefficient and the impact of extreme weather in Jiangsu Province in 2020–2021.
Part 1: the prediction of GAM about SFTS incidence and different infection coefficients in normal weather; Part 2: the prediction of GAM about SFTS incidence and different infection coefficients in hurricane; Yellow part is the duration of the hurricane; Part 3: the prediction of GAM about SFTS incidence and different infection coefficients in drought; green part is the duration of the drought).
Table 4.
Extreme weather forecast in Jiangsu Province.