[LUM#13] In the Mood for CoV
Mood’s goal is to improve global health surveillance and accelerate decision-making by providing health agencies with the right epidemiological intelligence tools. This European project, launched in January, couldn’t have come at a better time.
While the pace of science is not the same as that of current events, the two sometimes collide in a way that is, to say the least, abrupt. Conceived in a world where COVID-19 did not yet exist, the Mood project (Monitoring outbreaks for disease surveillance in a data science context)—which brings together 25 partners in 12 European countries and the U.S.—aims to assess and prioritize the needs of health agencies in order to collaboratively develop new epidemiological intelligence tools. The goal is to improve the early detection of emerging pathogens and enable public health officials to respond as quickly as possible.
Launched last January, the project has—by necessity—become a full-scale experiment. “With the onset of COVID-19, the European Commission very quickly asked us to redeploy our resources to address the agencies’ immediate needs,” explains Renaud Lancelot, a researcher at the Astre* laboratory and the project’s scientific coordinator. “The European Centre for Disease Prevention and Control (ECDC), based in Stockholm, was our main point of contact during this period.”
A Matter of Time
Because in times of crisis, everything comes down to time, the team led by Renaud Lancelot carried out the research, data collection, and analysis that the overwhelmed health agencies were unable to perform. “In particular, we were asked to focus on human mobility and to provide agencies with maps and time series data illustrating these patterns so they could be incorporated into models of the epidemic’s spread”— and thus to test various scenarios for controlling and monitoring the pandemic, foremost among which is lockdown.
Another focus of the Mood consortium’s efforts is the analysis and evaluation of the data produced by these same agencies.The ECDC has thus compiled a time series of mortality cases in Europe and worldwide. This work has been unanimously praised, yet—paradoxically—the agencies themselves have been unable to analyze it due to a lack of time and resources.“ We synthesized this time series into a dashboard so that the data collected by the ECDC could be used by the agency and so that decision-making could be based on the most up-to-date information possible.”
Detecting Weak Signals
Mood also aims to improve the detection of early warning signs of an emerging outbreak. “These signs must indicate a proven potential danger,” explains Renaud Lancelot. “In the case of COVID-19, there have already been several coronavirus outbreaks, so we know this can lead to a major pandemic.”
In this hunt for weak signals, anything goes. “We use every available means to detect health risks among all the accessible information , ” explains the epidemiologist. “If you want to draw a parallel, epidemiological intelligence is a bit like espionage. ” While researchers rely on official reports produced by agencies, they don’t overlook unofficial information either: social media analysis (see box), rumors, nonspecific signs… And here again, it’s all about the tools.
Generating secure data streams for data collection, creating dynamic maps and charts to track trends, developing text-mining software and smartphone apps… The range of tools for estimating the probability of a virus emerging is diverse; however, as Renaud Lancelot points out, only their adoption by health agencies and the establishment of a rapid information-sharing system can have a real impact on public health. “That’s precisely why it’s so important to invest in fundamental research—to have the tools, methods, and networks already in place—because when a crisis strikes, there’s no room for improvisation.”
Social Media: A Powerful Tool for Prevention
The goal of the DigEpi project—short for “digital epidemiology”—funded bythe National Research Agency and the Occitanie region, is to predict the level of local transmission through the analysis of social media. The principle is simple: analyze what people are saying on social media, particularly Twitter, by feeding keywords such as “Covid-19,” “hydroxychloroquine,” and “lockdown” into algorithms. “We then observe how these topics fluctuate and compare these trends with mathematical models of actual infections in order to anticipate a potential spike in infections,” explains Benjamin Roche, a research director atthe IRD.
These analyses also provide the anthropologists working on this project with valuable insights into the dynamics of the sentiments expressed by the public. They also help characterize the public’s behaviors and reactions to the measures taken. “Will people isolate themselves, go out, wear masks, spread rumors, or seek information about treatments? All these factors help us better understand the public’s perception of the various measures,” explains the epidemiologist. Launched in April in four major French cities, including Montpellier, this study will last 18 months before potentially being expanded to all French departments as well as to Mexico.
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*UMR ASTRE: Animals – Health – Territories – Risks – Ecosystems (CIRAD – INRAE)