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High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery

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dc.contributor.author Carrasco Escobar, Gabriel
dc.contributor.author Manrique, Edgar
dc.contributor.author Ruiz Cabrejos, Jorge
dc.contributor.author Saavedra Romero, Marlon Pierino
dc.contributor.author Alava, Freddy
dc.contributor.author Bickersmith, Sara
dc.contributor.author Prussing, Catharine
dc.contributor.author Vinetz, Joseph Michael
dc.contributor.author Conn, Jan E.
dc.contributor.author Moreno, Marta
dc.contributor.author Gamboa Vilela, Dionicia Baziliza
dc.date.accessioned 2019-07-04T17:00:15Z
dc.date.available 2019-07-04T17:00:15Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/20.500.12866/6817
dc.description.abstract Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions. en_US
dc.language.iso eng
dc.publisher Public Library of Science
dc.relation.ispartofseries PLoS Neglected Tropical Diseases
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject accuracy en_US
dc.subject algorithm en_US
dc.subject animal en_US
dc.subject Animals en_US
dc.subject Anopheles en_US
dc.subject Anopheles darlingi en_US
dc.subject Article en_US
dc.subject breeding en_US
dc.subject climate change en_US
dc.subject clinical article en_US
dc.subject cohort analysis en_US
dc.subject community care en_US
dc.subject comparative study en_US
dc.subject controlled study en_US
dc.subject disease transmission en_US
dc.subject drone en_US
dc.subject drought en_US
dc.subject ecosystem en_US
dc.subject Ecosystem en_US
dc.subject Entomology en_US
dc.subject environmental protection en_US
dc.subject female en_US
dc.subject Female en_US
dc.subject fluorescence imaging en_US
dc.subject food security en_US
dc.subject geographic mapping en_US
dc.subject growth, development and aging en_US
dc.subject habitat en_US
dc.subject health care survey en_US
dc.subject human en_US
dc.subject image processing en_US
dc.subject Image Processing, Computer-Assisted en_US
dc.subject intervention study en_US
dc.subject larval development en_US
dc.subject malaria en_US
dc.subject malaria falciparum en_US
dc.subject male en_US
dc.subject microorganism detection en_US
dc.subject microscopy en_US
dc.subject mosquito control en_US
dc.subject mosquito vector en_US
dc.subject Mosquito Vectors en_US
dc.subject nonhuman en_US
dc.subject normalized difference vegetation index en_US
dc.subject Optical Imaging en_US
dc.subject performance en_US
dc.subject Peru en_US
dc.subject Plasmodium vivax malaria en_US
dc.subject predictive value en_US
dc.subject procedures en_US
dc.subject proof of concept en_US
dc.subject Proof of Concept Study en_US
dc.subject scoring system en_US
dc.subject sensitivity analysis en_US
dc.subject sensitivity and specificity en_US
dc.subject spatial autocorrelation analysis en_US
dc.subject training en_US
dc.subject validation process en_US
dc.subject water management en_US
dc.subject zoology en_US
dc.title High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1371/journal.pntd.0007105
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.03.06
dc.relation.issn 1935-2735


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