MOnitoring Outbreak events for Disease surveillance in a data science context

Project Information

MOOD

Grant agreement ID: 874850

DOI

10.3030/874850

Project closed

EC signature date 6 December 2019

Start date 1 January 2020

End date 31 December 2024

Funded under

• SOCIETAL CHALLENGES - Health, demographic change and well-being

Total cost

€ 14 592 964,10

EU contribution

€ 13 910 747,25

Coordinated by

CENTRE DE COOPERATION INTERNATIONALE EN RECHERCHE AGRONOMIQUE POUR LEDEVELOPPEMENT - C.I.R.A.D. EPIC
France

Periodic Reporting for period 4 - MOOD (MOnitoring Outbreak events for Disease surveillance in a data science context)

Reporting period: 2024-01-01 to 2024-12-31

Summary of the context and overall objectives of the project

The emergence of the new coronavirus virus (COVID-19) at the end of 2019 highlighted the importance of early detection, monitoring, and assessment of emerging infectious diseases (EID) to help epidemic intelligence (EI) staff at public/animal health agencies and decision-makers in the early detection, monitoring, assessment and where relevant, management of epidemics.

The MOOD project aims to harness data mining and analytical techniques to the big data originating from multiple sources to improve the detection, monitoring, and assessment of EID.

MOOD outputs are designed and developed with EI staff to ensure routine use during and beyond MOOD. In addition, the consortium will set up an international not-for-profit association (INPA) to ensure the sustainability of the MOOD innovations and platform. The work will be conducted through five case studies: Avian influenza (AI), West Nile virus (WNV), Dengue, Zika and Chikungunya, Leptospirosis, Tick-borne encephalitis (TBE), Antimicrobial resistance (AMR), and disease X (COVID-19).

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the RP4, we reinforced the links and collaborations between WPs and in particular:

1. We continued the implementation of the learning loops (WP1) through six disease-specific modules (WNV, TBE, AMR, COVID-19, CHIK/DEN, HPAI) and generic modules (covariates visualisation and exploitation, and PADI-web media-monitoring tool, GeoNetwork data catalogue). We continued to exchange on the scientific advances and MODO platform through in-person meetings between task leaders, including the WP leaders, on a trimestral basis.

2. During the RP4, the research and modelling activities were finalised (WP2, WP4), especially on the priority diseases chosen with end-users for the implementation of the MOOD platform (TBE, WNV, HPAI, AMR) and two modelling works for the remaining diseases, LEPTO and TULA were initiated – thus allowing to finalise all research contributions for all disease models selected (WP4). Many of the conducted studies provide the code, the data and covariates (where applicable) to nourish an open science and FAIR (findable, accessible, interoperable & reusable) research. Completed disease profiles were achieved for all disease models (WP2) – they provide a very detailed and helpful description of the situation and perspectives for surveillance, control and research (including gender aspects) for Europe and linked to the MOOD platform.

3. The information workflow to feed the MOOD platform, which begins from Big heterogeneous data sources to provide standardised covariates according to spatial, temporal, and thematic dimensions and quality evaluation for the MOOD platform, continued during the RP4. Please check the MOOD platform, the data and the tools available (WP5): https://mood-platform.avia-gis.com/core.

4. We delivered the business plan for the MOOD not-for-profit association (INPA) to be established by the end of the project to allow for the sustainability of the MOOD tools and services (WP6). As of December 2024, eight partners have accepted membership.

5. We continued to promote the project achievements by regularly updating the MOOD website and social media and covering the two events: the MOOD launch of the MOOD platform and workshop with end-users at ITM, Antwerp, Belgium, in October 2024 and the scientific conference and final meeting of the project at the ISS, Rome, Italy, in November 2024 (WP6).

6. The Coordination, in link with the two external advisors on ethics and data protection, continued to monitor the respect of ethics and data protection laws and principles for all the project's tasks and activities (WP7 and WP8), including one gender workshop on the perceptions of how gender is considered in the EI process, from detection of events to analysis and modelling and communication and awareness raising (see WP7 and task on gender).

Overview of the main results and their exploitation:

a) We have had 50 deliverables among which 25 are publicly available.

b) We have provided freely available and open access datasets for many environmental, climate, host distribution factors that can be used as covariates for different purposes, from simple epidemiological analysis and from risk assessment to modelling.

c) Disease profiles complete with dashboards for all diseases in MOOD.

d) Risk mapping model outputs for TBE, WNV, HPAI, AMR, bat-borne, rodent-borne disease-X for Europe and finalising for TUL, CHIK, DEN.

e) The project results from point b), c), d) are available at the MOOD platform, open access and freely available at: https://app.mood-h2020.eu/.

f) We have provided code for 70 works and created several R packages helpful for scientists and epidemiologists at PH agencies to better control arboviral disease outbreaks in Europe (arbocarto) and phylogenetic analysis for researchers and modellers (phycova) as well as the following tools.

g) We have had 181 publications, 156 of which are in peer-reviewed papers.

h) We have organised two summer schools on machine learning and space-time analysis, with over 79 participants from 24 countries.

i) We have organised one test of the prototype MOOD platform in June 2023 and a final workshop launch and test of the MOOD platform in October 2024 and November 2024 with a total of 93 participants from 23 countries.

j) We have had over 26 newsletters and 33 webinars during the project duration.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The collective thinking of MOOD partners on the impact pathway has allowed identifying the output and outcome contributing to the four MOOD impacts:

a) Strengthen EU preparedness to address (re-)emerging infectious disease threats by making available the appropriate technology and tools to support an appropriate public health response.

b) Contribute to the EU One-Health action plan against antimicrobial resistance.

c) Contribute to the digital transformation of health and care in the EU Digital Single Market context.

d) Contribute to the Sustainable Development Goals (SDG):
• SDG03: (i) combat epidemics, and (ii) strengthen capacities for early warning and response to health risks.
• SDG13: (i) integrate climate change measures into national policies, strategies, and planning, and (ii) improve education, awareness, and institutional capacities on climate change adaptation, impact reduction, and early warning.

MOOD impacts are linked to the appropriation of MOOD tools and services and changes in EI practices in national and European PH/AH agencies.

We defined 22 impact indicators for the entire project duration. During the RP3 reporting period, we monitored 22 indicators. All the targets of the KPIS have been reached except those relying on the observation of the changes of practices. The online survey about the impact brought a reliable insight of the perceptions of the MOOD tools and services as delivered in late 2024. It validated the strong hypothesis about the main outcomes being the integration of MOOD tools and services in the routine of the PH and Ah agencies. The sustainable changes of practices will be based on the uses on yearly or monthly basis of the whole set of modules for exploration (to review hypothesis and detect new patterns), for a better detection integrating EBS and IBS sources, to produce documents for communication and for modeling. Although the probability of a regular use was high for Padi-Web, the covariates and the risk maps, some improvements are expected to mitigate the uncertain use of some modules.

MOOD Platform architecture

Figure 1: The process of co-construction of MOOD tools and services between end-users at public/anim