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Issues

In response to the emergence of new pathogens and their global spread (for instance the Covid-19 pandemic), public health bodies are increasingly using early detection systems to identify health threats by monitoring various data sources. These epidemic intelligence systems use two types of information: "official" sources declared by public health bodies, and "unofficial" sources that relay information from the media, social networks, scientific articles or laboratory data and electronic health records.

In recent years, systems based on unofficial sources have proved particularly good at detecting the emergence of new diseases. The only issue is that they generate vast amounts of data to be processed. MOOD aims to offer epidemic intelligence systems tools for detecting, gathering, storing and analysing data, to ensure more effective health surveillance. In addition to health data, other types of data will also be incorporated, such as climate, animal and human movements,  urbanization and deforestation, to assess the links between the risk of the emergence of infectious diseases and global changes.

Description

The MOOD project comprises four stages:

1. Five case studies on epidemic intelligence systems in European countries with different socioeconomic statuses, geography, climate, and surveillance methods have already begun. This is focused on Spain, Finland, France, Italy and Serbia, and the European Centre for Disease Control (ECDC).
2. Together with the epidemic intelligence stakeholders in these countries, work will then be carried out to characterize their systems and then jointly assess their needs in terms of epidemic intelligence tools and services (models, data, visualization etc).
3. Tools and services will be developed and made available to the ECDC and the public health bodies involved in the project and disseminated in Europe and beyond, particularly in the global South.
4. Finally, these new co-constructed tools and services will be shared at a reasonable cost and if possible be open source.

Main results

Based on case studies and information drawn from the pandemic, MOOD worked with local players to construct solutions to complement the systems already used by national public and animal health agencies. Those tools, disseminated as an open-source or low-cost utility, have served to exploit big data gathered from multiple sources. MOOD therefore achieved its objective to improve the detection, surveillance and assessment of emerging infectious diseases in Europe, by stepping up epidemic monitoring using advanced analysis and monitoring techniques.

• Craation of the MOOD platform
• Creation and leadership of a broad network of users in Europe and beyond
• Creation of an international non-profit association to maintain the tools and sustain the community
• 140 scientific publications
• More than 60 datasets available via OpenAIRE.

Open source tools for high-performance European health monitoring

Climate change, animal and human movements, population growth and urbanization mean ever greater risks of the emergence of new pathogens and the acceleration of their spread worldwide. How can we identify new signs of epidemics at an early stage? The European MOOD project consortium is working on innovative, user-friendly tools to improve health monitoring in Europe. Three open source modules are currently being built.