AIMSurv: First pan-European harmonized surveillance of &lt;italic&gt;Aedes&lt;/italic&gt; invasive mosquito species of relevance for human vector-borne diseases

Miranda, Miguel Ángel; Barceló, Carlos; Arnoldi, Daniele; Augsten, Xenia; Bakran-Lebl, Karin; Balatsos, George; Bengoa, Mikel; Bindler, Philippe; Boršová, Kristína; Bourquia, Maria; Bravo‐Barriga, Daniel; Palmer, John R.B.; Caputo, Beniamino; Christou, Maria; Delacour, Sarah; Eritja, Roger; Fassi-Fihri, Ouafaa; Ferraguti, Martina; Flacio, Eleonora; Frontera, Eva; Fuehrer, Hans‐Peter; Garcı́a-Pérez, Ana L.; Georgiades, Pantelis; Gewehr, Sandra; Goiri, Fátima; González, María Rosa Luengo; Gschwind, Martin; Gutiérrez‐López, Rafael; Horváth, Cintia; Ibáñez-Justicia, Adolfo; Jani, Viola; Kadriaj, Përparim; Kalan, Katja; Kavran, Mihaela; Klobučar, Ana; Kurucz, Kornélia; Lucientes, Javier; Lühken, Renke; Magallanes, Sergio; Marini, Giancarlo; Martinou, Angeliki F.; Michelutti, Alice; Mihalca, Andrei Daniel; Montalvo, Tomás; Montarsi, Fabrizio; Mourelatos, Spiros; Muja-Bajraktari, Nesade; Müller, Pie; Notarides, Gregoris; Osório, Hugo Costa; Oteo, José A.; Öter, Kerem; Pajović, I.; Petrinić, Sunčica; Răileanu, Cristian; Ries, Christian; Rogozi, Elton; Ruíz-Arrondo, Ignacio; Sanpera-Calbet, Isis; Sekulić, Nebojša; Sevim, Kivanc; Sherifi, Kurtesh; Silaghi, Cornelia; Silva, Manuel; Sokolovska, Nikolina; Soltész, Zoltán; Șuleșco, Tatiana; Šušnjar, Jana; Teekema, S.; Valsecchi, Andrea; Vasquez, Marlen Ines; Velo, Enkelejda; Michaelakis, Αntonios; Wint, William; Petrić, Dušan; Schaffner, Francis; Torre, Alessandra della

doi:10.46471/gigabyte.57

Cited by 11 publications

(14 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), depending on their needs, budget, and personnel. As a result, between (but also within) European countries the monitoring schemes are highly heterogeneous (Jourdain et al, 2019;Miranda et al, 2022), restricting the temporal and spatial extent of each monitoring effort and producing gaps in data collection in certain geographical areas or during specific periods. Whilst this is undoubtedly problematic, some studies suggest that different ovitraps can yield comparable results in terms of collected eggs (i.e., Velo et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…All the raw observations were already used to perform other analyses and publications (e.g. Tran et al, 2013;Tisseuil et al, 2018;Romiti et al, 2021;Da Re et al, 2022;Lencioni et al, 2023, Ravasi et al, 2021), and the observations collected from 65 ovitraps over 6 municipalities in Albania during 2020-2022 were already made available in Miranda et al (2022). The procedure of temporal downscaling and spatial aggregation did not alter the observed seasonal pattern of Ae.…”

Section: Technical Validationmentioning

confidence: 99%

See 1 more Smart Citation

VectAbundance: a spatio-temporal database of vector observations

Da Re,

Marini,

Bonannella

et al. 2023

Preprint

View full text Add to dashboard Cite

Modelling approaches play a crucial role in supporting local public health agencies by estimating and forecasting vector abundance and seasonality. However, the reliability of these models is contingent on the availability of standardized, high-quality data. Addressing this need, our study focuses on collecting and harmonizing egg count observations of Aedes albopictus, obtained through ovitraps in monitoring and surveillance efforts across Albania, France, Italy, and Switzerland from 2010 to 2022. We processed the raw observations to obtain a continuous time series of ovitraps observations allowing for an extensive geographical and temporal coverage of Ae. albopictus population dynamics. The resulting post-processed observations are stored in the open-access database VectAbundance, currently hosting data for Ae. albopictus. Future database releases may include observational data for other medically significant vectors, such as the common house mosquito Culex pipiens or the tick Ixodes ricinus. This initiative addresses the critical need for accessible, high-quality data, enhancing the reliability of modelling efforts and bolstering public health preparedness.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Technical Validationmentioning

confidence: 99%

VectAbundance: a spatio-temporal database of vector observations

Da Re,

Marini,

Bonannella

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…spp mosquitoes, and in particular Aedes aegypti and Aedes albopictus , are major vectors of arboviruses, among which the yellow fever, dengue, chikungunya and zika viruses ( 30 , 31 ). Due to their invasion potential and their health importance, they are the subject of entomological surveillance programs at nationals and European level ( 32 , 33 ).…”

Section: Invasive Aedes Mosquitoesmentioning

confidence: 99%

Dengue and chikungunya: future threats for Northern Europe?

Laverdeur,

Desmecht,

Hayette

et al. 2024

Front. Epidemiol.

View full text Add to dashboard Cite

Arthropod-borne viral diseases are likely to be affected by the consequences of climate change with an increase in their distribution and intensity. Among these infectious diseases, chikungunya and dengue viruses are two (re)emergent arboviruses transmitted by Aedes species mosquitoes and which have recently demonstrated their capacity for rapid expansion. They most often cause mild diseases, but they can both be associated with complications and severe forms. In Europe, following the establishment of invasive Aedes spp, the first outbreaks of autochtonous dengue and chikungunya have already occurred. Northern Europe is currently relatively spared, but climatic projections show that the conditions are permissive for the establishment of Aedes albopictus (also known as the tiger mosquito) in the coming decades. It is therefore essential to question and improve the means of surveillance in northern Europe, at the dawn of inevitable future epidemics.

show abstract

“…Despite the preprocessing operation on the observations collected by the ovitraps, some of these sources of variability have likely influenced our results and therefore should be taken into account while interpreting the results. Therefore, we want to highlight the importance of carrying out reproducible and comparable sampling schemes following the most updated standards for ovitraps monitoring, as those presented as the outcome of the AIM-COST cost action by Miranda et al (2022).…”

Section: Limitations and Future Perspectivesmentioning

confidence: 99%

Inferring the seasonal dynamics and abundance of an invasive species using a spatio-temporal stacked machine learning model

Da Re,

Marini,

Bonannella

et al. 2023

Preprint

View full text Add to dashboard Cite

Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The modelling approach choice, i.e. the algorithm, is a crucial factor for addressing the multiple sources of variability that can lead to disparate outcomes when different models are applied to the same dataset. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. This dataset spans various locations in southern Europe, covering four countries - Albania, France, Italy, and Switzerland - and encompasses multiple seasons from 2010 to 2022. Utilising these ovitrap observations and a set of environmental predictors, we employed a stacked machine learning model to forecast the weekly average number of mosquito eggs. This approach enabled us to i) unearth the seasonal dynamics of Ae. albopictus for 12 years; ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Beyond its immediate application for public health management, our work presents a versatile modelling framework adaptable to infer the spatio-temporal abundance of various species, extending its relevance beyond the specific case of Ae. albopictus.

show abstract

AIMSurv: First pan-European harmonized surveillance of <italic>Aedes</italic> invasive mosquito species of relevance for human vector-borne diseases

Cited by 11 publications

References 45 publications

VectAbundance: a spatio-temporal database of vector observations

VectAbundance: a spatio-temporal database of vector observations

Dengue and chikungunya: future threats for Northern Europe?

Inferring the seasonal dynamics and abundance of an invasive species using a spatio-temporal stacked machine learning model

Contact Info

Product

Resources

About