ArticlePloS one2015
Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- G20 Country Burden of High BMI-Related Liver Cancer: Trends from 1990 to 2021 and Projections to 2050.Journal of epidemiology and global health · 2026Article
- Association between early arterial pH, base excess and lactate and 24-h mortality and neurological outcomes after cardiac arrest and cardiopulmonary resuscitation: a translational study.Resuscitation plus · 2026Article
- Exploring the association between vitamin D levels and dyslipidemia risk: insights from machine learning and generalized additive models.Frontiers in nutrition · 2025Article
- Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model.Scientific reports · 2024Article
- Spatiotemporal variations and potential influencing factors of hemorrhagic fever with renal syndrome: A case study in Weihe Basin, China.PLoS neglected tropical diseases · 2023Article
- Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China.International journal of chronic obstructive pulmonary disease · 2023Article
- The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation.BMC infectious diseases · 2021Article
- Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models.Infection and drug resistance · 2020Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHu County is a serious hemorrhagic fever with renal syndrome (HFRS) epidemic area, with notable fluctuation of the HFRS epidemic in recent years. This study aimed to explore the optimal model for HFRS epidemic prediction in Hu.
methodsThree models were constructed and compared, including a generalized linear model (GLM), a generalized additive model (GAM), and a principal components regression model (PCRM). The fitting and predictive adjusted R2 of each model were calculated. Ljung-Box Q tests for fitted and predicted residuals of each model were conducted. The study period was stratified into before (1971-1993) and after (1994-2012) vaccine implementation epochs to avoid the confounding factor of vaccination.
resultsThe autocorrelation of fitted and predicted residuals of the GAM in the two epochs were not significant (Ljung-Box Q test, P>.05). The adjusted R2 for the predictive abilities of the GLM, GAM, and PCRM were 0.752, 0.799, and 0.665 in the early epoch, and 0.669, 0.756, and 0.574 in the recent epoch. The adjusted R2 values of the three models were lower in the early epoch than in the recent epoch.
conclusionsGAM is superior to GLM and PCRM for monthly HFRS case number prediction in Hu County. A shift in model reliability coincident with vaccination implementation demonstrates the importance of vaccination in HFRS control and prevention.
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