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      Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus

      , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 4 , 11 , 4 , 4 , 4 , 4 , 4 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 26 , 27 , 28 , 29 , 17 , 30 , 17 , 31 , 16 , 17 , 2 , 3 , 4 , 32 , 33 , 9 , 34 , 1 , 1 , 35 , 36 , 37 , 38 , 39 , 15 , 1 , 40 , , 4 , , 41

      Nature Microbiology

      Nature Publishing Group UK

      Microbiology, Infectious diseases

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          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

          The global population at risk from mosquito-borne diseases—including dengue, yellow fever, chikungunya and Zika—is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally.

          Abstract

          Statistical mapping techniques provide insights into the spread of two key arbovirus vectors in Europe and the United States, and predict the future distributions of both mosquitoes in response to accelerating urbanization, connectivity and climate change.

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          Most cited references 68

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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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            Grinnellian and Eltonian niches and geographic distributions of species.

            In the recent past, availability of large data sets of species presences has increased by orders of magnitude. This, together with developments in geographical information systems and statistical methods, has enabled scientists to calculate, for thousands of species, the environmental conditions of their distributional areas. The profiles thus obtained are obviously related to niche concepts in the Grinnell tradition, and separated from those in Elton's tradition. I argue that it is useful to define Grinnellian and Eltonian niches on the basis of the types of variables used to calculate them, the natural spatial scale at which they can be measured, and the dispersal of the individuals over the environment. I use set theory notation and analogies derived from population ecology theory to obtain formal definitions of areas of distribution and several types of niches. This brings clarity to several practical and fundamental questions in macroecology and biogeography.
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              Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data

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                Author and article information

                Contributors
                moritz.kraemer@zoo.ox.ac.uk
                sihay@uw.edu
                nick.golding.research@gmail.com
                Journal
                Nat Microbiol
                Nat Microbiol
                Nature Microbiology
                Nature Publishing Group UK (London )
                2058-5276
                4 March 2019
                4 March 2019
                2019
                : 4
                : 5
                : 854-863
                Affiliations
                [1 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Department of Zoology, , University of Oxford, ; Oxford, UK
                [2 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, , Harvard University, ; Boston, MA USA
                [3 ]ISNI 0000 0004 0378 8438, GRID grid.2515.3, Boston Children’s Hospital, ; Boston, MA USA
                [4 ]ISNI 0000000122986657, GRID grid.34477.33, Institute for Health Metrics and Evaluation, , University of Washington, ; Seattle, WA USA
                [5 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Centre for Mathematical Modelling of Infectious Diseases, , London School of Hygiene and Tropical Medicine, ; London, UK
                [6 ]ISNI 0000 0004 0425 469X, GRID grid.8991.9, Department of Infectious Disease Epidemiology, , London School of Hygiene and Tropical Medicine, ; London, UK
                [7 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, School of Geography and the Environment, , University of Oxford, ; Oxford, UK
                [8 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Oxford School of Global and Area Studies, , University of Oxford, ; Oxford, UK
                [9 ]ISNI 0000 0001 2348 0746, GRID grid.4989.c, Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, ; Brussels, Belgium
                [10 ]ISNI 0000 0004 0647 2148, GRID grid.424470.1, Fonds National de la Recherche Scientifique, ; Brussels, Belgium
                [11 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Statistics, , Harvard University, ; Cambridge, MA USA
                [12 ]ISNI 0000000100301493, GRID grid.62562.35, RTI International, ; Washington, DC USA
                [13 ]ISNI 0000 0004 1936 8868, GRID grid.4563.4, Epidemiology and Public Health Division, School of Medicine, , University of Nottingham, ; Nottingham, UK
                [14 ]ISNI 0000 0001 2168 0066, GRID grid.131063.6, Department of Biological Sciences and Eck Institute for Global Health, , University of Notre Dame, ; Notre Dame, IN USA
                [15 ]ISNI 0000 0004 0369 313X, GRID grid.419897.a, School of Health, Fudan University, Key Laboratory of Public Health Safety, , Ministry of Education, ; Shanghai, China
                [16 ]ISNI 0000 0004 1936 9297, GRID grid.5491.9, Department of Geography and Environment, , University of Southampton, ; Southampton, UK
                [17 ]GRID grid.475139.d, Flowminder Foundation, ; Stockholm, Sweden
                [18 ]ISNI 0000 0001 0379 7164, GRID grid.216417.7, School of Business, , Central South University, ; Changsha, China
                [19 ]ISNI 0000 0000 9548 2110, GRID grid.412110.7, College of Systems Engineering, , National University of Defense Technology, ; Changsha, China
                [20 ]GRID grid.443347.3, School of Business Administration, , Southwestern University of Finance and Economics, ; Chengdu, China
                [21 ]Waen Associates Ltd, Y Waen, Islaw’r Dref, Dolgellau, Gwynedd, UK
                [22 ]ISNI 0000 0001 0505 4321, GRID grid.4437.4, Pan American Health Organization (PAHO), ; Washington, DC USA
                [23 ]ISNI 0000 0004 0602 9808, GRID grid.414596.b, National Dengue Control Program, , Ministry of Health, ; Brasilia, Brazil
                [24 ]ISNI 0000 0004 1791 8889, GRID grid.418914.1, European Centre for Disease Prevention and Control, ; Stockholm, Sweden
                [25 ]ISNI 0000 0001 2153 5088, GRID grid.11505.30, Institute of Tropical Medicine, ; Antwerp, Belgium
                [26 ]GRID grid.423833.d, Avia-GIS, ; Zoersel, Belgium
                [27 ]Francis Schaffner Consultancy, Riehen, Switzerland
                [28 ]ISNI 0000 0004 1936 8083, GRID grid.47894.36, Department of Microbiology, Immunology, and Pathology, , Colorado State University, ; Fort Collins, CO USA
                [29 ]ISNI 0000 0001 2156 2780, GRID grid.5801.c, Computational Social Science, , ETH Zurich, ; Zurich, Switzerland
                [30 ]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Public Health Sciences, , Karolinska Institutet, ; Stockholm, Sweden
                [31 ]ISNI 0000 0001 1214 1861, GRID grid.419684.6, Stockholm School of Economics, ; Stockholm, Sweden
                [32 ]ISNI 0000 0001 2353 6535, GRID grid.428999.7, Insect–Virus Interactions Unit, , Institut Pasteur, CNRS, ; UMR2000 Paris, France
                [33 ]ISNI 0000 0001 2353 6535, GRID grid.428999.7, Mathematical Modelling of Infectious Diseases Unit, , Institut Pasteur, CNRS, ; UMR2000 Paris, France
                [34 ]ISNI 0000 0001 2242 8479, GRID grid.6520.1, Department of Geography, , Universite de Namur, ; Namur, Belgium
                [35 ]ISNI 0000 0004 1936 9684, GRID grid.27860.3b, Department of Entomology and Nematology, , University of California, Davis, ; Davis, CA USA
                [36 ]ISNI 0000 0000 8803 2373, GRID grid.198530.6, State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, , Chinese Center for Disease Control and Prevention, Changping, ; Beijing, China
                [37 ]ISNI 0000 0004 1761 1174, GRID grid.27255.37, Shandong University Climate Change and Health Center, School of Public Health, , Shandong University, Jinan, ; Shandong, China
                [38 ]WHO Collaborating Centre for Vector Surveillance and Management, Beijing, China
                [39 ]Chongqing Centre for Disease Control and Prevention, Chongqing, China
                [40 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Environmental Research Group Oxford (ERGO), Department of Zoology, , Oxford University, ; Oxford, UK
                [41 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, School of BioSciences, , University of Melbourne, ; Parkville, Victoria Australia
                376
                10.1038/s41564-019-0376-y
                6522366
                30833735
                © The Author(s), under exclusive licence to Springer Nature Limited 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                © The Author(s), under exclusive licence to Springer Nature Limited 2019

                infectious diseases, microbiology

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