How does data science boost epidemiological research?
Related topicsCreating a digital society eHealth Advancing in digital science and infrastructures Future and Emerging Technologies Future & Emerging Technologies (FET) (Unit C.3)
Data science is a quickly growing field mainly associated with new technologies (especially in the terms of data mining and big data). Its purpose is to analyse and understand a particular phenomenon by extracting knowledge from available data. Based on the data, it combines interdisciplinary knowledge allowing analysis of a particular phenomenon, such as the emergence and spread of contagious diseases.
This particular topic has been covered multiple times in the history of EU research funding opportunities. More than 10 years ago, in 2009, the EPIWORK project gained funding support within the 7th Framework programme by the Future and Emerging Technologies programme. Its aim was to develop the appropriate tools needed for design of epidemic forecast infrastructures, the research involved collecting epidemiological data during the 2009 outbreak of H1N1 influenza.
EPIWORK ran for 54 months and developed GLEaM Model (Global Epidemic and Mobility Model) – a computational system producing realistic simulations of the global spread of infectious diseases by combining real-world data on populations and human mobility with elaborate stochastic models of disease transmission. The revolutionary tool integrated information never before included in such forecasting, for example daily airline passenger traffic, censuses, hospital admissions and medical services, funeral attendances, and even information submitted from mobile phones. Later GLEAM was used for instance in forecasting spread of Ebola in 2014. The last version of GLEAM was released in October 2019.
On 1 January 2020, another project analysing the spread of contagious diseases began: the MOOD project. As the project coordinator, Dr Renaud Lancelot pointed out:
As soon as it started, there was the COVID-19 epidemic. And the European Commission very quickly asked us to focus on this particular issue. It does not mean that we stopped everything else, but as far as possible, we targeted our activities at COVID 19.
MOOD focuses on monitoring outbreak events for disease surveillance, while developing its own computational model based on data science. The MOOD coordinator, Dr Renaud Lancelot, specifies the importance of data science in epidemiological activities:
The general purpose of MOOD is to work in a scientific field called epidemic intelligence, which is about early identifying weak signals of starting epidemics, and triggering quick reaction to stop them before they spread. We work with big data, which are available - but the most important is to know what you’re looking for. Once you know it, you can spot the first signals of emerging diseases. The most important aspect of MOOD is to identify and assess the needs of public-health agencies in this domain, and co-design and co-develop tools addressing these needs, so that they are actually used in daily practice. For the case of COVID-19, the European Centre for Disease Prevention and Control (ECDC) asked us to support their modelling teams with human mobility data, and produce visualising tools to facilitate their rapid risk assessments. We also implemented sociological surveys to understand the decision making process following the production of epidemiological signals. That is a crucial aspect for shortening the time lag between the detection of such signals, and the actions to control the spread.
These experiences prove that data science can give the researches huge amount of information related to both individual and social behaviour, which can be used across multiple scientific disciplines. It also shows the importance and efficiency of cooperation between soft and hard sciences.
FET-Open and FET Proactive are now part of the Enhanced European Innovation Council (EIC) Pilot (more specifically the Pathfinder), the new home for deep-tech research and innovation in Horizon 2020, the EU funding programme for research and innovation.