Hot Topics in Computing: Effectiveness of social distancing strategies for protecting a community from a pandemic


The current situation of emergency is global. As of March 22nd 2020, there are more than 23 countries with more than 1.000 infected cases by COVID-19, in the exponential growth phase of the disease. Furthermore, there are different mitigation and suppression strategies in place worldwide, but many of them are based on enforcing, to a more or less extent, the so-called social distancing. The impact and outcomes of the adopted measures are yet to be contrasted and quantified. Therefore, realistic modeling approaches could provide important clues about what to expect and what could be the best course of actions. Such modeling efforts could potentially save thousands, if not millions of lives. Our report contains preliminary results that aim at answering the following questions in relation to the spread and control of the COVID-19 pandemic:

  • What is the expected impact of current social distancing strategies?
  • How long should such measures need to be in place?
  • How many people will be infected and at which social level?
  • How do R(t) and the epidemic dynamic change based on the adopted strategies?
  • What is the probability of having a second outbreak, i.e., a reemergence?
  • If there is a reemergence, how much time do we have to get ready?
  • What is the best strategy to minimize the current epidemic and get ready for a second wave?

Key findings:

  • School closures do not have a major impact on controlling the epidemic, despite closing them, infections keep occurring within the households and the community layers.
  • Passive social distance strategies are not enough to contain the epidemic, indicating that active strategies need to be established. For instance, large scale testing, remote symptoms monitoring, isolation and contact tracing.
  • School closures and self-distancing at 90% of adoption is a feasible strategy for minimizing the effects of the epidemic, but only if they are applied for a long period of time.
  • A full confinement is not feasible and will not solve the problem, without active measures in place after the confinement, since there would be a new outbreak.
  • If high resolution mobility data is available, our data-driven approach with real world data can be easily replicated for new cities or countries to measure the impact of social distance strategies and the epidemic.

Prof. Esteban Moro is a researcher, data scientist and professor at MIT IDSS (visiting) and Universidad Carlos III (UC3M) in Spain (tenured). He was previously researcher at University of Oxford. He is affiliate faculty at Joint Institute UC3M-Santander on Big Data at UC3M and the Joint Institute of Mathematical Sciences (Spain). He has published extensively throughout his career (more than 80 articles) and have led many projects funded by government agencies and/or private companies.
Esteban's work lies in the intersection of big data and computational social science, with special attention to human dynamics, collective intelligence, social networks and urban mobility in problems like viral marketing, natural disaster management, or economical segregation in cities. Apart from his academic career he has worked closely with companies like Twitter, Telefónica or BBVA in the use of massive datasets to understand problems like how humans communicate, how to political opinion spreads in social networks or building alternative wellbeing indexes. He has received numerous awards for his research, including the “Shared University Award” from IBM in 2007 for his research in modeling viral marketing in social networks and the “Excellence in Research” Awards in 2013 and 2015 from UC3M.
Esteban's work appeared in major journals including PNAS or Science Advances and is regularly covered by media outlets The Atlantic, The Washington Post, The Wall Street Journal, El País (Spain).

Prof. Alex 'Sandy' Pentland directs MIT Connection Science, an MIT-wide initiative housed within IDSS, helped create and direct the MIT Media Lab, and has an h-index of 137. He is on the Board of the UN Foundations' Global Partnership for Sustainable Development Data, co-led the World Economic Forum discussion in Davos that led to the EU privacy regulation GDPR, and was central in forging the transparency and accountability mechanisms in the UN's Sustainable Development Goals. His recent books are Trusted Data (MIT Press), Social Physics (Penguin), and Honest Signals (MIT Press).