• Record: found
  • Abstract: found
  • Article: found
Is Open Access

The global distribution of Crimean-Congo hemorrhagic fever

Read this article at

      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.



      Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne infection caused by a virus (CCHFV) from the Bunyaviridae family. Domestic and wild vertebrates are asymptomatic reservoirs for the virus, putting animal handlers, slaughter-house workers and agricultural labourers at highest risk in endemic areas, with secondary transmission possible through contact with infected blood and other bodily fluids. Human infection is characterized by severe symptoms that often result in death. While it is known that CCHFV transmission is limited to Africa, Asia and Europe, definitive global extents and risk patterns within these limits have not been well described.


      We used an exhaustive database of human CCHF occurrence records and a niche modeling framework to map the global distribution of risk for human CCHF occurrence.


      A greater proportion of shrub or grass land cover was the most important contributor to our model, which predicts highest levels of risk around the Black Sea, Turkey, and some parts of central Asia. Sub-Saharan Africa shows more focalized areas of risk throughout the Sahel and the Cape region.


      These new risk maps provide a valuable starting point for understanding the zoonotic niche of CCHF, its extent and the risk it poses to humans.

      Related collections

      Most cited references 63

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

      The global distribution and burden of dengue

      Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes 1 . For some patients dengue is a life-threatening illness 2 . There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread 3 . The contemporary worldwide distribution of the risk of dengue virus infection 4 and its public health burden are poorly known 2,5 . Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanisation. Using cartographic approaches, we estimate there to be 390 million (95 percent credible interval 284-528) dengue infections per year, of which 96 million (67-136) manifest apparently (any level of clinical or sub-clinical severity). This infection total is more than three times the dengue burden estimate of the World Health Organization 2 . Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help guide improvements in disease control strategies using vaccine, drug and vector control methods and in their economic evaluation. [285]
        • Record: found
        • Abstract: found
        • Article: not found

        A working guide to boosted regression trees.

        1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
          • Record: found
          • Abstract: found
          • Article: not found

          Ensemble forecasting of species distributions.

          Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

            Author and article information

            [a ]Department of Zoology, University of Oxford, Oxford, UK
            [b ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
            [c ]Department of Pediatrics, Harvard Medical School and Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, USA
            [d ]Livestock Systems and Environment (LSE), International Livestock Research Institute (ILRI),Nairobi, Kenya
            [e ]Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium
            [f ]Fonds National de la Recherche Scientifique, Brussels, Belgium
            [g ]Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
            [h ]Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
            Author notes
            [* ]Corresponding author: Tel: +44 (0) 1865 271 137; E-mail: jane.messina@
            Trans R Soc Trop Med Hyg
            Trans. R. Soc. Trop. Med. Hyg
            Transactions of the Royal Society of Tropical Medicine and Hygiene
            Oxford University Press
            August 2015
            04 July 2015
            04 July 2015
            : 109
            : 8
            : 503-513
            26142451 4501401 10.1093/trstmh/trv050 trv050
            © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

            Funded by: Wellcome Trust
            Award ID: #095066
            Funded by: Bill & Melinda Gates Foundation
            Award ID: #OPP1093011
            Funded by: IDAMS
            Award ID: #21803
            Funded by: Fogarty International Center
            Funded by: National Institutes of Health
            Funded by: National Library of Medicine
            Funded by: National Institutes of Health
            Award ID: 5R01LM010812-05
            Original Articles


            2017-12-21 16:01 UTC
            2017-12-21 12:25 UTC
            2017-12-21 11:45 UTC
            2017-12-21 11:44 UTC
            2017-12-21 11:40 UTC
            2017-12-21 11:40 UTC
            2017-12-21 11:39 UTC

            Comment on this article