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      Addressing the global snakebite crisis with geo-spatial analyses – Recent advances and future direction

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          Abstract

          Venomous snakebite is a neglected tropical disease that annually leads to hundreds of thousands of deaths or long-term physical and mental ailments across the developing world. Insufficient data on spatial variation in snakebite risk, incidence, human vulnerability, and accessibility of medical treatment contribute substantially to ineffective on-ground management. There is an urgent need to collect data, fill knowledge gaps and address on-ground management problems. The use of novel, and transdisciplinary approaches that take advantage of recent advances in spatio-temporal models, ‘big data’, high performance computing, and fine-scale spatial information can add value to snakebite management by strategically improving our understanding and mitigation capacity of snakebite. We review the background and recent advances on the topic of snakebite related geospatial analyses and suggest avenues for priority research that will have practical on-ground applications for snakebite management and mitigation. These include streamlined, targeted data collection on snake distributions, snakebites, envenomings, venom composition, health infrastructure, and antivenom accessibility along with fine-scale models of spatio-temporal variation in snakebite risk and incidence, intraspecific venom variation, and environmental change modifying human exposure. These measures could improve and ‘future-proof’ antivenom production methods, antivenom distribution and stockpiling systems, and human-wildlife conflict management practices, while simultaneously feeding into research on venom evolution, snake taxonomy, ecology, biogeography, and conservation.

          Highlights

          • Many knowledge gaps remain on snake distributions and spatial snakebite variation.

          • Targeted data collection and high resolution spatial models are needed.

          • Maps of snake distributions, bite incidence, and vulnerable populations are needed.

          • Area-specific antivenom delivery requires studies on spatial venom variation.

          • Human welfare and snake conservation require spatial management of conflict.

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          WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas

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            On the Relationship between Abundance and Distribution of Species

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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
                Journal
                Toxicon X
                Toxicon X
                Toxicon: X
                Elsevier
                2590-1710
                31 July 2021
                September 2021
                31 July 2021
                : 11
                : 100076
                Affiliations
                [a ]Division of Data, Analytics and Delivery for Impact (DDI), World Health Organization, Geneva, Switzerland
                [b ]Australian Institute of Tropical Health and Medicine, Division of Tropical Health and Medicine, James Cook University, Cairns, Australia
                [c ]GeoHealth Group, Institute of Global Health, Faculty of Medicine, University of Geneva, Geneva, Switzerland
                [d ]Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
                [e ]Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom
                [f ]Centre for Health Informatics, Computing and Statistics, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
                [g ]Research Group in Mathematical and Computational Biology (BIOMAC), Department of Biomedical Engineering, University of Los Andes, Bogotá, Colombia
                [h ]School of Biology, College of Science, University of Tehran, Iran
                [i ]MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, UK
                [j ]MRC Unit the Gambia at London School of Hygiene and Tropical Medicine, Atlantic Blvd, Fajara, Gambia
                [k ]Health Data Science Unit, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
                Author notes
                []Corresponding author. Australian Institute of Tropical Health and Medicine, Division of Tropical Health and Medicine , James Cook University, 1/14McGregor Road, Smithfield, QLD 4870, Australia. pintora@ 123456who.int
                Article
                S2590-1710(21)00012-6 100076
                10.1016/j.toxcx.2021.100076
                8350508
                34401744
                dd8d9894-af3c-4aa7-9775-06b1695890a7
                © 2021 Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/).

                History
                : 29 April 2021
                : 13 July 2021
                : 14 July 2021
                Categories
                Article from A trans-disciplinary view of snakebite envenoming, Edited by: Dr. Rafael Ruiz de Castañeda, Dr. Isabelle Bolon and Dr. Jose Maria Gutiérrez

                snakebite incidence,envenomings,neglected tropical diseases,spatio-temporal epidemiology,medically relevant snakes,species distribution models

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