Epidemiological and spatiotemporal analysis of severe fever with thrombocytopenia syndrome in Eastern China, 2011–2021

Abstract

Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease, which is caused by severe fever with thrombocytopenia syndrome virus (SFTSV) with high fatality. Recently, the incidence of SFTS increased obviously in Jiangsu Province. However, the systematic and complete analysis of spatiotemporal patterns and clusters coupled with epidemiological characteristics of SFTS have not been reported so far. Methods Data on SFTS cases were collected during 2011–2021. The changing epidemiological characteristics of SFTS were analyzed by adopting descriptive statistical methods. GeoDa 1.18 was applied for spatial autocorrelation analysis, and SaTScan 10.0 was used to identify spatio-temporal clustering of cases. The results were visualized in ArcMap. Results The annual incidence of SFTS increased in Jiangsu Province from 2011 to 2021. Most cases (72.4%) occurred during May and August with the obvious peak months. Elderly farmers accounted for most cases, among which both males and females were susceptible. The spatial autocorrelation and spatio-temporal clustering analysis indicated that the distribution of SFTS was not random but clustered in space and time. The most likely cluster was observed in the western region of Jiangsu Province and covered one county (Xuyi county) (Relative risk = 8.18, Log likelihood ratio = 122.645, P < 0.001) located in southwestern Jiangsu Province from January 1, 2017 to December 31, 2021. The Secondary cluster also covered one county (Lishui county) (Relative risk = 7.70, Log likelihood ratio = 94.938, P < 0.001) from January 1, 2017 to December 31, 2021. Conclusions The annual number of SFTS cases showed an increasing tendency in Jiangsu Province from 2011 to 2021. Our study elucidated regions with SFTS clusters by means of ArcGIS in combination with spatial analysis. The results demonstrated solid evidences for the orientation of limited sanitary resources, surveillance in high-risk regions and early warning of epidemic seasons in future prevention and control of SFTS in Jiangsu Province.

Epidemiological characteristics and spatio-temporal aggregation of severe fever with thrombocytopenia syndrome in Jinan City, China, 2018–2022

“…Age-specific CFRs can further clarify differences among age groups. Generally, these results were the same as previous studies [20,21,[26][27][28][29]. Epidemiological research indicates that SFTS fatalities are significantly linked to age, gender, underlying health conditions, and other host factors that influence the immune status of an individual.…”

Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China

“…However, there are also different views; for example, some scholars believe that the migration of ticks and the small animals that carry them may allow SFTSV to expand to more areas and thus lead to more human infections [ 38 , 39 ]. Similar to the findings of prior studies [ 13 , 16 , 40 , 41 ], SFTS cases were mainly recorded from April to October, which may be related to the dynamics of the ticks. Haemaphysalis longicornis and Dermacentor nuttalli are dominant tick species in Shandong Province, accounting for 75.3 and 24.7% of the collected ticks, respectively [ 33 , 42 ].…”

Analysis of the spatial-temporal distribution characteristics of hepatitis E in Jiangsu province from 2005 to 2020

“…HE showed a correlation at the provincial level and its overall incidence exhibited a spatial clustering pattern ( 20 ). Moran's I values of global spatial autocorrelation of HE incidence in different years were relatively stable, though different, and all positively correlated ( p < 0.01).…”

“…The closer the value is to −1, the greater the differences between the research units are, indicating a dispersed distribution. When I = 0, there is no spatial association, and the research objects are randomly distributed ( 20 , 21 ).…”

“…In this study, local spatial autocorrelation analysis was conducted at the county (district or municipal) level. LISA is classified into five categories: High-High (H-H), Low-Low (L-L), None (spatial outliers not significant), High-Low (H-L), and Low-High (L-H) ( 20 , 21 ). Z -tests were used to assess the significance of the statistics at a level of α = 0.05.…”