Eco-Epidemiological Intelligence for early Warning and response to mosquito-borne disease risk in Endemic and Emergence settings

Informations projet

E4Warning

N° de convention de subvention: 101086640

DOI

10.3030/101086640

Date de signature de la CE 4 Octobre 2022

Date de début 1 Janvier 2023

Date de fin 31 Decembre 2026

Financé au titre de

• Food, Bioeconomy Natural Resources, Agriculture and Environment

Coût total

€ 4 082 528,75

Contribution de l’UE

€ 4 082 528,75

Coordonné par

AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS
Spain

Periodic Reporting for period 2 - E4Warning (Eco-Epidemiological Intelligence for early Warning and response to mosquito-borne disease risk in Endemic and Emergence settings)

Période du rapport: 2024-07-01 au 2025-12-31

Résumé du contexte et des objectifs généraux du projet

Mosquito-borne diseases such as dengue, Zika, chikungunya and West Nile fever are expanding due to climate and land-use change, intensified travel and trade, and growing human exposure in peri-urban landscapes. Dengue alone infects an estimated 390 million people annually and causes tens of thousands of deaths. Although these infections remain concentrated in tropical and subtropical regions, Europe faces rising importation pressure and increasing suitability for invasive vectors and seasonal transmission. This creates a strategic need to move from reactive outbreak response to anticipatory surveillance and early warning that supports decisions under uncertainty in decentralised public-health systems.

E4Warning addresses this need through a “disease intelligence” approach that transforms fast-changing, multi-source data into actionable information for preparedness and control. The project targets two key transmission systems: urban arboviruses driven mainly by *Aedes* mosquitoes (dengue, Zika, chikungunya), and zoonotic transmission exemplified by West Nile virus, maintained in bird communities with spillover mediated largely by *Culex* mosquitoes. Working across both systems enables methods that generalise across urban and peri-urban contexts and across endemic, fringe, and cross-border risk settings.

The project builds early warning and decision-support systems by integrating participatory surveillance (Mosquito Alert), smart IoT traps, and passive acoustic monitoring for hosts with EO/climate and hydrological drivers, and by developing models that correct for observation effort and incorporate mobility and mechanistic interaction processes. The pathway to impact follows a continuous cycle from validated observations to routinely updated risk outputs aligned with decision cycles (daily operations, seasonal preparedness, and strategic planning). In Europe, this includes fine-scale risk intelligence and importation-oriented components; beyond Europe, it includes transferable dengue early warning (D-MOSS) developed with implementation-oriented engagement, including work with stakeholders in Vietnam and Sri Lanka. Social and behavioural dimensions are embedded to make outputs usable: citizen science is treated as a socio-technical system requiring trust and governance, and stakeholder engagement supports interpretation of probabilistic forecasts and alignment with real operational responsibilities.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

During RP2 (M19–M36), E4Warning shifted from building separate data streams to operating integrated pipelines that turn observations into calibrated risk outputs and mechanistic insight. Mosquito Alert scaled further and its AI triage (AIMA) was upgraded for faster, more robust near–real-time classification of heterogeneous citizen imagery. In parallel, IRIDEON’s VECTRACK matured via a consolidated IoT service/API, improved ML classifiers and automated device-health monitoring, with field validation in Spain, Greece and Brazil benchmarking AI outputs against expert/manual counts and documenting remaining limitations (context/temperature effects and occasional false positives).

These gains fed into operational modelling, notably the consolidated Barcelona workflow integrating AI-filtered citizen science, smart-trap streams, conventional traps and harmonised covariates with sampling-effort correction to deliver daily nowcasts and short-term forecasts for vector-control planning, complemented by a broader Spain-level structure for transfer. Host-related evidence also progressed through improved GPS/Sigfox biologging (enhanced antenna coverage enabling tracking of medium-sized passerines), large-scale extraction of dispersal traits from Movebank, and passive acoustic monitoring overlapped with professional distance-sampling transects to estimate abundance and quantify bias; WP7 added mechanistic contact information via blood-fed mosquito collection and molecular diet inference.

Environmental and ecological inputs expanded through an updated multi-source covariate backbone (EO, climate/reanalysis, demography and land-surface drivers), improved downscaling/forecast verification, and VIC-based hydrology variables (soil moisture, runoff, baseflow, evapotranspiration) extended toward European-scale production to test added value beyond precipitation/humidity proxies. For dengue, D-MOSS advanced toward a generalised framework across Vietnam, Sri Lanka and Malaysia, while “transmission fringe” emergence analyses and EU-facing importation modelling continued to strengthen evidence on where and when risk emerges. Overall, RP2 demonstrated that heterogeneous streams can be operationally integrated and validated to generate interpretable outputs at surveillance and control scales.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

E4Warning moves beyond the state of the art by operationalising “disease intelligence”: citizen reports, IoT smart traps and passive acoustic monitoring are validated and harmonised, then translated into routinely updated risk information at decision-relevant scales. In RP2, Mosquito Alert’s AI triage and IRIDEON’s VECTRACK stack matured for near–real-time use (API integration, automated monitoring, improved classifiers), and key error regimes (context/temperature dependence and false positives) were quantified to drive targeted mitigation.

On the modelling side, the Barcelona SDSS demonstrates continuous ingestion of multi-source surveillance with sampling-effort correction to deliver daily nowcasts and short-term forecasts for vector-control planning. The project also broadened drivers and mechanisms by adding hydrology-based covariates, competence and movement evidence, and vector–host contact inference, while D-MOSS progressed toward a transferable dengue early-warning framework. Remaining technical needs focus on larger labelled datasets and calibration sites, additional demonstrations to test portability, and stable access to routine surveillance/forecast inputs for robust evaluation.

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