Data-driven solutions to the Covid-19 pandemic, a scientist’s opinion
Dr Renaud Lancelot, a veterinary epidemiologist coordinating the recently funded Horizon 2020 project: MOnitoring Outbreak events for Disease surveillance in a data science context (MOOD), highlights how networks of data-science experts are helping public health agencies prepare for and address emerging diseases.
How will the MOOD project contribute to tackle pandemics?
Renaud Lancelot: MOOD focuses on the use of big data to improve the surveillance of emerging infectious diseases. Our multidisciplinary team brings together genomic data, text mining and environmental data, to co-construct outputs that meet the needs of public health agencies in terms of epidemic intelligence, and that are useful for triggering appropriate control measures.
We are working with 25 partners from across 12 countries, including the global network of experts that are part of the International Society of Infectious Diseases’s ProMED (Program for Monitoring Emerging Diseases). We want to work with existing networks and resources for the benefit of public health agencies to improve their practice and capacity to detect and react to known or unknown emerging infectious diseases. We are not creating an additional surveillance system.
Although existing surveillance systems are becoming more effective at detecting emerging diseases, their weakness lies in reaching decision makers and prompting them to adopt measures quickly. By working directly with public health agencies and at their interface with decision makers, we hope to overcome this issue.
We will collaborate closely with researchers on the VEO (Versatile Emerging infectious disease Observatory) project, also funded in the latest H2020 call for research on epidemic intelligence. VEO uses traditional and new types of data, including data from the thawing of permafrost in Siberia, to forecast disease outbreaks.
What challenges are associated with the use of data to monitor the emergence of disease?
Renaud Lancelot: Through ongoing discussions with some public health agencies we are assessing and prioritising their needs and designing tools (models, software, training programmes…) and secure flows of information to address them. This requires us to identify information sources, organise, standardise and harmonise many different types of data.
We are keen to make our outputs sustainable; any tools or services we design will be open source (as much as possible) and available free of charge, or at a moderate cost, to achieve sustainability. We are also looking to make our outputs available more broadly to other organisations, such as sub-national agencies or patient associations.
We pay a lot of attention to the appropriation and dissemination of our outputs, as well as all the ethical questions arising from the use of a wide range of survey data, including social media. When necessary, the validation of research protocols by ethical committees, as well as individual informed consents are sought to avoid any data privacy issues. This is crucial to build respect and mutual confidence between scientists and the people who are producing the data.
What do you think of the EU’s reaction to Covid-19 in terms of mobilising research funds?
Renaud Lancelot: The European Commission’s response was very quick. As soon as Covid-19 was identified we were asked by the Commission to re-orient the project towards detecting the virus, predicting its spread and informing the public health response. Within hours, links were established between our expert networks across Europe and the Commission representatives so they could rapidly access research and data about the spread of the virus.
We look forward to sharing the outputs of our project with public health agencies in European countries and in countries around Europe, such as North Africa and Turkey. We have also started collaborating with regional networks in Africa and the Caribbean region, to strengthen international solidarity and the global capacity in early warning and reaction to emerging infectious diseases.
How are other data-driven EU initiatives helping to fight Covid-19?
Renaud Lancelot: We will be coordinating our efforts with the EC Covid-19 data platform and highlighting what we are doing there. Initiatives focusing on countries outside Europe, like in Africa for example, are key to increase the global resilience to disease outbreaks. We need to ensure that the global science information system is prepared, and this requires identifying and working on the weakest parts of the system to help fix problems.
How will this research help us to be better prepared for the future pandemics?
Renaud Lancelot: Undoubtedly, Covid-19 has increased public awareness of data-driven research. One issue is that the Commission is not funding long-term projects. Long-term data sets are crucial for understanding the reasons behind disease emergence, and we know from previous experience that there are often multiple factors. For example, a 10-year project investigating the role of climate change in the increase in tick-borne diseases in Northern and Central Europe, found that a reduction in social services provision, which led to more people going out foraging, was a key factor. I expect that we will find multiple causes associated with the emergence of Covid-19.
By using our experience to identify and interpret the most relevant data, we can support public health agencies and help decision makers react faster, and appropriately, to emerging diseases. It is important to note that we aren’t just working on Covid-19, we keep looking for weak signals of what could be the next health threat in order to improve preparedness and early response. To improve the coordination of measures and responses, we will be collaborating with SONAR-Global, a network of social scientists that focus on understanding how people perceive disease risk and how this perception changes during disease emergence. Enhancing the communication between complementary networks of experts will ensure everyone benefits from the data and enable faster detection of and faster reaction to emerging diseases.
