Evidence map›Paper›PMID 39358773›Full record

ArticleParasites & vectors2024

An easier life to come for mosquito researchers: field-testing across Italy supports VECTRACK system for automatic counting, identification and absolute density estimation of Aedes albopictus and Culex pipiens adults.

Martina Micocci, Mattia Manica, Ilaria Bernardini, Laura Soresinetti, Marianna Varone, Paola Di Lillo, Beniamino Caputo, Piero Poletti, Francesco Severini, Fabrizio Montarsi and 3 more

Abstract read
In one paragraph

Article in Parasites & vectors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Horizons of the Future: Preparedness and Response.Current topics in microbiology and immunology · 2026
    Review
  4. High-resolution characterisation of diel activity rhythms inCurrent research in parasitology & vector-borne diseases · 2026
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Martina Micocci *Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy.
Mattia Manica *Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Ilaria BernardiniDepartment of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy.
Laura SoresinettiDepartment of Biosciences and Pediatric Clinical Research Center "Romeo Ed Enrica Invernizzi", University of Milan, Milan, Italy.
Marianna VaroneDepartment of Biology, University of Naples Federico II, Naples, Italy.
Paola Di LilloDepartment of Biology, University of Naples Federico II, Naples, Italy.
Beniamino CaputoDepartment of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy.
Piero PolettiCenter for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Francesco SeveriniDepartment of Infectious Diseases, Istituto Superiore Di Sanità, Rome, Italy.
Fabrizio MontarsiIstituto Zooprofilattico Sperimentale Delle Venezie, Legnaro, Italy.
Sara EpisDepartment of Biosciences and Pediatric Clinical Research Center "Romeo Ed Enrica Invernizzi", University of Milan, Milan, Italy.
Marco SalveminiDepartment of Biology, University of Naples Federico II, Naples, Italy.
Alessandra Della TorreDepartment of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy. alessandra.dellatorre@uniroma1.it.

Funding

Ministero dell'Università e della Ricerca (Italy), PNRR PE00000007Ministero dell'Università e della Ricerca (Italy), PON Research and Innovation 2014-2020 DOT1326YBR-1
6 · The paper itself

Abstract

backgroundDisease-vector mosquito monitoring is an essential prerequisite to optimize control interventions and evidence-based risk predictions. However, conventional entomological monitoring methods are labor- and time-consuming and do not allow high temporal/spatial resolution. In 2022, a novel system coupling an optical sensor with machine learning technologies (VECTRACK) proved effective in counting and identifying Aedes albopictus and Culex pipiens adult females and males. Here, we carried out the first extensive field evaluation of the VECTRACK system to assess: (i) whether the catching capacity of a commercial BG-Mosquitaire trap (BGM) for adult mosquito equipped with VECTRACK (BGM + VECT) was affected by the sensor; (ii) the accuracy of the VECTRACK algorithm in correctly classifying the target mosquito species genus and sex; (iii) Ae. albopictus capture rate of BGM with or without VECTRACK.

methodsThe same experimental design was implemented in four areas in northern (Bergamo and Padua districts), central (Rome) and southern (Procida Island, Naples) Italy. In each area, three types of traps-one BGM, one BGM + VECT and the combination of four sticky traps (STs)-were rotated each 48 h in three different sites. Each sampling scheme was replicated three times/area. Collected mosquitoes were counted and identified by both the VECTRACK algorithm and operator-mediated morphological examination. The performance of the VECTRACK system was assessed by generalized linear mixed and linear regression models. Aedes albopictus capture rates of BGMs were calculated based on the known capture rate of ST.

resultsA total of 3829 mosquitoes (90.2% Ae. albopictus) were captured in 18 collection-days/trap/site. BGM and BGM + VECT showed a similar performance in collecting target mosquitoes. Results show high correlation between visual and automatic identification methods (Spearman Ae. albopictus: females = 0.97; males = 0.89; P < 0.0001) and low count errors. Moreover, the results allowed quantifying the heterogeneous effectiveness associated with different trap types in collecting Ae. albopictus and predicting estimates of its absolute density.

conclusionsObtained results strongly support the VECTRACK system as a powerful tool for mosquito monitoring and research, and its applicability over a range of ecological conditions, accounting for its high potential for continuous monitoring with minimal human effort.

Indexed as

AedesCulexMosquito ControlMosquito VectorsAnimalsFemaleItalyMachine LearningMalePopulation DensityAedes albopictusAutomatic identificationCapture RateCulex pipiensGenus and sex classificationMachine learningMosquito monitoringMosquito trapOptical sensor

Identifiers

PMID39358773
PMCPMC11448096

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.