ArticleJMIR public health and surveillance2023
The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis.
Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 17 citations in OpenAlex.
- Urbanization influences hemorrhagic fever with renal syndrome transmission: 34-year evidence from China's national surveillance.Infectious Disease Modelling · 2026Article
- A molecular survey of orthohantaviruses in rodents across the tri-border region of China, Russia, and North Korea.PLoS neglected tropical diseases · 2026Article
- Spatio-temporal clustering and meteorological factors influencing HFRS incidence in mainland China, 2004-2021.Epidemiology and infection · 2025Article
- Non-negligible impacts of urbanization on spatiotemporal variations of infectious disease: a case study of hemorrhagic fever with renal syndrome epidemics in China.Infectious diseases of poverty · 2025Article
- Correlating hemorrhagic fever with renal syndrome incidence and research publications in China: insights from epidemiological and bibliometric analysis.One health outlook · 2025Article
- Environmental Change and Hemorrhagic Fever with Renal Syndrome Transmission Risk on the China-Russia Border.EcoHealth · 2025Article
- Forecasting tuberculosis epidemics using an autoregressive fractionally integrated moving average model: a 17-year time series analysis.Journal of global health · 2025Article
- Risk of hemorrhagic fever with renal syndrome associated with meteorological factors in diverse epidemic regions: a nationwide longitudinal study in China.Infectious diseases of poverty · 2025Article
- Spatiotemporal distribution and meteorological factors of hemorrhagic fever with renal syndrome in Hubei province.PLoS neglected tropical diseases · 2024Article
- Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.BMC public health · 2024Article
- Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model.Scientific reports · 2024Article
- Investigating the impact of climatic and environmental factors on HFRS prevalence in Anhui Province, China, using satellite and reanalysis data.Frontiers in public health · 2024Article
- Building a pathway to One Health surveillance and response in Asian countries.Science in One Health · 2024Review
- Spatial-temporal analysis of hepatitis E in Hainan Province, China (2013-2022): insights from four major hospitals.Frontiers in public health · 2024Article
- Epidemiological characteristics and prediction model construction of hemorrhagic fever with renal syndrome in Quzhou City, China, 2005-2022.Frontiers in public health · 2023Article
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12 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease mainly transmitted by rodents. However, the determinants of its spatiotemporal patterns in Northeast China remain unclear.
objectiveThis study aimed to investigate the spatiotemporal dynamics and epidemiological characteristics of HFRS and detect the meteorological effect of the HFRS epidemic in Northeastern China.
methodsThe HFRS cases of Northeastern China were collected from the Chinese Center for Disease Control and Prevention, and meteorological data were collected from the National Basic Geographic Information Center. Times series analyses, wavelet analysis, Geodetector model, and SARIMA model were performed to identify the epidemiological characteristics, periodical fluctuation, and meteorological effect of HFRS in Northeastern China.
resultsA total of 52,655 HFRS cases were reported in Northeastern China from 2006 to 2020, and most patients with HFRS (n=36,558, 69.43%) were aged between 30-59 years. HFRS occurred most frequently in June and November and had a significant 4- to 6-month periodicity. The explanatory power of the meteorological factors to HFRS varies from 0.15 ≤ q ≤ 0.01. In Heilongjiang province, mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean pressure with a 5-month lag had the most explanatory power on HFRS. In Liaoning province, mean temperature with a 1-month lag, mean ground temperature with a 1-month lag, and mean wind speed with a 4-month lag were found to have an effect on HFRS, but in Jilin province, the most important meteorological factors for HFRS were precipitation with a 6-month lag and maximum evaporation with a 5-month lag. The interaction analysis of meteorological factors mostly showed nonlinear enhancement. The SARIMA model predicted that 8,343 cases of HFRS are expected to occur in Northeastern China.
conclusionsHFRS showed significant inequality in epidemic and meteorological effects in Northeastern China, and eastern prefecture-level cities presented a high risk of epidemic. This study quantifies the hysteresis effects of different meteorological factors and prompts us to focus on the influence of ground temperature and precipitation on HFRS transmission in future studies, which could assist local health authorities in developing HFRS-climate surveillance, prevention, and control strategies targeting high-risk populations in China.
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