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

Abstract

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 speed, and sunshine duration. The best GAM was a model with tick transmissibility to humans as the dependent variable, without considering lagged effects (GCV = 5.9247E-22, R2 = 96%). Reported incidence increased when sunshine duration was higher than 11 h per day and decreased when temperatures were too high (>28°C). Sunshine duration and temperature had the greatest effect on transmission from host animals to humans. The effect of extreme weather conditions on SFTS was short-term, but there was no effect on SFTS after high temperature and sunshine hours. Conclusions Different factors affect the infection rate coefficients of different transmission routes. Sunshine duration, relative humidity, temperature and tick density are important factors affecting the occurrence of SFTS. Hurricanes reduce the incidence of SFTS in the short term, but have little effect in the long term. The most effective intervention to reduce the incidence of SFTS is to reduce population exposure to high-risk environments.

The ecological and etiological investigation of ticks and rodents in China: results from an ongoing surveillance study in Zhejiang Province

“…Ticks, especially the H. longicornis, which had a higher density and wider distribution, were suggested to be the primary vector of SFTSV in China in recent years (1)(2)(3)(4). Research found that the tick density was one of the important factors affecting the occurrence of SFTS, and a high tick density reminded a higher exposure risk to SFTS (24,25). Zhejiang Province has always been the natural epidemic focus of SFTS (26).…”

Research Progress of Fever with Thrombocytopenia Syndrome

“…Hurricanes reduce the incidence of SFTS in the short term, but have little effect in the long term. The most effective intervention to reduce the incidence of SFTS is to reduce population exposure to high-risk environments [ 15 ].…”

Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China

“…Previous studies have mainly focused on multivariate analysis methods to predict the incidence of SFTS. These studies analyzed various environmental, meteorological, and social factors, among others, to identify incidence-related risk factors and establish prediction models based on these factors ( Cho et al., 2021 ; Deng et al., 2022 ; Ding et al., 2014 ; Sun et al., 2018 , 2021 ; Wu et al., 2020 ). However, few studies have reported models for prediction of SFTS incidence just based on the historical incidence data, which may be more convenient and practical.…”