Correlation analysis of incidence trends of severe fever with thrombocytopenia syndrome (SFTS) and meteorological factors in Weifang city, Shandong province, 2015-2024
10.3760/cma.j.cn112866-20250212-00023
- VernacularTitle:影响山东省潍坊市2015—2024年发热伴血小板减少综合征发病上升趋势因素分析
- Author:
Ziliang FAN
1
;
Xiyuan HUO
;
Yaqi SHEN
;
Cuimei GU
;
Zhu YANG
;
Senmei YUAN
;
Miaomiao SHAN
;
Jian ZHOU
;
Ye ZHANG
;
Dongying LI
Author Information
1. 潍坊市疾病预防控制中心，潍坊 261061
- Publication Type:Journal Article
- Keywords:
Severe fever with thrombocytopenia syndrome;
Spatiotemporal distribution;
Meteorological factors;
Epidemic trend
- From:
Chinese Journal of Experimental and Clinical Virology
2025;39(2):154-161
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the potential causes of the rising epidemic of severe fever with thrombocytopenia syndrome (SFTS) in Weifang, Shandong province.Methods:The temporal trend of SFTS epidemic was segmented using Joinpoint regression analysis. Changes in epidemiological characteristics across different periods were compared, and correlation analysis was conducted to identify meteorological factors influencing the epidemic trend.Results:Joinpoint regression revealed two distinct periods for SFTS epidemic in Weifang: 2015-2021 and 2022-2024. No significant trend was observed during 2015-2021 ( P=0.634), while a sharp annual increase of 46.69% occurred from 2022 to 2024 ( P=0.006). Spatial autocorrelation analysis demonstrated a global Moran’s I of 0.42 ( Z=8.55, P<0.001) for 2015-2021, with 15 high-high clustering areas identified. For 2022-2024, the global Moran’s I decreased to 0.37 ( Z=7.31, P<0.001), with 13 high-high clusters, including newly emerging hotspots in Anqiu and Zhucheng in the southeastern region. High-risk populations remained individuals aged ≥50 in mountainous and hilly areas, with a marked rise in incidence in these groups. The male-to-female ratio of cases was higher in plain areas than in mountainous/hilly regions. Autumn (September-November) temperatures from the preceding year showed a positive correlation with annual case numbers ( P=0.004, r=0.82). The linear regression expression is y=40.61x-580.78 (y is the annual incidence, and x is the average daily temperature of last autumn). Conclusions:The SFTS epidemic in Weifang is showing a rising trend. There is a linear correlation between the temperature of the previous autumn and the scale of SFTS epidemic in the following year. This correlation allows for predicting the subsequent year′s epidemic, thereby enabling early warning of SFTS.
