The European Commission recently awarded two grants under the H2020 program
to VEO and MOOD. The call was aimed at “Mining big data for early detection of
infectious disease threats driven by climate change and other factors” and invited
proposals with the expectation that they would develop:
- the technology to allow the pooling, access, analysis and sharing of relevant data,
including next generation sequencing;
- the innovative bio-informatics and modelling methodologies that enable risk
modelling and mapping; and
- the analytical tools for early warning, risk assessment and monitoring of
(re)emerging infectious disease threats.
The call text also stated that the ready-to-use analytical tools and services that are
developed should be based on an assessment of the needs of potential end-users in the
Member States and on European level, should as far as possible build on and be
compatible with existing European initiatives, and should remain available for public
use at the end of the project at a reasonable cost.
VEO stands for Versatile Emerging Disease Observatory. It builds from parts of the
partnership developed through the COMPARE project, but with refocusing on emerging
diseases, new data types (metagenomics, system serology, citizen science) in addition to
public data. It aims to work towards prediction of outbreak risk using public data as
well as specifically collected data, and development of tools for validation of signals
from such a system using advanced laboratory tools (metagenomics, systems serology).
MOOD stands for Monitoring Outbreak Events for Disease Surveillance in a Data Science
Context. It builds upon the EDEN and EDENext collaborations. MOOD is driven from the
end-user needs and ex-ante and in itinere building of the impact of new tools and
services embedded in the current epidemic intelligence systems at supra national and
national health agencies. It aims to develop sustainable tools and services using public
data for the early detection, assessment, and monitoring of current and potential
infectious diseases threats in Europe in a context of global change including climate
change.
The table below shows the main emphasis of VEO and MOOD activities.
There is no absolute separation of activities but the overlap is limited. VEO is more
linked to conditions associated with the apparition of an emergence, and MOOD is more
linked to the early detection, assessment and monitoring of an emergence.
| VEO |
MOOD |
| Prediction of outbreak risk, tracking in reservoir |
Detection, assessment and monitoring of
outbreaks in humans and animals |
Public data complemented with newly generated
metagenomic, systems serology, citizen science |
Public data (indicator-based and event-based),
and in particular web data as new sources |
Disease system analysis to look for data to
predict |
Disease system analysis to look for data to detect |
| Driver change based analysis |
Indicator and event-based surveillance, driver
data use to contextualize the modelling |
Research focus, new methods, some case studies
for public health application |
Epi interpretation of data, case diseases and case
study countries for public health application |
Pilot projects generating large datasets
metagenomic and systems serology |
Working with existing sources of data (no new
survey), including web scraping / text mining |
Disease specific input comes from experts within
consortium |
Disease specific input comes from internal and
external experts |
| |
Impact assessment and monitoring during project
implementation |
Exploring potential for future surveillance and
early warning of new data types, sustainability
of data hubs |
Sustainability of data streams and data processing
accounting for spatio-temporal heterogeneity |