ArticleEpidemiology and infection2018
Using a distributed lag non-linear model to identify impact of temperature variables on haemorrhagic fever with renal syndrome in Shandong Province.
Article in Epidemiology and infection, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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15 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Distribution of geographical scale, data aggregation unit and period in the correlation analysis between temperature and incidence of HFRS in mainland China: A systematic review of 27 ecological studies.PLoS neglected tropical diseases · 2019Pooled it
- Associations between Legionellosis and meteorological variables in Ohio, 2001-2023.Infectious Disease Modelling · 2026Article
- Spatio-temporal clustering and meteorological factors influencing HFRS incidence in mainland China, 2004-2021.Epidemiology and infection · 2025Article
- Impact and lag effects of meteorology, air pollution and their interaction on HFRS: A case study of cities in Central China.One health (Amsterdam, Netherlands) · 2025Article
- Effect and prediction of long-term weather and pollutant exposure on hemorrhagic fever with renal syndrome: based on statistical models.Frontiers in public health · 2025Article
- Spatiotemporal distribution and meteorological factors of hemorrhagic fever with renal syndrome in Hubei province.PLoS neglected tropical diseases · 2024Article
- Using empirical dynamic modeling to identify the impact of meteorological factors on hemorrhagic fever with renal syndrome in Weifang, Northeastern China, from 2011 to 2020.PLoS neglected tropical diseases · 2024Article
- The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis.JMIR public health and surveillance · 2023Article
- Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units.Frontiers in public health · 2023Article
- Meteorological change and hemorrhagic fever with renal syndrome epidemic in China, 2004-2018.Scientific reports · 2022Article
- Analysis of the effect of meteorological factors on hemorrhagic fever with renal syndrome in Taizhou City, China, 2008-2020.BMC public health · 2022Article
- Effects and interaction of meteorological factors on hemorrhagic fever with renal syndrome incidence in Huludao City, northeastern China, 2007-2018.PLoS neglected tropical diseases · 2021Article
- Using the internet search data to investigate symptom characteristics of COVID-19: A big data study.World journal of otorhinolaryngology - head and neck surgery · 2020Article
- Overview of Existing Heat-Health Warning Systems in Europe.International journal of environmental research and public health · 2019Review
- The impact of meteorological factors on COVID-19 of California and its lag effect.Meteorological applicationsArticle
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Haemorrhagic fever with renal syndrome (HFRS) is transmitted to humans mainly by rodents and this transmission could be easily influenced by meteorological factors. Given the long-term changes in climate associated with global climate change, it is important to better identify the effects of meteorological factors of HFRS in epidemic areas. Shandong province is one of the most seriously suffered provinces of HFRS in China. Daily HFRS data and meteorological data from 2007 to 2012 in Shandong province were applied. Quasi-Poisson regression with the distributed lag non-linear model was used to estimate the influences of mean temperature and Diurnal temperature range (DTR) on HFRS by sex, adjusting for the effects of relative humidity, precipitation, day-of-the-week, long-term trends and seasonality. A total of 6707 HFRS cases were reported in our study. The two peaks of HFRS were from March to June and from October to December, particularly, the latter peak in 2012. The estimated effects of mean temperature and DTR on HFRS were non-linear. The immediate and strong effect of low temperature and high DTR on HFRS was found. The lowest temperature -8.86°C at lag 0 days indicated the largest related relative risk (RRs) with the reference (14.85 °C), respectively, 1.46 (95% CI 1.11-1.90) for total cases, 1.33 (95% CI 1.00-1.78) for the males and 1.76 (95% CI 1.12-2.79) for the females. Highest DTR was associated with a higher risk on HFRS, the largest RRs (95% CI) were obtained when DTR = 15.97 °C with a reference at 8.62 °C, with 1.26 (0.96-1.64) for total cases and 1.52 (0.97-2.38) for the female at lag 0 days, 1.22 (1.05-1.41) for the male at lag 5 days. Non-linear lag effects of mean temperature and DTR on HFRS were identified and there were slight differences for different sexes.
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