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      Global mapping of infectious disease

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          Abstract

          The primary aim of this review was to evaluate the state of knowledge of the geographical distribution of all infectious diseases of clinical significance to humans. A systematic review was conducted to enumerate cartographic progress, with respect to the data available for mapping and the methods currently applied. The results helped define the minimum information requirements for mapping infectious disease occurrence, and a quantitative framework for assessing the mapping opportunities for all infectious diseases. This revealed that of 355 infectious diseases identified, 174 (49%) have a strong rationale for mapping and of these only 7 (4%) had been comprehensively mapped. A variety of ambitions, such as the quantification of the global burden of infectious disease, international biosurveillance, assessing the likelihood of infectious disease outbreaks and exploring the propensity for infectious disease evolution and emergence, are limited by these omissions. An overview of the factors hindering progress in disease cartography is provided. It is argued that rapid improvement in the landscape of infectious diseases mapping can be made by embracing non-conventional data sources, automation of geo-positioning and mapping procedures enabled by machine learning and information technology, respectively, in addition to harnessing labour of the volunteer ‘cognitive surplus’ through crowdsourcing.

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          Most cited references 62

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          Global trends in emerging infectious diseases

          The next new disease Emerging infectious diseases are a major threat to health: AIDS, SARS, drug-resistant bacteria and Ebola virus are among the more recent examples. By identifying emerging disease 'hotspots', the thinking goes, it should be possible to spot health risks at an early stage and prepare containment strategies. An analysis of over 300 examples of disease emerging between 1940 and 2004 suggests that these hotspots can be accurately mapped based on socio-economic, environmental and ecological factors. The data show that the surveillance effort, and much current research spending, is concentrated in developed economies, yet the risk maps point to developing countries as the more likely source of new diseases. Supplementary information The online version of this article (doi:10.1038/nature06536) contains supplementary material, which is available to authorized users.
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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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              Emerging fungal threats to animal, plant and ecosystem health.

              The past two decades have seen an increasing number of virulent infectious diseases in natural populations and managed landscapes. In both animals and plants, an unprecedented number of fungal and fungal-like diseases have recently caused some of the most severe die-offs and extinctions ever witnessed in wild species, and are jeopardizing food security. Human activity is intensifying fungal disease dispersal by modifying natural environments and thus creating new opportunities for evolution. We argue that nascent fungal infections will cause increasing attrition of biodiversity, with wider implications for human and ecosystem health, unless steps are taken to tighten biosecurity worldwide.
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                Author and article information

                Journal
                Philos Trans R Soc Lond B Biol Sci
                Philos. Trans. R. Soc. Lond., B, Biol. Sci
                RSTB
                royptb
                Philosophical Transactions of the Royal Society B: Biological Sciences
                The Royal Society
                0962-8436
                1471-2970
                19 March 2013
                19 March 2013
                : 368
                : 1614 , Discussion Meeting Issue 'Next-generation molecular and evolutionary epidemiology of infectious disease' compiled and edited by O. G. Pybus, C. Fraser and A. Rambaut
                rstb20120250
                10.1098/rstb.2012.0250
                3679597
                23382431

                © 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.

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                March 19, 2013

                Philosophy of science

                public health, crowdsourcing, cartography, biosurveillance, atlas, surveillance

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