Work

Augmenting community-driven vector surveillance with automated image classification: Lessons from the Artificial Intelligence Mosquito Alert (AIMA) system

Year: 2025

Type: article

Abstract: The Mosquito Alert (MA) platform leverages artificial intelligence to enhance community-driven mosquito surveillance by automatically identifying mosquito species from geolocated images submitted via a mobile app. This empowers the public to report both native and invasive mosquitoes of public health relevance, contributing to early detection and monitoring efforts. The Artificial Intelligence Mosquito Alert (AIMA) system integrates machine learning image classification within an automated backend pipeline to enable real-time triaging of submissions: critical reports are flagged for expert review, routine cases are classified automatically, and contributors receive immediate feedback fostering participant engagement. By automating routine identifications, the system reduces the burden on experts, allowing them to focus on complex or ambiguous cases that require taxonomic expertise. This study focuses on two AIMA operational periods in 2023 and 2024. We evaluate model updates and performance across these years, highlighting both progress achieved and remaining limitations under real-world citizen science conditions. The most reliably classified species across both models were Aedes albopictus and Culex sp., whereas Aedes aegypti remained difficult to identify. Despite its limitations, AIMA remains central to enabling scalable, responsive, and intelligent mosquito vector surveillance, substantially reducing the time experts must devote to routine identifications. Functioning as an Early Warning System (EWS), MA produces real-time distribution maps of invasive species and rapidly delivers actionable information to public health authorities, facilitating timely responses and intervention. • Demonstrates an AI-driven pipeline within the Mosquito Alert citizen science platform for real-time mosquito species identification, fast feedback to participants, and early warning to stakeholders. • Analyses community participation trends across Europe and Spain, reporting 75,216 total reports in 2023 and 49,381 in 2024, with category breakdowns for mosquito reports, breeding sites, bites, and other species. • Provides detailed snapshots of early warning system performance at the end of both 2023 and 2024, alongside their corresponding 2024 metrics, demonstrating sustained model effectiveness across seasons. • Compares 2023 vs . 2024 alert outputs (660 to 468) with stable detection precision (~42%) and improved false-negative rate (from 17.5% to 13.1%). • Benchmark analysis with external databases showing that training on citizen science images resolves biological meaningful features that are identified in other types of images. • Demonstrates how the integrated AI and distribution-map pipeline supports a real-time early warning system, enabling rapid detection and alerting of invasive mosquito species to inform timely public health interventions.

Source: Epidemics

DOI: 10.1016/j.epidem.2025.100863