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.
Aedes albopictus
Culex pipiens
Automatic identification
Capture Rate
Genus and sex classification
Machine learning
Mosquito monitoring
Mosquito trap
Optical sensor
Journal
Parasites & vectors
ISSN: 1756-3305
Titre abrégé: Parasit Vectors
Pays: England
ID NLM: 101462774
Informations de publication
Date de publication:
02 Oct 2024
02 Oct 2024
Historique:
received:
07
06
2024
accepted:
03
09
2024
medline:
3
10
2024
pubmed:
3
10
2024
entrez:
2
10
2024
Statut:
epublish
Résumé
Disease-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. The 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. A 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. Obtained 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.
Sections du résumé
BACKGROUND
BACKGROUND
Disease-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.
METHODS
METHODS
The 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.
RESULTS
RESULTS
A 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.
CONCLUSIONS
CONCLUSIONS
Obtained 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.
Identifiants
pubmed: 39358773
doi: 10.1186/s13071-024-06479-z
pii: 10.1186/s13071-024-06479-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
409Subventions
Organisme : Ministero dell'Università e della Ricerca (Italy), PON Research and Innovation 2014-2020
ID : DOT1326YBR-1
Organisme : Ministero dell'Università e della Ricerca (Italy), PNRR
ID : PE00000007
Informations de copyright
© 2024. The Author(s).
Références
World Health Organization. World malaria report 2023. Geneva; 2023 Nov. Licence: CC BY-NC-SA 3.0 IGO. https://www.who.int/publications/i/item/9789240086173 .
World Health Organization. Disease outbreak news; dengue—global situation. 2023. https://www.who.int/emergencies/disease-outbreak-news/item/2023-DON498 .
World Health Organization. Yellow fever. Fact-sheets. 2023 May. https://www.who.int/news-room/fact-sheets/detail/yellow-fever .
Lühken R, Brattig N, Becker N. Introduction of invasive mosquito species into Europe and prospects for arbovirus transmission and vector control in an era of globalization. Infect Dis Poverty. 2023;12:109. https://doi.org/10.1186/s40249-023-01167- .
doi: 10.1186/s40249-023-01167-
pubmed: 38037192
pmcid: 10687857
Simonin Y. Circulation of West Nile virus and Usutu virus in Europe: overview and challenges. Viruses. 2024;16:599. https://doi.org/10.3390/v16040599 .
doi: 10.3390/v16040599
pubmed: 38675940
pmcid: 11055060
European Centre for Disease Prevention and Control. Surveillance, prevention and control of West Nile virus and Usutu virus Infections in the EU/EEA. https://www.ecdc.europa.eu/en/publications-data/surveillance-preventionand-control-west-nile-virus-and-usutu-virus-infections . Accessed 21 May 2024.
Veo C, Della Ventura C, Moreno A, Rovida F, Percivalle E, Canziani S, et al. Evolutionary dynamics of the lineage 2 West Nile virus that caused the largest European epidemic: Italy 2011–2018. Viruses. 2019;11:814. https://doi.org/10.3390/v11090814.PMID:31484295;PMCID:PMC6784286 .
doi: 10.3390/v11090814.PMID:31484295;PMCID:PMC6784286
pubmed: 31484295
pmcid: 6784286
European Centre for Disease Prevention and Control. Historical data by year—West Nile virus seasonal surveillance. Stockholm: ECDC. https://www.ecdc.europa.eu/en/west-nile-fever/surveillance-and-disease-data/historical . Accessed 09 Apr 2024.
European Centre for Disease Prevention and Control. Epidemiological update: West Nile virus transmission season in Europe, 2023. Stockholm: ECDC. https://www.ecdc.europa.eu/en/news-events/epidemiological-update-west-nile-virus-transmission-season-europe-2023-0 . Accessed 09 Apr 2024
European Centre for Disease Prevention and Control. Increasing risk of mosquito-borne diseases in EU/EEA following spread of Aedes species. 2023. https://www.ecdc.europa.eu/en/news-events/increasing-risk-mosquito-borne-diseases-eueea-following-spread-Aedes-species . Accessed 21 May 2024.
Rezza G, Nicoletti L, Angelini R, Romi R, Finarelli AC, Panning M, et al. Infection with chikungunya virus in Italy: an outbreak in a temperate region. Lancet. 2007;370:1840–6. https://doi.org/10.1016/S0140-6736(07)61779-6 .
doi: 10.1016/S0140-6736(07)61779-6
pubmed: 18061059
Venturi G, Di Luca M, Fortuna C, Remoli ME, Riccardo F, Severini F, et al. Detection of a chikungunya outbreak in Central Italy, August to September 2017. Euro Surveill. 2017;22:17–00646. https://doi.org/10.2807/1560-7917.ES.2017.22.39.17-00646 .
doi: 10.2807/1560-7917.ES.2017.22.39.17-00646
pubmed: 29019306
pmcid: 5709953
Caputo B, Russo G, Manica M, Vairo F, Poletti P, Guzzetta G, et al. A comparative analysis of the 2007 and 2017 Italian chikungunya outbreaks and implication for public health response. PLoS Negl Trop Dis. 2020;14:e0008159. https://doi.org/10.1371/journal.pntd.0008159 .
doi: 10.1371/journal.pntd.0008159
pubmed: 32525957
pmcid: 7289343
Gjenero-Margan I, Aleraj B, Krajcar D, Lesnikar V, Klobučar A, Pem-Novosel I, et al. Autochthonous dengue fever in Croatia, august-september 2010. Euro Surveill. 2011;16:19805.
doi: 10.2807/ese.16.09.19805-en
pubmed: 21392489
Succo T, Leparc-Goffart I, Ferré JB, Roiz D, Broche B, Maquart M, et al. Autochthonous dengue outbreak in Nîmes, South of France, July to September 2015. Euro Surveill. 2016. https://doi.org/10.2807/1560-7917.ES.2016.21.21.30240 .
doi: 10.2807/1560-7917.ES.2016.21.21.30240
pubmed: 27254729
Lazzarini L, Barzon L, Foglia F, Manfrin V, Pacenti M, Pavan G, et al. First autochthonous dengue outbreak in Italy, August 2020. Eur Surveill. 2020;25:2001606. https://doi.org/10.2807/1560-7917.ES.2020.25.36.2001606 .
doi: 10.2807/1560-7917.ES.2020.25.36.2001606
European Centre for Disease Prevention and Control (ECDC). Autochthonous vectorial transmission of dengue virus in mainland EU/EEA, 2010-present. Stockholm: ECDC. https://www.ecdc.europa.eu/en/all-topics-z/dengue/surveillance-and-disease-data/autochthonous-transmission-dengue-virus-eueea . Accessed: 09 Apr 2024.
Barzon L, Gobbi F, Capelli G, Montarsi F, Martini S, Riccetti S, et al. Autochthonous dengue outbreak in Italy 2020: clinical, virological and entomological findings. J Travel Med. 2021;28:taab130. https://doi.org/10.1093/jtm/taab130 .
doi: 10.1093/jtm/taab130
pubmed: 34409443
pmcid: 8499737
European Centre for Disease Prevention and Control. Guidelines for the surveillance of invasive mosquitoes in Europe. Stockholm: ECDC; 2012 Aug. https://doi.org/10.2900/61134 .
European Centre for Disease Prevention and Control. Guidelines for the surveillance of native mosquitoes in Europe. Stockholm: ECDC; 2014 Nov. https://doi.org/10.2900/37227 .
Caputo B, Manica M. Mosquito surveillance and disease outbreak risk models to inform mosquito-control operations in Europe. Curr Opin Insect Sci. 2020;39:101–8. https://doi.org/10.1016/j.cois.2020.03.009 .
doi: 10.1016/j.cois.2020.03.009
pubmed: 32403040
Zardini A, Menegale F, Gobbi A, Manica M, Guzzetta G, d’Andrea V, et al. Estimating the potential risk of transmission of arboviruses in the Americas and Europe: a modelling study. Lancet Planet Health. 2024;8:e30–40. https://doi.org/10.1016/S2542-5196(23)00252-8 .
doi: 10.1016/S2542-5196(23)00252-8
pubmed: 38199719
Maia LJ, Oliveira CH, Silva AB, Souza PAA, Müller NFD, Cardoso JDC, et al. Arbovirus surveillance in mosquitoes: historical methods, emerging technologies, and challenges ahead. Exp Biol Med. 2023;248:2072–82. https://doi.org/10.1177/15353702231209415 .
doi: 10.1177/15353702231209415
González-Pérez MI, Faulhaber B, Williams M, Brosa J, Aranda C, Pujol N, et al. A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy. Parasit Vectors. 2022;15:190. https://doi.org/10.1186/s13071-022-05324-5.PMID:35668486;PMCID:PMC9169302 .
doi: 10.1186/s13071-022-05324-5.PMID:35668486;PMCID:PMC9169302
pubmed: 35668486
pmcid: 9169302
González-Pérez MI, Faulhaber B, Aranda C, Williams M, Villalonga P, Silva M, et al. Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex. Parasit Vectors. 2024;17:97. https://doi.org/10.1186/s13071-024-06177-w.PMID:38424626 .
doi: 10.1186/s13071-024-06177-w.PMID:38424626
pubmed: 38424626
pmcid: 10905882
Facchinelli L, Valerio L, Pombi M, Reiter P, Costantini C, Della TA. Development of a novel sticky trap for container-breeding mosquitoes and evaluation of its sampling properties to monitor urban populations of Aedes albopictus. Med Vet Entomol. 2007;21:183–95. https://doi.org/10.1111/j.1365-2915.2007.00680.x .
doi: 10.1111/j.1365-2915.2007.00680.x
pubmed: 17550438
Marini F, Caputo B, Pombi M, Tarsitani G, Della TA. Study of Aedes albopictus dispersal in Rome, Italy, using sticky traps in mark-release-recapture experiments. Med Vet Entomol. 2010;24:361–8. https://doi.org/10.1111/j.1365-2915.2010.00898.x .
doi: 10.1111/j.1365-2915.2010.00898.x
pubmed: 20666995
Severini F, Toma L, Di Luca M, Romi R. Italian mosquitoes: general information and identification of adults (Diptera, Culicidae)/Le zanzare italiane: generalità e identificazione degli adulti (Diptera, Culicidae). Fragmenta Entomol. 2009;41:213–372. https://doi.org/10.13133/2284-4880/92 .
doi: 10.13133/2284-4880/92
Brooks ME, Kristensen K, VAN Benthem KJ, Magnusson A, Berg CW, Nielsen A, et al. Glmmtmb balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 2017;9:378–400. https://doi.org/10.32614/RJ-2017-066 .
doi: 10.32614/RJ-2017-066