Infectious diseases (London, England) · 2026
Spatiotemporal analysis and prediction of severe fever with thrombocytopenia syndrome in Qingdao city, China, 2014-2025.
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Meng Xiangcheng, Zhang Huaqiang, Hu Xiaowen, Zhai Hongrui, Du Shanshan, Lu Runze, Gao Ruqin, Jia Jing
Аннотация
OBJECTIVES: Research on Severe Fever with Thrombocytopenia Syndrome (SFTS) in Qingdao, an emerging public health threat, remains limited. This study aimed to systematically characterise its epidemiological features, identify spatiotemporal clustering patterns and develop a time-series prediction model to inform targeted prevention and control strategies.
METHODS: SFTS case data in Qingdao from 2014 to 2025 were acquired from the China Information System for Disease Control and Prevention. Spatial autocorrelation analysis, encompassing both global and local assessments, was performed using ArcGIS 10.8. The identification of spatiotemporal clusters was carried out with SatScan version 9.6. Furthermore, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed utilising R software (version 4.4.2) for time-series forecasting.
RESULTS: From 2014 to 2025, 711 SFTS cases were reported in Qingdao, corresponding to an average annual incidence of 0.61 per 100,000 population. There were 28 deaths, yielding a case fatality rate of 3.94%. Cases showed strong seasonality, with 99.16% occurring between April and October, predominantly among elderly farmers. Incidence exhibited positive spatial autocorrelation, with high-high clusters primarily concentrated in Huangdao District in the southern part of Qingdao. Spatiotemporal scan analysis identified three significant clusters located in the southern, northeastern and southwestern regions. The SARIMA model indicates that the number of cases is projected to exhibit an increasing trend beyond 2025.
CONCLUSION: SFTS in Qingdao exhibits obvious spatiotemporal clustering, and the number of cases continues to show an increasing trend.
METHODS: SFTS case data in Qingdao from 2014 to 2025 were acquired from the China Information System for Disease Control and Prevention. Spatial autocorrelation analysis, encompassing both global and local assessments, was performed using ArcGIS 10.8. The identification of spatiotemporal clusters was carried out with SatScan version 9.6. Furthermore, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed utilising R software (version 4.4.2) for time-series forecasting.
RESULTS: From 2014 to 2025, 711 SFTS cases were reported in Qingdao, corresponding to an average annual incidence of 0.61 per 100,000 population. There were 28 deaths, yielding a case fatality rate of 3.94%. Cases showed strong seasonality, with 99.16% occurring between April and October, predominantly among elderly farmers. Incidence exhibited positive spatial autocorrelation, with high-high clusters primarily concentrated in Huangdao District in the southern part of Qingdao. Spatiotemporal scan analysis identified three significant clusters located in the southern, northeastern and southwestern regions. The SARIMA model indicates that the number of cases is projected to exhibit an increasing trend beyond 2025.
CONCLUSION: SFTS in Qingdao exhibits obvious spatiotemporal clustering, and the number of cases continues to show an increasing trend.