VectAbundance: a spatio-temporal database of vector observations

Abstract

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.

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

“…(Da Re et al, 2023b). This, unfortunately, is a limitation related to the different sampling and monitoring schemes employed by the different institutions (Da Re et al, 2023b). 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.…”

“…Overall, 30 aggregated ovitraps were located in the Alpine biogeographical region, while 48 and 71 were located in the Continental and Mediterranean biogeographical regions, respectively. Most of the ovitraps were active during the period 2020-2022, with only a few stations that were monitored for more than three seasons (Da Re et al 2023b). 120 aggregate ovitraps were used to train the models, whilst 19 were retained for the external validation.…”

“…First of all, egg observations were pooled from ovitraps having different volumes, shapes, oviposition substrates, liquid solutions, revisit times, etc. (Da Re et al, 2023b). This, unfortunately, is a limitation related to the different sampling and monitoring schemes employed by the different institutions (Da Re et al, 2023b).…”