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 and Yuming Guo
Additional contact information
Junyu He: Ocean College, Zhejiang University, Zhoushan 316021, China
Yong Wang: Chinese PLA Center for Disease Control and Prevention, Beijing 100071, China
Di Mu: Division of Infectious Diseases, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Zhiwei Xu: School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland 4059, Australia
Quan Qian: Chinese PLA Center for Disease Control and Prevention, Beijing 100071, China
Gongbo Chen: Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne 3004, Australia
Liang Wen: Chinese PLA Center for Disease Control and Prevention, Beijing 100071, China
Wenwu Yin: Division of Infectious Diseases, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Shanshan Li: Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne 3004, Australia
Wenyi Zhang: Chinese PLA Center for Disease Control and Prevention, Beijing 100071, China
Yuming Guo: Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne 3004, Australia

IJERPH, 2019, vol. 16, issue 18, 1-13

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.

Keywords: orthohantavirus; hantavirus disease; risk map; distributed lag non-linear model; meta-analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/16/18/3434/pdf (application/pdf)
https://www.mdpi.com/1660-4601/16/18/3434/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:16:y:2019:i:18:p:3434-:d:267649

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().