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 were applied 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. GAMs revealed a U-shaped relationship between rodent density and HFRS and an inverted U-shaped relationship 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). Bayesian models 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. Prevention strategies should integrate local economic conditions with meteorological and anthropogenic factors. Bayesian time-series modeling effectively predicts HFRS trends, supporting precision prevention strategies.
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Results The national incidence increased until 2017 and has decreased since 2018, whereas the AAPC rebound to 4.32% during phase IV (2019–2024). The proportion of female cases decreased, while that of adults ≥70 years increased significantly. In phase IV, the average annual percent change increased in central and urban regions. The lag effect of meteorological factors lengthened from 4 to 6 months, with mean temperature (Tmean) and relative humidity (RH) representing the primary predictors. Phase III (2013–2018) displayed the highest cumulative relative risk (RR) for Tmean at 25.2 °C (RR, 5.86; 95% confidence interval [CI], 2.56–13.42), whereas in phase IV, only moderate RH (58%) remained significantly associated with incidence (RR, 1.68; 95% CI, 1.29–2.20).
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