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Abstract/Summary
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<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>The recent rise in the transmission of mosquito-borne diseases such as dengue virus (DENV), Zika (ZIKV), chikungunya (CHIKV), Oropouche (OROV), and West Nile (WNV) is a major concern for public health managers worldwide. Emerging technologies for automated remote mosquito classification can be supplemented to improve surveillance systems and provide valuable information regarding mosquito vector catches in real time.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>We coupled an optical sensor to the entrance of a standard mosquito suction trap (BG-Mosquitaire) to record 9151 insect flights in two Brazilian cities: Rio de Janeiro and Brasilia. The traps and sensors remained in the field for approximately 1 year. A total of 1383 mosquito flights were recorded from the target species: <jats:italic>Aedes aegypti</jats:italic> and <jats:italic>Culex quinquefasciatus</jats:italic>. Mosquito classification was based on previous models developed and trained using European populations of <jats:italic>Aedes albopictus</jats:italic> and <jats:italic>Culex pipiens</jats:italic>.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>The VECTRACK sensor was able to discriminate the target mosquitoes (<jats:italic>Aedes</jats:italic> and <jats:italic>Culex</jats:italic> genera) from non-target insects with an accuracy of 99.8%. Considering only mosquito vectors, the classification between <jats:italic>Aedes</jats:italic> and <jats:italic>Culex</jats:italic> achieved an accuracy of 93.7%. The sex classification worked better for <jats:italic>Cx. quinquefasciatus</jats:italic> (accuracy: 95%; specificity: 95.3%) than for <jats:italic>Ae. aegypti</jats:italic> (accuracy: 92.1%; specificity: 88.4%).</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>The data reported herein show high accuracy, sensitivity, specificity and precision of an automated optical sensor in classifying target mosquito species, genus and sex. Similar results were obtained in two different Brazilian cities, suggesting high reliability of our findings. Surprisingly, the model developed for European populations of <jats:italic>Ae. albopictus</jats:italic> worked well for Brazilian <jats:italic>Ae. aegypti</jats:italic> populations, and the model developed and trained for <jats:italic>Cx. pipiens</jats:italic> was able to classify Brazilian <jats:italic>Cx. quinquefasciatus</jats:italic> populations. Our findings suggest this optical sensor can be integrated into mosquito surveillance methods and generate accurate automatic real-time monitoring of medically relevant mosquito species.</jats:p>
</jats:sec><jats:sec>
<jats:title>Graphical Abstract</jats:title>
</jats:sec>
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Hinweis
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DeepGreen-ID: c350c96c8c824c7896fe7ae19b07b358 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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