19/03/2020 4 mins

Response to emerging diseases: rethinking global health monitoring

Does the arrival of COVID-19 mean we need to rethink our health monitoring systems? And how can we identify new epidemic warning signs at an early stage? These are the questions currently being addressed by European and North American researchers specialising in emerging diseases (most of which originate in animals), as part of the MOOD project, which started in January 2020. Epidemiologists from ANSES's Lyon Laboratory are contributing to this H2020 project, which brings together 25 research institutes* and public health agencies, and is being coordinated by CIRAD.

What are the risks of a new pathogen being introduced into Europe? What are the risks of it spreading? Which areas provide favourable conditions for its spread? These are the initial questions being addressed by the MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context), which is seeking to improve epidemic intelligence tools and services, with a strong focus at the moment on COVID-19.

Coordinated by CIRAD as part of the European H2020 programme, MOOD has a budget of 14 million euros and brings together 25 partners – research institutions, public health agencies and veterinary services – from 12 countries. When it comes to an end in late 2023, its participants will have developed new monitoring tools to complement the existing ones, which can be shared throughout all countries.

Faced with the COVID-19 emergency, the MOOD teams have already begun taking specific measures, particularly in modelling the transmission of the virus, and early detection and monitoring of emerging cases. 

A project to unify and improve health monitoring in Europe and around the world

MOOD will be carried out in four stages:

  1. Five case studies on epidemic intelligence systems in European countries with different socio-economic statuses, geographies, climates and surveillance methods have already started. These are focusing on Spain, Finland, France, Italy and Serbia;
  2. Participatory work will then be carried out with epidemic intelligence stakeholders in these countries to characterise their systems, then jointly assess their needs in terms of epidemic intelligence tools and services;
  3. The tools and services will be developed and made available to the ECDC and public health agencies involved in the project, and distributed throughout Europe and beyond, particularly in developing countries;
  4. Lastly, these jointly developed new tools and services will be shared at a reasonable cost and, if possible, will be made open source.

At ANSES, the Epidemiology and Surveillance Support Unit of the Lyon Laboratory is especially involved in:

  • identifying surveillance stakeholders and end users of the tools and services developed in MOOD; drawing up an inventory of epidemiological data for case studies and identifying access procedures;
  • developing modelling approaches (machine learning) for time-series analysis, trend assessment and anomaly detection;
  • mapping antimicrobial resistance in animal populations in France;
  • modelling the circulation of resistant bacteria in the cattle sector in France via movements of these animals.

In the last phases of the project, the teams will take part in assessing the impact of the tools developed on the surveillance practices of users (decision-makers, monitoring stakeholders, the ESA platform, risk assessors).

For four years, therefore, research institutes, foundations and government public health and veterinary organisations will work together to unify and improve epidemic intelligence tools and services. In France, alongside CIRAD and ANSES, INSERM, INRAE and the University of Montpellier are also involved in the project. The European Centre for Disease Control (ECDC) is a key partner of MOOD, together with the World Health Organization (WHO), Food and Agriculture Organization of the United Nations (FAO) and World Organisation for Animal Health (OIE).

Partners involved in MOOD

  • France: ANSES, CIRAD, INRAE, Inserm, University of Montpellier, GERDAL;
  • Germany: Mundialis GmbH & Co KG;
  • Belgium: University of Antwerp, Free University of Brussels, Catholic University of Leuven, AVIA-GIS;
  • Spain: Instituto de Salud Carlos III;
  • Italy: Fondazione Edmund Mach, Istituto Superiore di Sanita;
  • Finland: National Institute for Health and Welfare;
  • Netherlands: Stichting OpenGeoHub;
  • Portugal: University of Lisbon;
  • United Kingdom: Environmental Research Group Oxford, University of Southampton;
  • Serbia: Institute of Public Health of Serbia;
  • Switzerland: ETH Zürich, Swiss Institute of Bioinformatics;
  • United States: International Society for Infectious Diseases;

Epidemic intelligence systems or how to assess the risk of emerging pathogens

With climate change, animal and human mobility, population growth, urbanisation, etc., there is now an increased risk of the emergence and accelerated global spread of new pathogens. Speed is crucial when detecting these emerging threats and assessing the risks they pose to public health: sometimes we only have a matter of days, or even hours...

In response to this, public health agencies are developing epidemic intelligence systems that rely on two types of information: "official" sources reported by public health services, and "unofficial" sources, which relay information found in the media, scientific articles, or laboratory data. 

In recent years, systems based on unofficial sources have proved particularly effective in detecting the emergence of new diseases, for example by exploiting data from Internet forums, online newspaper articles and even social networks. However, these sources generate huge amounts of data needing processing. How can we ensure early detection of emerging signals among all this information? How can they be prioritised? How should they be interpreted? How can we assess the risk of a new pathogen spreading?

MOOD intends to answer these questions by providing existing surveillance platforms with methodological and practical support in response to their needs. Besides health data, other types of data (such as on climate, migration, land use or deforestation, etc.) will be incorporated to better assess the risk of pathogen spread.

Ce projet a reçu des fonds du programme de recherche et d'innovation Horizon 2020 de l'Union Européenne sous la convention de financement No 874850 - MOOD.

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