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      Spatial prediction of risk areas for vector transmission of Trypanosoma cruzi in the State of Paraná, southern Brazil

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          Abstract

          After obtaining certification of the absence of transmission of the Trypanosoma cruzi by Triatoma infestans in 2006, other native species of protozoan vectors have been found in human dwellings within municipalities of the State of Paraná, Southern Brazil. However, the spatial distribution of T. cruzi vectors and how climatic and landscape combined variables explain the distribution are still poorly understood. The goal of this study was to predict the potential distribution of T. cruzi vectors as a proxy for Chagas disease transmission risk using Ecological Niche Models (ENMs) based on climatic and landscape variables. We hypothesize that ENM based on both climate and landscape variables are more powerful than climate-only or landscape-only models, and that this will be true independent of vector species. A total of 2,662 records of triatomines of five species were obtained by community-based entomological surveillance from 2007 to 2013. The species with the highest number of specimens was Panstrongylus megistus (73%; n = 1,943), followed by Panstrongylus geniculatus (15.4%; 411), Rhodnius neglectus (6.0%; 159), Triatoma sordida (4.5%; 119) and Rhodnius prolixus (1.1%; 30). Of the total, 71.9% were captured at the intradomicile. T. cruzi infection was observed in 19.7% of the 2,472 examined insects. ENMs were generated based on selected climate and landscape variables with 1 km 2 spatial resolution. Zonal statistics were used for classifying the municipalities as to the risk of occurrence of synanthropic triatomines. The integrated analysis of the climate and landscape suitability on triatomines geographical distribution was powerful on generating good predictive models. Moreover, this showed that some municipalities in the northwest, north and northeast of the Paraná state have a higher risk of T. cruzi vector transmission. This occurs because those regions present high climatic and landscape suitability values for occurrence of their vectors. The frequent invasion of houses by infected triatomines clearly indicates a greater risk of transmission of T. cruzi to the inhabitants. More public health attention should be given in the northern areas of the State of Paraná, which presents high climate and landscape suitabilities for the disease vectors. In conclusion, our results–through spatial analysis and predictive maps–showed to be effective in identifying areas of potential distribution and, consequently, in the definition of strategic areas and actions to prevent new cases of Chagas' disease, reinforcing the need for continuous and robust surveillance in these areas.

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          Trypanosoma cruzi, the etiologic agent of Chagas' disease (CD), circulates among humans, vectors and domestic and wild reservoirs. Frequent invasion of housing by infected triatomines may present a risk of vector transmission from T. cruzi to humans. The objective of this study was to predict the potential distribution of triatomines in the State of Paraná and to infer potential high risk areas for T. cruzi vector transmission. Five distinct species of three different genera of triatomines were collected, the majority of which were Panstrongylus megistus (73% of occurrences). More than half of collected specimens (54.8%) were captured intradomicile. The infection rate was 24.7%, which may represent a risk of occurrence of new cases of CD by vector transmission. Ecological Niche Models indicated suitable areas to triatomine occurrence in municipalities of Northwest, North and Northeast of Paraná state. Thus, more effective management actions are needed to ensure epidemiological surveillance of CD. P. megistus is the main species that colonizes the intradomicile, reinforcing the importance of the maintenance of surveillance for directing vector control actions of the CD in the local health systems in this and similar regions, mainly in the northern region of the State of Paraná, with the intensive participation of the community and also of municipal agents.

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          Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change

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            On the selection of thresholds for predicting species occurrence with presence‐only data

            Abstract Presence‐only data present challenges for selecting thresholds to transform species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (maxSSS and maxF pb) and examine the effectiveness of the threshold‐based prevalence estimation approach. Six virtual species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence‐only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods maxSSS and max F pb with four presence‐only datasets with different ratios of the number of known presences to the number of random points (KP–RP ratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with maxF pb varied as the KP–RP ratio of the threshold selection datasets changed. Datasets with the KP–RP ratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxFpb had specificity too low for very common species using Random Forest and Maxent models. In contrast, maxSSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that maxF pb is affected by the KP–RP ratio of the threshold selection datasets, but maxSSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold‐based approach.
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              Biogeografia, origem e distribuição da domiciliação de triatomíneos no Brasil

              Considerando a distribuição atual da domicialiação triatomínea no Brasil, pode-se verificar sua associação com a referente aos espaços abertos. Estes podem ser considerados como naturais e artificiais. Os primeiros estão essencialmente situados nos domínios paisagísticos das caatingas, dos cerrados, totalmente incluídos em território brasileiro, e das pradarias mistas subtropicais pertencentes a paisagem que se estende além desses limites. Os outros são os originados da ação antrópica resultando em expansão da paisagem aberta principalmente em virtude da devastação da cobertura florestal do domínio tropical atlântico. Embora tentativamente, procurou-se aplicar o modelo de refúgios paleoecológicos e da existência de centros de endemismo às quatro espécies de domiciliação epidemiologicamente significante. O Triatoma sordida, Triatoma brasiliensis e Triatoma pseudomaculata parece ter seus centros de endemismo nos espaços abertos dos cerrados e das caatingas, enquanto o Panstrongylus megistus teria tido sua origem nas florestas do ambiente tropical atlântico. Quanto ao Triatoma infestans, sua área endêmica estaria localizada em território boliviano, de onde se dispersou e continua se dispersando pela ação do homem. A invasão domiciliar, ao que tudo indica, obedece a mecanismo oportunista propiciado por vários estímulos de abrigo e alimentação. Uma vez instalada, a domiciliação permite não apenas a sobrevivência, mas também a dispersão da espécie. Esses conceitos devem ser levados em conta nas campanhas de controle, uma vez que a probabilidade de sucesso aumenta com essa especialização do triatomíneo. A continuidade da ação antrópica sobre o ambiente, atualmente intensificada na região do domínio equatorial amazônico, resultará na expansão dos espaços abertos. Assim sendo, seja a custa de espécies locais seja a custa de espécies introduzidas, poderá ocorrer a domiciliação triatomínea, como problema de saúde pública, em região onde ainda não foi assinalada.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Software
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Methodology
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Software
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Software
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Supervision
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Supervision
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Methodology
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                plosntds
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, CA USA )
                1935-2727
                1935-2735
                26 October 2018
                October 2018
                : 12
                : 10
                : e0006907
                Affiliations
                [1 ] Postgraduate Program in Health Sciences, Health Sciences Center, State University of Maringá, Maringá, Paraná, Brazil
                [2 ] Spatial Ecology and Conservation lab (LEEC), Department of Ecology, Institute of Biosciences, São Paulo State University, Rio Claro, São Paulo, Brazil
                [3 ] Department of Nursing, Health Sciences Center, State University of Maringá, Maringá, Paraná, Brazil
                [4 ] Department of Medicine, Health Sciences Center, State University of Maringá, Maringá, Paraná, Brazil
                [5 ] Department of Basic Health Sciences, Health Sciences Center, State University of Maringá, Maringá, Paraná, Brazil
                Yale University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9650-7575
                http://orcid.org/0000-0002-6466-6210
                http://orcid.org/0000-0002-4312-202X
                http://orcid.org/0000-0001-6314-8668
                Article
                PNTD-D-18-00516
                10.1371/journal.pntd.0006907
                6221357
                30365486
                661746c4-6731-4f40-8a5a-c9055b6c3e2f
                © 2018 Ferro e Silva et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 April 2018
                : 8 October 2018
                Page count
                Figures: 5, Tables: 1, Pages: 17
                Funding
                Funded by: Araucária Foundation for Scientific and Technological Development
                Award ID: #10.943.812
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: #305.853/2014-7
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: #150319/2017-7
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: #2015/17739-4
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: #2017/21816-0
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: #2017/09676-8
                Award Recipient :
                Araucária Foundation for Scientific and Technological Development, grant #10.943.812, 251/2014; National Council for Scientific and Technological Development (CNPq), grants #305.853/2014-7 and #150319/2017-7; São Paulo Research Foundation (FAPESP), grants #2015/17739-4, #2017/21816-0 and #2017/09676-8. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Protozoans
                Parasitic Protozoans
                Trypanosoma
                Trypanosoma Cruzi
                People and places
                Geographical locations
                South America
                Brazil
                Biology and Life Sciences
                Population Biology
                Population Dynamics
                Geographic Distribution
                Biology and Life Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Forests
                Ecology and Environmental Sciences
                Terrestrial Environments
                Forests
                Medicine and Health Sciences
                Tropical Diseases
                Neglected Tropical Diseases
                Chagas Disease
                Medicine and Health Sciences
                Parasitic Diseases
                Protozoan Infections
                Chagas Disease
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Invertebrates
                Arthropoda
                Insects
                Research and Analysis Methods
                Simulation and Modeling
                Climate Modeling
                Earth Sciences
                Atmospheric Science
                Climatology
                Climate Modeling
                Medicine and Health Sciences
                Infectious Diseases
                Disease Vectors
                Triatoma
                Biology and Life Sciences
                Species Interactions
                Disease Vectors
                Triatoma
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-11-07
                All relevant data are within the paper and its Supporting Information files.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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