e4warning.eu – HORIZON Europe (HORTIZON Research and Innovation Actions)

E4Warning is an holistic approach to improve our understanding of the interplay between humans, mosquitoes, reservoir species and the environment for a better disease intelligence capable of anticipating and identifying mosquito-borne diseases epidemic risk and outbreaks.

Mosquito-borne diseases, such as dengue, Zika, chikungunya and West Nile fever, are emerging and re-emerging worldwide because of climate change and globalisation. Every year, an estimated 390 million dengue infections occur around the world, resulting in up to 36,000 deaths. The burden of these diseases is highest in tropical and subtropical areas, but increasing numbers of autochthonous case are being reported from European countries since 2010, raising concerns about the potential for the establishment of these pathogens in temperate regions.

To address the complexity of Mosquito-borne diseases (MBDs), the E4Warning consortium is made up of an interdisciplinary and innovative team from 12 organisations from Spain, Germany, Greece, Belgium, Switzerland and the United Kingdom, with experience in entomology, movement ecology, epidemiology, Earth Observation science, sensor engineering, sociodemography and spatial statistic modelling.

The 7 Vs of intelligence: E4W strategic framework

Our consortium is built to help institutions anticipate risk, act faster, and respond smarter. By combining citizen participation, advanced sensing technologies, and AI‑driven analytics, we transform vast environmental signals into early‑warning intelligence that supports real‑world decision making in emerging and endemic sites. Our system is powered by seven intelligence pillars that turn complex environmental data into actionable public‑health insights.

Volume

We process massive georeferenced datasets, over 126,000 citizen observations combined with IoT sensor streams.

Variety

We harmonize 99 complementary datasets, from satellite Earth Observation and climate reanalysis to human mobility and acoustic monitoring.

Viability

Our tools are built for long‑term adoption, supporting public health institutions both in endemic and emerging scenarios with scalable, sustainable pathways.

Value

We transform raw environmental signals into operational early‑warning intelligence aligned with real‑world decision cycles.

Velocity

AI‑assisted pipelines (AIMA) validate incoming citizen observations within minutes, enabling near‑real‑time response.

Veracity

Data is rigorously cross‑checked using citizen reports, traditional traps, and smart sensors to ensure high reliability.

Visualization

All outputs follow FAIR principles (meaning Findable, Accessible, Interoperable, Reusable) and are openly archived for global research use.

OUR IMPACT SO FAR
Advancing environmental intelligence

Transforming environmental intelligence

Surface‑Water Dynamics: Earth Observation models support dengue predictions in Vietnam and improve European West Nile Virus (WNV) risk assessments.
EU Vector Maps: high‑resolution 1 km suitability maps for major mosquito species across Europe.
Climate Impact Evidence: demonstrated that human‑driven warming increased dengue incidence by up to 49% in parts of Brazil.

Technologies for near Real-Time tracing

Mosquito Alert: a citizen‑science platform with an AI system for classifying mosquito images (AIMA) reaching 0.80 accuracy combined with expert‑validated alerts.
Smart Sensing VECTRACK: automated counting and genus‑level recognition reduce field work and surveillance costs.
Bio‑logging Innovation: lightweight SigFox tags track WNV bird hosts at resolutions not achievable with standard GPS.

One Health integration and operational impact

D‑MOSS Forecasting: Provides 1–6 month dengue forecasts; now supported by the open‑source GHRtools suite and adopted in Sri Lanka and Malaysia.
Urban Risk Portals: local now‑casts and 6‑day forecasts at 20 m resolution deployed for Barcelona.
Spillover Modelling: end‑to‑end workflow linking bird, mosquito, and human networks to track risk propagation.

Key research areas: E4W core domains of expertise

E4W project brings together environmental intelligence, advanced sensing technologies, and open science to strengthen Europe’s preparedness against mosquito‑borne threats. By combining Earth Observation, AI‑enhanced surveillance, participatory science, and One Health analytics, we are building operational tools that support authorities, researchers, and communities with timely, actionable insights—locally and globally.

Vectors ecology & surveillance

High quality real-time information on vectors for a scalable and flexible “epidemic intelligence”

100

Ecosystem barriers to disease spreading

Host and vector dispersal capacities. Movement patterns in complex mosaic landscapes.

Earth Observation data

Estimate and anticipate mosquito prevalence and disease risk.

250

Disease forecasting

Dengue forecasting in South Asia and endemic hotspots.

Human mobility

How human activity produce differential disease exposure and contribute to the spreading of invasive mosquitoes and diseases.

Dengue importation risk

Dengue prevalence in endemic areas and global traffic patterns will anticipate connectivity and importation risk.

Work Packages

WP1 – Coordination and management

To ensure the scientific coordination and implementation of the whole project life cycle and work plan according to the grant rules and consortium agreements.

WP2 – Citizen-Science Mosquito Surveillance

Exploit and scale up the expert-validated citizen-science vector surveillance and early warning system at the heart of EIC-finalist FARSEER.

WP3 – Networked smart mosquito traps

Improve the method and software for the automatic detection and classification of mosquitoes; and manufacture prototype subsystem units to be used in field tests in real operational environment with end-users.

WP4 – Earth Observation, hydroclimate, vector suitability maps

Provide a covariate data suite that covers the needs of the different modelling efforts; Model water availability based on the Variable Infiltration Capacity Macroscale Hydrologic Model; Model vector suitability based on traditional data sources; Model vector seasonality and activity based on different modelling pathways.

WP5 – Vector, animal, and human movement ecology

Quantifying the movement patterns of potential reservoir species as well as vectors along a gradient of human population density from urban to natural areas to assess ecosystem barriers and their resilience in facing emerging diseases. It also aims at understanding the role of human mobility patterns as introducing negative and positive feedback to disease risk.

WP6 – Disease risk models in endemic settings

Combine novel data generated in WP2-5 with disease surveillance data to predict the risk of outbreaks and emergence of new transmission hotspots along fringe areas of endemic regions. Using Bayesian space-time prediction frameworks, we aim to model the complex relationship between climate, human behaviour and disease across different settings.

WP7 – Emerging risk models in Europe

WP 7 draws on insights from WPs 2-6 to address emerging risks of mosquito-borne disease in Europe. It takes an integrated one-health approach, connecting factors related to hosts, vectors, reservoirs, and the environment across multiple scales, including importation models linked to the endemic areas studied in WP6, and spreading models linked to insights on human, mosquito, and bird mobility drawn from WPs 4 and 5 to gain a deeper understanding of MBDs and to increase the effectiveness of interventions.

WP8 – Capacity building, policy & exploitation

Establishing strategic engagement with governmental and non-governmental stakeholders, policymakers, relevant institutions and academia, the commercial sector, and international funding bodies in research and public health.

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E4Warning – Eco-Epidemiological Intelligence for early Warning and response to mosquito-borne disease risk in Endemic
and Emergence settings. E4Warning has received funding from the European Union’s Horizon Europe programme ((HORIZON Research and Innovation Actions) under Grant Agreement 101086640