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      Mapping global environmental suitability for Zika virus

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          Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes, which also act as vectors for dengue and chikungunya viruses throughout much of the tropical world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In 2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barré syndrome observed in this outbreak have raised concerns about continued global spread of Zika virus, prompting its declaration as a Public Health Emergency of International Concern by the World Health Organization. We conducted species distribution modelling to map environmental suitability for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable environmental conditions with over 2.17 billion people inhabiting these areas.


          eLife digest

          Zika virus is transmitted between humans by mosquitoes. The majority of infections cause mild flu-like symptoms, but neurological complications in adults and infants have been found in recent outbreaks.

          Although it was discovered in Uganda in 1947, Zika only caused sporadic infections in humans until 2007, when it caused a large outbreak in the Federated States of Micronesia. The virus later spread across Oceania, was first reported in Brazil in 2015 and has since rapidly spread across Latin America. This has led many people to question how far it will continue to spread. There was therefore a need to define the areas where the virus could be transmitted, including the human populations that might be risk in these areas.

          Messina et al. have now mapped the areas that provide conditions that are highly suitable for the spread of the Zika virus. These areas occur in many tropical and sub-tropical regions around the globe. The largest areas of risk in the Americas lie in Brazil, Colombia and Venezuela. Although Zika has yet to be reported in the USA, a large portion of the southeast region from Texas through to Florida is highly suitable for transmission. Much of sub-Saharan Africa (where several sporadic cases have been reported since the 1950s) also presents an environment that is highly suitable for the Zika virus. While no cases have yet been reported in India, a large portion of the subcontinent is also suitable for Zika transmission.

          Over 2 billion people live in Zika-suitable areas globally, and in the Americas alone, over 5.4 million births occurred in 2015 within such areas. It is important, however, to recognize that not all individuals living in suitable areas will necessarily be exposed to Zika.

          We still lack a great deal of basic epidemiological information about Zika. More needs to be known about the species of mosquito that spreads the disease and how the Zika virus interacts with related viruses such as dengue. As such information becomes available and clinical cases become routinely diagnosed, the global evidence base will be strengthened, which will improve the accuracy of future maps.


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          Most cited references 121

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          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]
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            Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

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              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.

                Author and article information

                Role: Reviewing editor
                eLife Sciences Publications, Ltd
                19 April 2016
                : 5
                [1 ]deptDepartment of Zoology , University of Oxford , Oxford, United Kingdom
                [2 ]deptWellcome Trust Centre for Human Genetics , University of Oxford , Oxford, United Kingdom
                [3 ]deptInstitute for Health Metrics and Evaluation , University of Washington , Seattle, United States
                [4 ]deptDepartment of BioSciences , University of Melbourne , Parkville, United Kingdom
                [5 ]deptWorldPop project, Department of Geography and Environment , University of Southampton , Southampton, United Kingdom
                [6 ]deptBoston Children's Hospital , Harvard Medical School , Boston, United Kingdom
                [7 ]deptDepartment of Medicine, Division of Infectious Diseases , University of Toronto , Toronto, Canada
                [8 ]deptLi Ka Shing Knowledge Institute , St Michael's Hospital , Toronto, Canada
                [9 ]Flowminder Foundation , Stockholm, Sweden
                [10 ]deptSection Clinical Tropical Medicine, Department for Infectious Diseases , Heidelberg University Hospital , Heidelberg, Germany
                [11 ]deptGerman Centre for Infection Research (DZIF) , Heidelberg partner site , Heidelberg, Germany
                [12 ]deptSecretariat of Health Surveillance , Ministry of Health Brazil , Brasilia, Brazil
                [13 ]deptDepartment of Entomology and Nematology , University of California Davis , Davis, United States
                London School of Hygiene & Tropical Medicine, and Public Health England , United Kingdom
                London School of Hygiene & Tropical Medicine, and Public Health England , United Kingdom
                Author notes
                [* ]For correspondence: jane.messina@ (JPM);
                © 2016, Messina et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                Funded by: FundRef, European Commission;
                Award ID: 21893
                Award Recipient :
                Funded by: FundRef, Bill and Melinda Gates Foundation;
                Award ID: OPP1053338; OPP1119467; OPP1106023; OPP1093011; OPP1081737; OPP1068048
                Award Recipient :
                Funded by: FundRef, Wellcome Trust;
                Award ID: 095066
                Award Recipient :
                Funded by: FundRef, University of Southampton;
                Award ID: Economic and Social Research Council's Doctoral Training Centre
                Award Recipient :
                Funded by: FundRef, National Institutes of Health;
                Award ID: P01AI098670
                Award Recipient :
                Funded by: FundRef, University of Melbourne;
                Award ID: McKenzie fellowship
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Research Article
                Epidemiology and Global Health
                Microbiology and Infectious Disease
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                A global map of environmental suitability for Zika virus and the estimated population living at potential risk can help refine public health guidelines, travel advisories and intervention strategies at a crucial time in the global emergence of this arbovirus.

                Life sciences

                virus, human, vector-borne disease, disease mapping, zika virus


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