ArticlePLoS neglected tropical diseases2023
Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model.
Article in PLoS neglected tropical diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 16 citations in OpenAlex.
- Spatiotemporal dynamics of the capture rate of Rattus tanezumi and its implications for rodent-borne diseases in the Three Gorges Reservoir Area, China.Environmental monitoring and assessment · 2026Article
- Prediction, lag and mixture effects of meteorology and pollutants on the incidence of pulmonary tuberculosis in Jining City, China.BMC public health · 2026Article
- Impact of weather extremes on the spatiotemporal dynamics of visceral leishmaniasis in Brazil.PLoS neglected tropical diseases · 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
- Association between exposure to air pollutants and cardiovascular mortality in Iran: a case-crossover study.Scientific reports · 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
- Meteorological drivers of hemorrhagic fever with renal syndrome in China's Jiaodong Peninsula: an ecological time-series study from 2020 to 2024.Frontiers in public health · 2025Article
- The impact of air pollutants on the risk of goiter based on a 9-year time series data.Frontiers in public health · 2025Article
- Spatiotemporal distribution and meteorological factors of hemorrhagic fever with renal syndrome in Hubei province.PLoS neglected tropical diseases · 2024Article
- Pollutants-mediated viral hepatitis in different types: assessment of different algorithms and time series models.Scientific reports · 2024Article
- Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model.Scientific reports · 2024Article
- Long- and Short-Run Asymmetric Effects of Meteorological Parameters on Hemorrhagic Fever with Renal Syndrome in Heilongjiang: A Population-Based Retrospective Study.Transboundary and emerging diseases · 2024Article
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3 authors at 2 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 rodent-related zoonotic disease induced by hantavirus. Previous studies have identified the influence of meteorological factors on the onset of HFRS, but few studies have focused on the stratified analysis of the lagged effects and interactions of pollution and meteorological factors on HFRS.
methodsWe collected meteorological, contaminant and epidemiological data on cases of HFRS in Shenyang from 2005-2019. A seasonal autoregressive integrated moving average (SARIMA) model was used to predict the incidence of HFRS and compared with Holt-Winters three-parameter exponential smoothing model. A distributed lag nonlinear model (DLNM) with a maximum lag period of 16 days was applied to assess the lag, stratification and extreme effects of pollution and meteorological factors on HFRS cases, followed by a generalized additive model (GAM) to explore the interaction of SO2 and two other meteorological factors on HFRS cases.
resultsThe SARIMA monthly model has better fit and forecasting power than its own quarterly model and the Holt-Winters model, with an optimal model of (1,1,0) (2,1,0)12. Overall, environmental factors including humidity, wind speed and SO2 were correlated with the onset of HFRS and there was a non-linear exposure-lag-response association. Extremely high SO2 increased the risk of HFRS incidence, with the maximum RR values: 2.583 (95%CI:1.145,5.827). Extremely low windy and low SO2 played a significant protective role on HFRS infection, with the minimum RR values: 0.487 (95%CI:0.260,0.912) and 0.577 (95%CI:0.370,0.898), respectively. Interaction indicated that the risk of HFRS infection reached its highest when increasing daily SO2 and decreasing humidity.
conclusionsThe SARIMA model may help to enhance the forecast of monthly HFRS incidence based on a long-range dataset. Our study had shown that environmental factors such as humidity and SO2 have a delayed effect on the occurrence of HFRS and that the effect of humidity can be influenced by SO2 and wind speed. Public health professionals should take greater care in controlling HFRS in low humidity, low windy conditions and 2-3 days after SO2 levels above 200 μg/m3.
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