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

Informazioni relative al progetto

E4Warning

ID dell’accordo di sovvenzione: 101086640

DOI

10.3030/101086640

Data della firma CE 4 Ottobre 2022

Data di avvio 1 Gennaio 2023

Data di completamento 31 Dicembre 2026

Finanziato da

• Food, Bioeconomy Natural Resources, Agriculture and Environment

Costo totale

€ 4 082 528,75

Contributo UE

€ 4 082 528,75

Coordinato da

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)

Periodo di rendicontazione: 2024-07-01 al 2025-12-31

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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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