Discrepancies between Aedes aegypti identification in the field and in the laboratory after collection with a sticky trap

Maciel-de-Freitas, Rafael; Lima, Arthur Weiss da Silva; Araújo, Simone Costa; Lima, José Bento Pereira; Galardo, Allan Kardec Ribeiro; Honório, Nildimar Alves; Braga, Ima Aparecida; Coelho, Giovanini Evelim; Codeço, Cláudia Torres; Valle, Denise

doi:10.1590/0074-0276140125

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Previous works 5 – 11 reported limited information, such as the focal length, background, and illumination (wavelength and intensity), when acquiring data. To address the problem, we built a piece of hardware called the Aedes Detector to improve the image acquisition, training and testing processes of the model.…”

Section: Methodsmentioning

confidence: 99%

“…Despite numerous studies demonstrating the ability of a DCNN to provide 80 to 97% accuracy to classify medically important mosquitoes 7 – 11 , 15 , 16 , these results that use a large pretrained model might not be practical when deployed in hardware/devices. We deployed the proposed model in a platform called p5.js visual coding tool written in JavaScript by using the microcomputer of the Aedes Detector to verify the proficiency of the model at classifying a mixture of three different generations of mosquitoes in real time.…”

Section: Introductionmentioning

confidence: 96%

“…Another major bottleneck in the classification of mosquitoes is the condition of the samples. In reality, especially for mosquitoes collected from the field, the markings on the dorsal thorax commonly have some level of disappearance 11 , which increases the difficulties of recognizing and distinguishing closely related interspecies, such as Ae. aegypti and Ae.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Ong

Nair

Isawasan

et al. 2021

Sci Rep

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Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Ong

Nair

Isawasan

et al. 2021

Sci Rep

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“…aegypti captured using the sticky trap MosquiTRAP [24]. However, a later study found that mosquito identification in the laboratory was superior to that performed in locus by trained field agents [25]; divergent results presented by these studies may be due to differences in the way the field agents were trained and qualified.…”

Section: Gravid Mosquito Trapsmentioning

confidence: 99%

New Cost-Benefit of Brazilian Technology for Vector Surveillance Using Trapping System

Eiras¹,

Resende²,

Acebal³

et al. 2019

Malaria

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The recent introduction of chikungunya and Zika virus and their subsequent dispersion in the Americas have encouraged the use of novel technologies for adult Aedes surveillance to improve vector control. In Brazil, two platforms for surveillance of eggs and gravid Aedes aegypti have been developed. First, it consists of using data of sampling of eggs in ovitraps associated with GIS technologies to monitor Aedes spp. populations. Although effective, it is not realistic to use in a largescale epidemic scenario as it requires a large amount of human resources for field and laboratory activities. Second, it consists of trapping female Ae. aegypti citywide at fine spatial and temporal scales for vector surveillance (MI-Aedes) to detect high Aedes infestation areas using a GIS environment and the identification of arbovirusinfected trapped mosquitoes by RT-PCR (MI-Virus platforms). Such integration of continuous vector surveillance and targeting vector control in hotspot areas is cost-effective (less than US$ 1.00/person/year), and it has been shown to reduce mosquito population and prevent dengue transmission. The main advantage of the MI-Aedes platform over traditional mosquito surveillance is the integration of continuous vector monitoring coupled with an information technology platform for near real-time data collection, analysis, and decision-making. The technologies also provide data to model the role of climate on the vector population dynamics.

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“…albopictus in the state of Acre, since 2020 14 , but without an official record of any findings of the immature forms or adults of this species. The methods used for the surveillance of Aedes with medical importance, especially Aedes aegypti , are highly dependent on human operation and skills to identify the mosquito species, both in the field and laboratory; therefore, it is important to invest time and resources in the training and qualification of health agents 15 .…”

Section: Figurementioning

confidence: 99%

First Record of Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the state of Acre, Brazil

Rocha

Cardoso

Souza

et al. 2022

Preprint

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Introduction: Aedes (Stegomyia) albopictus (Skuse, 1854) was reported in Brazil for the first time in 1986, and has displayed successful expansion throughout the Brazilian territory. Methods: During a routine activity to control dengue conducted by the Division of Entomology of the Municipal Health Department in Rio Branco, adults and immatures of Culicidae were collected in a peri-urban area. Results: The Culicidae forms identified indicated that they belonged to the species Ae. albopictus. Conclusions: This is the first official record of the presence of Ae. albopictus in the state of Acre, confirming its current presence in all Brazilian states.

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Discrepancies between Aedes aegypti identification in the field and in the laboratory after collection with a sticky trap

Cited by 5 publications

References 11 publications

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

New Cost-Benefit of Brazilian Technology for Vector Surveillance Using Trapping System

First Record of Aedes (Stegomyia) albopictus (Diptera: Culicidae) in the state of Acre, Brazil

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