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MOOD · Project

Early Warning System That Detects Disease Outbreaks Before They Spread

healthTestedTRL 6
mood-h2020.eu

Imagine you're trying to spot a wildfire before it becomes uncontrollable — but instead of fires, you're watching for disease outbreaks. Right now, public health agencies mostly wait for doctors to report cases, which is slow and misses things. MOOD built a system that scans news, environmental data, animal health records, and other sources all at once to catch warning signs of epidemics much earlier. Think of it as a weather radar for infectious diseases across Europe.

By the numbers
27
consortium partners contributing disease data and expertise
12
European countries covered in the surveillance network
25
project deliverables including tools, datasets, and algorithms
4
SMEs involved in developing commercial applications
The business problem

What needed solving

Infectious disease outbreaks are detected too late because traditional surveillance relies on doctors and hospitals reporting cases — a slow process that misses emerging threats. With climate change, increased travel, and urbanization, new and exotic pathogens can spread across borders before anyone notices. Organizations responsible for public health, livestock protection, and travel safety need faster, data-driven early warning systems.

The solution

What was built

MOOD built a visual analytics prototype for real-time disease monitoring, data processing algorithms that link disease records with environmental and socio-economic data, standardized datasets combining disease and covariate information, and a data integration toolchain using text mining, spatial analysis, and semantic approaches — totaling 25 deliverables across a 27-partner consortium.

Audience

Who needs this

National public health agencies responsible for epidemic preparednessVeterinary health authorities monitoring zoonotic disease risksTravel risk management and insurance companiesLivestock and poultry industry associations tracking disease threatsSmart city and urban health monitoring solution providers
Business applications

Who can put this to work

Public Health & Epidemiology
enterprise
Target: National or regional public health agencies

If you are a public health agency dealing with delayed outbreak detection because you rely on case reporting alone — this project developed a real-time data platform combining environmental, socio-economic, and epidemiological data from multiple sources that helps you spot disease signals weeks earlier. The system was tested on airborne, vector-borne, and waterborne diseases across a network of 27 partner organizations in 12 countries.

Animal Health & Veterinary Services
mid-size
Target: Veterinary pharmaceutical companies or livestock health monitoring firms

If you are a veterinary health company struggling to anticipate zoonotic disease spread that affects livestock markets — this project built data mining and analysis tools using a One Health approach that links animal and human disease signals with climate and environmental changes. The platform was designed with end users and tested on antimicrobial resistance patterns alongside vector-borne diseases.

Travel & Insurance
any
Target: Travel insurance or corporate travel risk management companies

If you are a travel risk company that needs to assess disease threats in real time for clients traveling across Europe — this project created a visual analytics prototype and standardized disease datasets covering 12 countries that can feed into risk scoring models. With 25 deliverables including processing algorithms for linking disease data with environmental covariates, the outputs can power destination-level risk assessments.

Frequently asked

Quick answers

What would it cost to access or license this disease surveillance platform?

The project was publicly funded EU research (RIA), so core outputs and standardized datasets are likely available under open or preferential licensing terms. Specific commercial licensing would need to be negotiated with the coordinator CIRAD in France. Contact the consortium to discuss access terms.

Can this scale to cover diseases and regions beyond Europe?

The platform was designed and tested across 12 countries with 27 partner organizations contributing data. The data mining methods handle heterogeneous big data sources, and the algorithms were built for airborne, vector-borne, and waterborne diseases including antimicrobial resistance — suggesting strong adaptability to new regions and disease types.

Who owns the intellectual property and can we license it?

As an RIA (Research and Innovation Action) under Horizon 2020, IP typically stays with the consortium partners who created it. CIRAD coordinates the 27-partner consortium. Licensing discussions should start with them, particularly for the visual analytics prototype and the data processing algorithms.

Has this been tested in real outbreak situations?

The project ran from 2020 through 2024, which means it operated through the entire COVID-19 pandemic period. The deliverables describe prototypes tested on selected datasets and evaluated by domain experts. The system was fine-tuned on airborne, vector-borne, and waterborne model diseases.

How does this integrate with existing national health surveillance systems?

MOOD was explicitly designed with end users — national and international human and veterinary public health organizations — to ensure routine use during and beyond the project. The data integration toolchain includes standardized exchange protocols and combines text mining, spatial analysis, and semantic approaches for interoperability.

Is there ongoing support or a team behind this?

The consortium includes 27 partners across 12 countries with 11 research organizations, 10 universities, and 3 industry partners. The project invested in capacity building, training activities, and a network of disease experts. Studies on cost-effectiveness were conducted to support sustainable uptake beyond the project end in 2024.

What regulations or compliance standards does this meet?

The project addressed barriers to data sharing as a core work area, which is critical given GDPR and health data regulations in Europe. Based on available project data, specific regulatory certifications are not mentioned, but the platform was built for use by official public health organizations who operate under strict regulatory requirements.

Consortium

Who built it

This is a research-heavy consortium with 27 partners across 12 countries — dominated by 11 research organizations and 10 universities, with only 3 industry partners (11% industry ratio). Coordinated by CIRAD, a major French agricultural research center, the project has deep scientific credibility but limited commercial muscle. The 4 SMEs in the consortium could be entry points for licensing or partnership discussions, but a business looking to adopt this technology should expect to work closely with academic partners for customization and integration.

How to reach the team

CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement), France — a major public agricultural research institute. Look for the MOOD project lead on the project website or CIRAD's research directory.

Order a report on this consortiumSee CENTRE DE COOPERATION INTERNATIONALE EN RECHERCHE AGRONOMIQUE POUR LEDEVELOPPEMENT - C.I.R.A.D. EPIC profile →
Next steps

Talk to the team behind this work.

Want an introduction to the MOOD team to discuss licensing the surveillance platform or integrating their disease detection algorithms? SciTransfer can arrange a direct meeting with the right consortium partner for your use case.

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