The effectiveness of social distancing strategies in the face of an epidemic has been analysed
UC3M/DICYT The complete isolation of the population in the face of an epidemic such as COVID-19 is a strategy that requires subsequently adopting active measures in order to maximise its effectiveness, such as conducting large scale diagnostic tests, isolating people with symptoms and identifying those with whom they have had contact. This is one of the main conclusions from a study conducted by a team of researchers from the Universidad Carlos III de Madrid (UC3M), the University of Zaragoza, the Massachusetts Institute of Technology (MIT) and the ISI Foundation in Italy.
The results, based on mathematical models and real-world data from the mobility flow of peoples, indicate that complete isolation of the population requires subsequently adopting active measures,otherwise, a new outbreak could occur. “Using contact matrices at this level of detail we can understand the effect of strategies such as closing schools, places of work or restaurants and other non-essential spaces”, explains Esteban Moro, a professor in the Department of Mathematics at the UC3M and co-author of this study, currently a guest professor at the MIT Media Lab.
“Before the need to adopt measures to contain and eradicate the current COVID-19 pandemic, we have simulated the evolution of the epidemic in a real-world population, in this case within the area of Boston. Our models show us that a new wave of infection is highly likely in almost all scenarios. The fundamental conclusion is that passive containment policies should be combined with other more aggressive measures,” noted Yamir Moreno, theoretical physicist, co-author of the study and responsible for the Group of Networks and Complex Systems (COSNET) at the Institute of Biocomputation and Physics of Complex Systems (BIFI) at the University of Zaragoza.
To conduct this research, the team of scientists used real-world mobility data from mobile phone users within the US,provided by Cuebiq Inc’s Data for good programme. Cuebiq Inc. is a company that records the locations of its users and aggregates them anonymously. They also analysed data from a Boston area census to construct a joint network of locations in three layers (community, schools and homes) and used the SIR model to model the spread of epidemics. “We are also currently working with real-world mobility data in New York City, which is the current epicentre for the epidemic in the US”, Esteban Moro noted. “If high resolution mobility data is available, our approach can be easily replicated for new cities or countries to measure the impact of social distancing strategies before the epidemic”, he added.
This research seeks to obtain data that will help to evaluate the impact of social distancing strategies that have been adopted in different countries in an effort to fight COVID-19, as well as define how long they should be in force and which are the most effective measures. They also analyse the probability that a second outbreak could occur later or the best way to prepare for a hypothetical second wave.
These preliminary results have just been published under a Creative Commons license on a website so that they are available to the authorities and scientific community, that can be used to contrast, update and optimise the analysis with new data. The company Zensei Technologies S.L., based in Madrid, is also collaborating on this project in response to the health crisis caused by COVID-19.
Effectiveness of social distancing strategies for protecting a community from a pandemic with a data driven contact network based on census and real-world mobility data. David Martín-Calvo, Alberto Aleta, Alex Pentland, Yamir Moreno, Esteban Moro. https://covid-19-sds.github.io