A new project of CSIC uses computing and data science techniques to see how the containment measures that have been taken to stop the spread of COVID-19 disease are being effective. The results are going to be essential to improve social distancing strategies taken in future outbreaks of this disease or others. To carry out the research, a multidisciplinary team with experts in computing, demographics, physics, and motion study is analyzing high-resolution, anonymous bulk data that is being obtained from phone operators and map servers. These are data that explain how mobility and social contacts have changed since the confinement began.

The project, already pre-financed from the CSIC thanks to the donation received from AENA, It is coordinated by scientists José Javier Ramasco, from the Institute of Complex Systems Physics (IFISC, joint center of the CSIC and the University of the Balearic Islands) and Frederic Bartumeus, from the Center for Advanced Studies in Blanes (CEAB-CSIC) and CREAF . It has the participation of teams from the Institute of Economics, Geography and Demography (IEGD-CSIC), the Institute of Physics of Cantabria (IFCA-CSIC), the National Center for Biotechnology (CNB-CSIC), as well as scientists from the University Pompeu Fabra and the National Center for Epidemiology-Carlos III Health Institute (ISCIII).

How to lift the confinement and when

With all the information gathered, the team simulates different scenarios or strategies for social distancing and help for decision-making. Results are key both in deciding whether to trigger stricter containment and in planning for a safe and effective containment purpose. "We hope the results will serve to better understand the effects of confinement on disease spread, but also to assist in decision-making related to revocation of measures; to find out whether it is better to end confinement progressively or not. "explains Bartumeus.

"To reach this goal, the project includes several phases that are being carried out in parallel," explains Ramasco. "First, the characterization of mobility is carried out, which is being coordinated by the IFISC based on the contribution of different data platforms: information, for example, from online social networks and mobility patterns captured by mobile phone registries. In the latter case, the data is collected by the operators and companies that participate in the project and provide the research team with aggregate travel flows between zones, "the researcher details. In no case is individual information accessed.

A second aspect is the change in people's behavior due to risk perception. From the CEAB and the IEGD, surveys and mobile applications are being developed to quantify these changes, trying to estimate the adherence to personal protection measures by the population and what are the changes in the quantity and quality of the contacts that are made. "This information is crucial to understand the contagion process," says Ramasco.

Finally, all these data are part of computational models that are being developed by IFISC and IFCA in order to study the different scenarios for exiting the crisis. "Confinement has been widespread and relatively sudden, but if new outbreaks are to be avoided, it is necessary to have simulators capable of evaluating scenarios with different rates of return to normality, both by sector and by geographical area," Ramasco warns.

The epidemiology of the future

The project uses artificial intelligence and data science tools and integrates real-time, massive data on human mobility, geolocated surveys and computational models. It is a new way of doing epidemiology that combines computational epidemiology, digital demography, and models of human mobility. "The study will take into account aspects as important as the spatial distribution of the population, its age structure, and the distribution and characteristics of socio-health centers (hospitals, health centers, nursing homes). We can see how the Containment measures have changed people's mobility and behavior. "Ramasco comments.

The information and models that will be developed during this research will be made publicly available for future use following an open data model under the FAIR principles (acronym for Findable, Accessible, Interoperable, Reusable).

A second long-term objective is to establish the germ of a network of computational epidemiology in Spain, as it already exists in other countries, and a series of interoperable analytical tools, based on epidemiological theory, data science, and artificial intelligence, to inform the Decision-making in future situations of epidemiological crisis, which, as scientists say, is something that "has already happened on several occasions since 2009 and is likely to be recurring in a globalized and interconnected world like the current one."

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