PLoS neglected tropical diseases · 2025
Epidemiological characteristics and risk distribution prediction of severe fever with thrombocytopenia syndrome in Zhejiang Province, China.
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Qu Rongrong, Liu Ying, Chen Mengsha, Zhang Huihui, Feng Qianqian, Yang Shigui, Sun Jimin
Аннотация
BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease, garnering increasing attention due to rising case numbers and expanding geographical reach. However, there is limited research on the potential factors influencing the distribution of SFTS.
METHODS: Data on SFTS cases in Zhejiang province were obtained from 2011 to 2022. Data on ecoclimatic factors, land cover, and human population density at the county level were also collected. Spatial autocorrelation analysis was used to analyze the epidemic characteristics and spatial clustering. A boosted regression tree (BRT) model was used to assess ecoclimatic and socioenvironmental drivers for the distributions of SFTS.
RESULTS: The SFTS cases increased from 9 in 2011-1,103 in 2022 with an average incidence rate of 0.099 per 100,000. There is an obvious seasonality to SFTS cases, primarily occurring between April and August. We detected global spatial autocorrelation of SFTS cases in all years (P < 0.05) except 2011, 2012 and 2014. Local spatial autocorrelation analysis suggested that the "High-high" agglomeration areas are mainly distributed in the hilly terrain of the east coast of Zhejiang province. Furthermore, factors such as mean temperature of wettest quarter (relative contribution, RC = 18.51%) and annual precipitation (9.29%) were found to have significantly contribution to the occurrence of SFTS. The model-predicted risk areas, particularly in Daishan County (predicted probability of cases: 0.986), Linhai city (0.972), and Tiantai County (0.971), align with reported cases.
CONCLUSIONS: These findings suggested that SFTS incidence has increased and spatially expanded over the past few years. It is necessary to expand the scope and improve the sensitivity of surveillance, especially in western, southern and northern Zhejiang.
METHODS: Data on SFTS cases in Zhejiang province were obtained from 2011 to 2022. Data on ecoclimatic factors, land cover, and human population density at the county level were also collected. Spatial autocorrelation analysis was used to analyze the epidemic characteristics and spatial clustering. A boosted regression tree (BRT) model was used to assess ecoclimatic and socioenvironmental drivers for the distributions of SFTS.
RESULTS: The SFTS cases increased from 9 in 2011-1,103 in 2022 with an average incidence rate of 0.099 per 100,000. There is an obvious seasonality to SFTS cases, primarily occurring between April and August. We detected global spatial autocorrelation of SFTS cases in all years (P < 0.05) except 2011, 2012 and 2014. Local spatial autocorrelation analysis suggested that the "High-high" agglomeration areas are mainly distributed in the hilly terrain of the east coast of Zhejiang province. Furthermore, factors such as mean temperature of wettest quarter (relative contribution, RC = 18.51%) and annual precipitation (9.29%) were found to have significantly contribution to the occurrence of SFTS. The model-predicted risk areas, particularly in Daishan County (predicted probability of cases: 0.986), Linhai city (0.972), and Tiantai County (0.971), align with reported cases.
CONCLUSIONS: These findings suggested that SFTS incidence has increased and spatially expanded over the past few years. It is necessary to expand the scope and improve the sensitivity of surveillance, especially in western, southern and northern Zhejiang.