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      Predicting the geographical distributions of the macaque hosts and mosquito vectors of Plasmodium knowlesi malaria in forested and non-forested areas

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          Abstract

          Background

          Plasmodium knowlesi is a zoonotic pathogen, transmitted among macaques and to humans by anopheline mosquitoes. Information on P. knowlesi malaria is lacking in most regions so the first step to understand the geographical distribution of disease risk is to define the distributions of the reservoir and vector species.

          Methods

          We used macaque and mosquito species presence data, background data that captured sampling bias in the presence data, a boosted regression tree model and environmental datasets, including annual data for land classes, to predict the distributions of each vector and host species. We then compared the predicted distribution of each species with cover of each land class.

          Results

          Fine-scale distribution maps were generated for three macaque host species ( Macaca fascicularis, M. nemestrina and M. leonina) and two mosquito vector complexes (the Dirus Complex and the Leucosphyrus Complex). The Leucosphyrus Complex was predicted to occur in areas with disturbed, but not intact, forest cover (> 60 % tree cover) whereas the Dirus Complex was predicted to occur in areas with 10–100 % tree cover as well as vegetation mosaics and cropland. Of the macaque species, M. nemestrina was mainly predicted to occur in forested areas whereas M. fascicularis was predicted to occur in vegetation mosaics, cropland, wetland and urban areas in addition to forested areas.

          Conclusions

          The predicted M. fascicularis distribution encompassed a wide range of habitats where humans are found. This is of most significance in the northern part of its range where members of the Dirus Complex are the main P. knowlesi vectors because these mosquitoes were also predicted to occur in a wider range of habitats. Our results support the hypothesis that conversion of intact forest into disturbed forest (for example plantations or timber concessions), or the creation of vegetation mosaics, will increase the probability that members of the Leucosphyrus Complex occur at these locations, as well as bringing humans into these areas. An explicit analysis of disease risk itself using infection data is required to explore this further. The species distributions generated here can now be included in future analyses of P. knowlesi infection risk.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13071-016-1527-0) contains supplementary material, which is available to authorized users.

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          Most cited references67

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          High-resolution global maps of 21st-century forest cover change.

          Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.
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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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              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.
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                Author and article information

                Contributors
                catherinemoyes@gmail.com
                Journal
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central (London )
                1756-3305
                28 April 2016
                28 April 2016
                2016
                : 9
                : 242
                Affiliations
                [ ]Spatial Ecology & Epidemiology Group, The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, OX3 7BN UK
                [ ]Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, OX1 3PS UK
                [ ]Department of Social Sciences, Oxford Brookes University, Oxford, OX1 0BP UK
                [ ]Department of Zoology, Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak Malaysia
                [ ]Departments of Zoology and Botany, University of British Columbia, Vancouver, BC V6T 1Z4 Canada
                [ ]Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6T 1Z4 Canada
                [ ]Primate Research Unit, Department of Biology, Faculty of Science, Chulalongkorn University, Bangkok, 10330 Thailand
                [ ]Fauna & Flora International, Singapore, 247672 Singapore
                [ ]Institute for Global Environmental Strategies, Kamiyamaguchi 2108-11, Hayama-cho, 240-0115 Kanagawa Japan
                [ ]Wildlife Conservation Society, Mpala Research Center, Nanyuki, 10400 Kenya
                [ ]Research Institute for the Environment and Livelihoods, Charles Darwin University, Northern Territory, 0909 Australia
                [ ]Faculty of Science and Technology, Federation University Australia, Mt Helen, Victoria 3350 Australia
                [ ]Evolutionary Morphology Section, Primate Research Institute, Kyoto University, Inuyama, Japan
                [ ]Field Conservation Program, S.P.E.C.I.E.S., Ventura, CA USA
                [ ]Conservation Science Program, Tiger Creek Wildlife Refuge, Tyler, TX USA
                [ ]Eijkman-Oxford Clinical Research Unit, Jakarta, Indonesia
                [ ]Department of Parasitology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
                [ ]Department of Entomology, Faculty of Agriculture, Kasetsart University, Bangkok, 10900 Thailand
                [ ]Public Health and Malaria Control Department, International SOS, Jalan Kertajasa, Kuala Kencana, Papua, 99920 Indonesia
                [ ]Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121 USA
                [ ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN UK
                Article
                1527
                10.1186/s13071-016-1527-0
                4850754
                27125995
                3b3e4c7c-2a3b-4fa1-8b3f-9e1976f018e8
                © Moyes et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 9 February 2016
                : 21 April 2016
                Funding
                Funded by: Wellcome Trust (GB)
                Award ID: 095066
                Funded by: FundRef http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
                Award ID: OPP1093011
                Funded by: FundRef http://dx.doi.org/10.13039/501100003093, Ministry of Higher Education, Malaysia;
                Award ID: NRGS/1088/2013(02)
                Award Recipient :
                Funded by: FRGS
                Award ID: 990/2013[31]/1/2013/ STWN10/UNIMAS/02/2
                Award Recipient :
                Funded by: Mohamad bin Zayed Foundation
                Funded by: WILMAR International
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Parasitology
                species distribution model,deforestation
                Parasitology
                species distribution model, deforestation

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