The Impacts of Climatic Factors and Vegetation on Hemorrhagic Fever with Renal Syndrome Transmission in China: A Study of 109 Counties

Junyu He, Yong Wang, Di Mu, Zhiwei Xu, Quan Qian, Gongbo Chen, Liang Wen, Wenwu Yin, Shanshan Li, Wenyi Zhang, Yuming Guo

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3 Citations (Scopus)

Abstract

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne infectious disease caused by hantaviruses. About 90% of global cases were reported in China. We collected monthly data on counts of HFRS cases, climatic factors (mean temperature, rainfall, and relative humidity), and vegetation (normalized difference vegetation index (NDVI)) in 109 Chinese counties from January 2002 to December 2013. First, we used a quasi-Poisson regression with a distributed lag non-linear model to assess the impacts of these four factors on HFRS in 109 counties, separately. Then we conducted a multivariate meta-analysis to pool the results at the national level. The results of our study showed that there were non-linear associations between the four factors and HFRS. Specifically, the highest risks of HFRS occurred at the 45th, 30th, 20th, and 80th percentiles (with mean and standard deviations of 10.58 ± 4.52 °C, 18.81 ± 17.82 mm, 58.61 ± 6.33%, 198.20 ± 22.23 at the 109 counties, respectively) of mean temperature, rainfall, relative humidity, and NDVI, respectively. HFRS case estimates were most sensitive to mean temperature amongst the four factors, and the lag patterns of the impacts of these factors on HFRS were heterogeneous. Our findings provide rigorous scientific support to current HFRS monitoring and the development of early warning systems.

Original languageEnglish
Article number3434
Number of pages13
JournalInternational Journal of Environmental Research and Public Health
Volume16
Issue number18
DOIs
Publication statusPublished - 16 Sep 2019

Keywords

  • distributed lag non-linear model
  • hantavirus disease
  • meta-analysis
  • orthohantavirus
  • risk map

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