ArticleOsong public health and research perspectives2025
Analysis of factors influencing hemorrhagic fever with renal syndrome and its prediction in Weifang, China from 2013 to 2021.
Article in Osong public health and research perspectives, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Drivers of Viral Spillover: An Examination of How Pathogens Spread.Pathogens (Basel, Switzerland) · 2026Review
- Epidemiological characteristics and spatiotemporal heterogeneity of hemorrhagic fever with renal syndrome in the Jiaodong Peninsula region, China, 2019-2025.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundThis 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.
methodsThe 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.
resultsWeifang 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).
conclusionClimate, 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.