Meteorological factors and tick density affect the dynamics of SFTS in jiangsu province, China.

• Published in: PLOS Neglected Tropical Diseases
• Main Authors: Bin Deng, Jia Rui, Shu-Yi Liang, Zhi-Feng Li, Kangguo Li, Shengnan Lin, Li Luo, Jingwen Xu, Weikang Liu, Jiefeng Huang, Hongjie Wei, Tianlong Yang, Chan Liu, Zhuoyang Li, Peihua Li, Zeyu Zhao, Yao Wang, Meng Yang, Yuanzhao Zhu, Xingchun Liu, Nan Zhang, Xiao-Qing Cheng, Xiao-Chen Wang, Jian-Li Hu, Tianmu Chen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Public Library of Science (PLoS) 2022
• Subjects: Arctic medicine. Tropical medicine; RC955-962; Public aspects of medicine; RA1-1270; Arctic; Gam
• Online Access: https://doi.org/10.1371/journal.pntd.0010432; https://doaj.org/article/45794d216bcd4041939d08f0f6a5b018

Background This study aimed to explore whether the transmission routes of severe fever with thrombocytopenia syndrome (SFTS) will be affected by tick density and meteorological factors, and to explore the factors that affect the transmission of SFTS. We used the transmission dynamics model to calculate the transmission rate coefficients of different transmission routes of SFTS, and used the generalized additive model to uncover how meteorological factors and tick density affect the spread of SFTS. Methods In this study, the time-varying infection rate coefficients of different transmission routes of SFTS in Jiangsu Province from 2017 to 2020 were calculated based on the previous multi-population multi-route dynamic model (MMDM) of SFTS. The changes in transmission routes were summarized by collecting questionnaires from 537 SFTS cases in 2018-2020 in Jiangsu Province. The incidence rate of SFTS and the infection rate coefficients of different transmission routes were dependent variables, and month, meteorological factors and tick density were independent variables to establish a generalized additive model (GAM). The optimal GAM was selected using the generalized cross-validation score (GCV), and the model was validated by the 2016 data of Zhejiang Province and 2020 data of Jiangsu Province. The validated GAMs were used to predict the incidence and infection rate coefficients of SFTS in Jiangsu province in 2021, and also to predict the effect of extreme weather on SFTS. Results The number and proportion of infections by different transmission routes for each year and found that tick-to-human and human-to-human infections decreased yearly, but infections through animal and environmental transmission were gradually increasing. MMDM fitted well with the three-year SFTS incidence data (P<0.05). The best intervention to reduce the incidence of SFTS is to reduce the effective exposure of the population to the surroundings. Based on correlation tests, tick density was positively correlated with air temperature, wind ...