{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T16:20:56Z","timestamp":1787934056763,"version":"build-2784847793"},"reference-count":30,"publisher":"Korea Disease Control and Prevention Agency","issue":"6","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["ZR2025QC908"],"award-info":[{"award-number":["ZR2025QC908"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32090023"],"award-info":[{"award-number":["32090023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ophrp.org"],"crossmark-restriction":true},"short-container-title":["PHRP"],"published-print":{"date-parts":[[2025,12,31]]},"abstract":"<jats:p>Objectives: This study aimed to analyze the epidemiology and trends of hemorrhagic fever with renal syndrome (HFRS) in Weifang, China (2013\u20132021) and to guide prevention strategies.Methods: The study examined the prevalence and incidence trends of HFRS in Weifang (2013\u20132021). 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&gt;0.8). Wavelet analysis identified Mus musculus (2013\u20132016) and Rattus norvegicus (2017\u20132021) 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 \u00b0C) 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.<\/jats:p>","DOI":"10.24171\/j.phrp.2025.0367","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T09:26:25Z","timestamp":1767086785000},"page":"575-585","update-policy":"https:\/\/doi.org\/10.24171\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis of factors influencing hemorrhagic fever with renal syndrome and its prediction in Weifang, China from 2013 to 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