Analysis of factors influencing hemorrhagic fever with renal syndrome and its prediction in Weifang, China from 2013 to 2021
10.24171/j.phrp.2025.0367
- Author:
Hui ZHANG
1
;
Wan-Ying ZHAO
;
Yan-Qing YANG
;
Xue-Yan GUO
;
Yi-Han SHI
;
Qi-Yong LIU
;
Jing LI
Author Information
1. School of Public Health, Shandong Second Medical University, Weifang, China
- Publication Type:Original Article
- From:
Osong Public Health and Research Perspectives
2025;16(6):575-585
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:This study aimed to analyze the epidemiology and trends of hemorrhagic fever with renal syndrome (HFRS) in Weifang, China (2013–2021) and to guide prevention strategies.
Methods:The study examined the prevalence and incidence trends of HFRS in Weifang (2013–2021). Spearman correlation and wavelet analysis were employed to explore variable relationships and their associations with HFRS incidence. Generalized additive models (GAMs)were used to identify key risk factors, while structural equation modeling (SEM) quantified direct and indirect pathways influencing HFRS transmission. Finally, Bayesian time-series models wereapplied to predict future HFRS risk.
Results:Weifang reported 2,118 HFRS cases, which displayed distinct seasonality. Spearman correlation linked economic factors (gross domestic product [GDP], crop area, grain output, green space) and meteorological factors (temperature, pressure) to incidence (r > 0.8). Wavelet analysis identified Mus musculus (2013–2016) and Rattus norvegicus (2017–2021) as dominant reservoirs, with temperature, precipitation, and humidity correlating with incidence. GAMsrevealed a U-shaped relationship between rodent density and HFRS and an inverted U-shapedrelationship between temperature (threshold, 11.64 °C) and HFRS. SEM highlighted the direct and indirect effects of climate via rodent density, mirrored by economic factors (e.g., GDP). Bayesianmodels effectively predicted HFRS (root mean square error, 7.36; mean absolute percentage error, 0.28; R2 = 0.65).
Conclusion:Climate, economic, and anthropogenic factors drive the spread of HFRS. Preventionstrategies should integrate local economic conditions with meteorological and anthropogenicfactors. Bayesian time-series modeling effectively predicts HFRS trends, supporting precisionprevention strategies.
