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      A new malaria vector in Africa: Predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk

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          Significance

          In 2012, an unusual outbreak of malaria occurred in Djibouti City followed by increasingly severe annual outbreaks. Investigations revealed the presence of an Asian mosquito species; Anopheles stephensi, which thrives in urban environments. An. stephensi has since been identified in Ethiopia and Sudan. By combining data for An. stephensi across its full range (Asia, Arabian Peninsula, Horn of Africa) with spatial models that identify the species’ preferred habitat, we provide evidence-based maps predicting the possible African locations where An. stephensi could establish if allowed to spread. Our results suggest over 126 million people in cities across Africa could be at risk. This supports the WHO’s call for targeted An. stephensi control and prioritized surveillance.

          Abstract

          In 2012, an unusual outbreak of urban malaria was reported from Djibouti City in the Horn of Africa and increasingly severe outbreaks have been reported annually ever since. Subsequent investigations discovered the presence of an Asian mosquito species; Anopheles stephensi, a species known to thrive in urban environments. Since that first report, An. stephensi has been identified in Ethiopia and Sudan, and this worrying development has prompted the World Health Organization (WHO) to publish a vector alert calling for active mosquito surveillance in the region. Using an up-to-date database of published locational records for An. stephensi across its full range (Asia, Arabian Peninsula, Horn of Africa) and a set of spatial models that identify the environmental conditions that characterize a species’ preferred habitat, we provide evidence-based maps predicting the possible locations across Africa where An. stephensi could establish if allowed to spread unchecked. Unsurprisingly, due to this species’ close association with man-made habitats, our maps predict a high probability of presence within many urban cities across Africa where our estimates suggest that over 126 million people reside. Our results strongly support the WHO’s call for surveillance and targeted vector control and provide a basis for the prioritization of surveillance.

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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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            A statistical explanation of MaxEnt for ecologists

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              The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015

              Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015 and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542–753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                6 October 2020
                14 September 2020
                14 September 2020
                : 117
                : 40
                : 24900-24908
                Affiliations
                [1] aDepartment of Zoology, University of Oxford , Oxford, United Kingdom, OX1 3SZ;
                [2] bBiodiversity Informatics and Spatial Analysis Department, Royal Botanic Gardens Kew , Richmond, Surrey, United Kingdom, TW9 3DS;
                [3] cBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford , Oxford, United Kingdom, OX3 7LF;
                [4] dDepartment of Vector Biology, Liverpool School of Tropical Medicine , Liverpool, United Kingdom, L3 5QA
                Author notes
                1To whom correspondence may be addressed. Email: marianne.sinka@ 123456zoo.ox.ac.uk or janet.hemingway@ 123456lstmed.ac.uk .

                Edited by Nils Chr. Stenseth, University of Oslo, Norway, and approved July 27, 2020 (received for review March 26, 2020)

                Author contributions: M.E.S. conceived the research; M.E.S. designed the research; M.E.S. performed the research; S.P. contributed primary modelling; N.C.M. updated the South-East Asian vector occurrence database to include all sibling species and data up to 2016.; J.L. provided the background data points used in the model; C.L.M. conceived and created the PAR table; J.H., C.L.M., and K.J.W. provided technical advice; and M.E.S. wrote the paper with assistance from all authors.

                2C.L.M. and K.J.W. contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-7145-3179
                https://orcid.org/0000-0002-8937-7626
                https://orcid.org/0000-0002-4151-9031
                https://orcid.org/0000-0002-3200-7173
                https://orcid.org/0000-0002-8028-4079
                Article
                202003976
                10.1073/pnas.2003976117
                7547157
                32929020
                c4a45d6a-0a52-4e92-8e2e-b8813faf449e
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 9
                Funding
                Funded by: Wellcome 100010269
                Award ID: 108440/Z/15/Z
                Award Recipient : Marianne Sinka Award Recipient : Catherine Moyes
                Funded by: Google 100006785
                Award ID: DFR01520
                Award Recipient : Marianne Sinka Award Recipient : Catherine Moyes
                Categories
                Biological Sciences
                Environmental Sciences
                From the Cover

                vector,urban malaria,ensemble modeling,species distribution model,invasive species

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