The global compendium of Aedes aegypti and Ae. albopictus occurrence

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Abstract

Aedes aegypti and Ae. albopictus are the main vectors transmitting dengue and chikungunya viruses. Despite being pathogens of global public health importance, knowledge of their vectors’ global distribution remains patchy and sparse. A global geographic database of known occurrences of Ae. aegypti and Ae. albopictus between 1960 and 2014 was compiled. Herein we present the database, which comprises occurrence data linked to point or polygon locations, derived from peer-reviewed literature and unpublished studies including national entomological surveys and expert networks. We describe all data collection processes, as well as geo-positioning methods, database management and quality-control procedures. This is the first comprehensive global database of Ae. aegypti and Ae. albopictus occurrence, consisting of 19,930 and 22,137 geo-positioned occurrence records respectively. Both datasets can be used for a variety of mapping and spatial analyses of the vectors and, by inference, the diseases they transmit.

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Most cited references 19

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Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.

Steven Phillips, Miroslav Dudík, Jane Elith … (2009)

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.

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Defining Challenges and Proposing Solutions for Control of the Virus Vector Aedes aegypti

Amy C Morrison, Emily Zielinski-Gutierrez, Thomas W. Scott … (2008)

If done properly, say the authors,Aedes aegypti suppression is a practical method to control urban dengue, yellow fever, and chikungunya viruses.

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Ecology of invasive mosquitoes: effects on resident species and on human health

Steven Juliano, L. Philip Lounibos (2005)

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Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group

ISSN (Electronic): 2052-4463

Publication date (Electronic): 07 July 2015

Publication date Collection: 2015

Volume: 2

Electronic Location Identifier: 150035

Affiliations

[1 ] Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford , South Parks Road, Oxford OX1 3PS, UK

[2 ] Wellcome Trust Centre for Human Genetics,University of Oxford , Oxford, UK

[3 ] Institute for Health Metrics and Evaluation, University of Washington , Seattle, USA

[4 ] Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California , Davis, CA, USA

[5 ] Center for Vectorborne Diseases, University of California , Davis, CA, USA

[6 ] Fogarty International Center, National Institutes of Health , Bethesda, Maryland 20892, USA

[7 ] Department of Microbiology, Immunology and Pathology, Colorado State University , Fort Collins, CO, USA

[8 ] National Dengue Control Program, Ministry of Health , Brasilia, DF, Brazil

[9 ] European Centre for Disease Prevention and Control , Stockholm, Sweden

[10 ] Avia-GIS , Zoersel, Belgium

[11 ] Environmental Research Group Oxford Ltd, Department of Zoology , South Parks Road, Oxford OX1 3PS, UK

[12 ] Eijkman-Oxford Clinical Research Unit , Jakarta, Indonesia

[13 ] Center for Research, Diagnostics and Vaccine Development, Centers for Disease Control , Taipei, Taiwan (ROC)

Author notes

[a ] S.I.H. (email: simon.i.hay@ 123456gmail.com

[]

M.U.G.K. drafted the manuscript with editorial input from J.P.M. and S.I.H. and approval from all authors. M.E.S. coordinated and compiled the data collection. K.A.D., A.M., M.U.G.K. and F.M.S. compiled the data records. M.U.G.K. performed database standardisation and technical validation. C.M.B., C.G.M., R.G.C., G.E.C., I.R.F.E., H.J.T., W.V.B., F.S., G.H., O.J.B. and G.R.W.W. provided additional data and geo-positioning. S.I.H. conceived the database design and advised on standardisation and validation procedures.

Article

Publisher Item ID: sdata201535

DOI: 10.1038/sdata.2015.35

PMC ID: 4493829

PubMed ID: 26175912

SO-VID: 11bf1ef7-be95-4643-82f5-0da832618d3a

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The global compendium of Aedes aegypti and Ae. albopictus occurrence

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Most cited references 19

Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data.

Defining Challenges and Proposing Solutions for Control of the Virus Vector Aedes aegypti

Ecology of invasive mosquitoes: effects on resident species and on human health

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