Abstract
The spread of the invasive mosquitoes Aedes albopictus, Aedes aegypti, and Aedes japonicus in Spain represents an increasing public health risk due to their capacity to transmit arboviruses such as dengue, Zika, and chikungunya, among others. Traditional field entomological surveillance remains essential for tracking their spread, but it faces limitations in terms of cost, scalability, and labor intensity. Since 2014, the Mosquito Alert citizen-science project has enabled public participation in surveillance through the submission of geolocated images via a mobile app, which are identified using AI in combination with expert validation. While field surveillance provides high accuracy, citizen science offers low-cost, large-scale, real-time data collection aligned with open data management principles. It is particularly useful for detecting long-distance dispersal events and has contributed up to one-third of the municipal detections of invasive mosquito species since 2014. This study assesses the value of integrating both surveillance systems to capitalize on their complementary strengths while compensating for their weaknesses in the areas of taxonomic accuracy, scalability, spatial detection patterns, data curation and validation systems, geographic precision, interoperability, and real-time output. We present the listing of municipal detections of these species from 2004 to 2024, integrating data from both sources. Spain's integrated approach demonstrates a pioneering model for cost-effective, scalable vector surveillance tailored to the dynamics of invasive species and emerging epidemiological threats.