For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
46
views
37
references
 Top references
cited by
53
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      98
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Climate Variability and Hemorrhagic Fever with Renal Syndrome Transmission in Northeastern China

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          The transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by climatic variables. However, few studies have examined the quantitative relationship between climate variation and HFRS transmission.

          Objective

          We examined the potential impact of climate variability on HFRS transmission and developed climate-based forecasting models for HFRS in northeastern China.

          Methods

          We obtained data on monthly counts of reported HFRS cases in Elunchun and Molidawahaner counties for 1997–2007 from the Inner Mongolia Center for Disease Control and Prevention and climate data from the Chinese Bureau of Meteorology. Cross-correlations assessed crude associations between climate variables, including rainfall, land surface temperature (LST), relative humidity (RH), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI) and monthly HFRS cases over a range of lags. We used time-series Poisson regression models to examine the independent contribution of climatic variables to HFRS transmission.

          Results

          Cross-correlation analyses showed that rainfall, LST, RH, and MEI were significantly associated with monthly HFRS cases with lags of 3–5 months in both study areas. The results of Poisson regression indicated that after controlling for the autocorrelation, seasonality, and long-term trend, rainfall, LST, RH, and MEI with lags of 3–5 months were associated with HFRS in both study areas. The final model had good accuracy in forecasting the occurrence of HFRS.

          Conclusions

          Climate variability plays a significant role in HFRS transmission in northeastern China. The model developed in this study has implications for HFRS control and prevention.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: found
          • Article: not found

          Potential effect of population and climate changes on global distribution of dengue fever: an empirical model.

          Simon Hales, Neil de Wet, John Maindonald … (2002)
          Existing theoretical models of the potential effects of climate change on vector-borne diseases do not account for social factors such as population increase, or interactions between climate variables. Our aim was to investigate the potential effects of global climate change on human health, and in particular, on the transmission of vector-borne diseases. We modelled the reported global distribution of dengue fever on the basis of vapour pressure, which is a measure of humidity. We assessed changes in the geographical limits of dengue fever transmission, and in the number of people at risk of dengue by incorporating future climate change and human population projections into our model. We showed that the current geographical limits of dengue fever transmission can be modelled with 89% accuracy on the basis of long-term average vapour pressure. In 1990, almost 30% of the world population, 1.5 billion people, lived in regions where the estimated risk of dengue transmission was greater than 50%. With population and climate change projections for 2085, we estimate that about 5-6 billion people (50-60% of the projected global population) would be at risk of dengue transmission, compared with 3.5 billion people, or 35% of the population, if climate change did not happen. We conclude that climate change is likely to increase the area of land with a climate suitable for dengue fever transmission, and that if no other contributing factors were to change, a large proportion of the human population would then be put at risk.
          Article 0 comments Cited 310 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Hantaviruses: a global disease problem.

            C Schmaljohn, B Hjelle (1997)
            Hantaviruses are carried by numerous rodent species throughout the world. In 1993, a previously unknown group of hantaviruses emerged in the United States as the cause of an acute respiratory disease now termed hantavirus pulmonary syndrome (HPS). Before than, hantaviruses were known as the etiologic agents of hemorrhagic fever with renal syndrome, a disease that occurs almost entirely in the Eastern Hemisphere. Since the discovery of the HPS-causing hantaviruses, intense investigation of the ecology and epidemiology of hantaviruses has led to the discovery of many other novel hantaviruses. Their ubiquity and potential for causing severe human illness make these viruses an important public health concern; we reviewed the distribution, ecology, disease potential, and genetic spectrum.
            Article 0 comments Cited 206 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Why large-scale climate indices seem to predict ecological processes better than local weather.

              T B Hallett, T Coulson, J Pilkington … (2004)
              Large-scale climatic indices such as the North Atlantic Oscillation are associated with population dynamics, variation in demographic rates and values of phenotypic traits in many species. Paradoxically, these large-scale indices can seem to be better predictors of ecological processes than local climate. Using detailed data from a population of Soay sheep, we show that high rainfall, high winds or low temperatures at any time during a 3-month period can cause mortality either immediately or lagged by a few days. Most measures of local climate used by ecologists fail to capture such complex associations between weather and ecological process, and this may help to explain why large-scale, seasonal indices of climate spanning several months can outperform local climatic factors. Furthermore, we show why an understanding of the mechanism by which climate influences population ecology is important. Through simulation we demonstrate that the timing of bad weather within a period of mortality can have an important modifying influence on intraspecific competition for food, revealing an interaction between climate and density dependence that the use of large-scale climatic indices or inappropriate local weather variables might obscure.
              Article 0 comments Cited 195 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Environ Health Perspect
                Title: Environmental Health Perspectives
                Publisher: National Institute of Environmental Health Sciences
                ISSN (Print): 0091-6765
                ISSN (Electronic): 1552-9924
                Publication date (Print): July 2010
                Publication date (Electronic): 8 February 2010
                Volume: 118
                Issue: 7
                Pages: 915-920
                Affiliations
                [1 ] State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
                [2 ] Inner Mongolia Center for Disease Control and Prevention, Hohhot, China
                [3 ] Department of Statistics, School of Public Health, Tianjin Medical University, Tianjin, China
                [4 ] Discipline of Public Health, University of Adelaide, Adelaide, Australia
                [5 ] Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
                [6 ] Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
                [7 ] School of Public Health, Queensland University of Technology, Queensland, Australia
                Author notes
                Address correspondence to W. Cao, Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogen and Biosecurity, 20 Dong-Da St., Fengtai District, Beijing 100071, China. Telephone: 86-10-63896082. Fax: 86-10-63896082. E-mail: caowc@ 123456nic.bmi.ac.cn

                The authors declare they have no actual or potential competing financial interests.

                Article
                Publisher ID: ehp-118-915
                DOI: 10.1289/ehp.0901504
                PMC ID: 2920909
                PubMed ID: 20142167
                SO-VID: d5033bf0-eca4-4b00-8b1b-1caa45cceaa1
                Copyright © This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.
                History
                Date received : 24 September 2009
                Date accepted : 8 February 2010
                Categories
                Subject: Research

                ScienceOpen disciplines: Public health
                Keywords: time-series poisson regression,cross-correlation,china,hemorrhagic fever with renal syndrome,forecast,risk factors
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: time-series poisson regression, cross-correlation, china, hemorrhagic fever with renal syndrome, forecast, risk factors

                Comments

                Comment on this article

                scite_

                Similar content98

                See all similar

                Cited by53

                See all cited by

                Most referenced authors648

                See all reference authors