Effects of Physical Distancing Measures

The COVID-19 pandemic poses an extraordinary challenge to the world, our societies, health care systems, and economies. Currently the virus has been confirmed in more than 200 countries and territories.  

In this context, many countries are using physical distancing policies (from school closures to travel restrictions or full lockdowns) as tools to reduce disease spread, looking to avoid (or flatten) the curves of cases and deaths, seen already in so many countries.  

While there is an obvious relation between reduced social contacts and the speed at which the disease spreads there is little knowledge and a general lack of tools to understand the secondary effects of the containment measures. For instance, how will social distancing measures effect our societies, what socioeconomic effects will they have, how suitable are they in vulnerable and impoverished contexts, and are they sustainable over time. 

In this unprecedented situation there is a clear need for real time information. However, to leverage the full potential of Data Science, Big Data, Complex Systems Theory, Epidemic Modeling, and Computational Social Science requires joint efforts between scientific institutions, governments, and international organizations.

It is central to provide evidence and tools that allow for timely action, and for identifying the needs of the most vulnerable, in order to balance the severity of containment measures while mitigating the socioeconomic impacts that this pandemic will surely have. 

Through data and data science partnerships with private sector companies and leading research groups, Magic Box — UNICEF's big data initiative — is working to provide data, tools and insights that allow timely monitoring of physical distancing, evidence on the suitability and sustainability of mobility reductions for low income settings, and better models that allow a better understanding and balancing of the potential impact of these measures on the disease as well as on the underlying communities. 

We're currently producing insights for 10 UNICEF programme countries: Colombia, Cote d'Ivoire, India, Indonesia, Malaysia, Myanmar, Mozambique, Mexico, Nigeria, and Ukraine. We're working with partners all over the world to increase data and analytical capacity and forming collaborations with leading research groups and private sector companies to help fight this disease.  

We focus on: 

1. Building tools to monitor and evaluate aggregated metrics of physical distance and human mobility, with special attention to the bias of big data 
2. Developing methodologies to understand the suitability of containment measures (including mobility restrictions) for the most vulnerable contexts — low income settings, slums, refugee camps, etc.
3. Disaggregating the impact of secondary effects across age, gender, and socioeconomic status  

Manuel Garcia-Herranz, Chief Scientist 

Dohyung Kim, Principal Researcher and Remote Sensing Lead 

Vedran Sekara, Principal Researcher and Machine Learning Lead 

Frank Schlosser 
Humboldt University Berlin & Robert Koch Institute 

Laura Alessandretti 
Section for Cognitive Systems, Technical University of Denmark  

Sune Lehmann 
DTU Compute, Technical University of Denmark  

Rachel Oidtman 
Perkins Lab, University of Notre Dame 

Alex Perkins 
Perkins Lab, University of Notre Dame 

Kelton Minor 
Center for Social Data Science, University of Copenhagen 

Esteban Moro 
Massachusetts Institute of Technology & Universidad Carlos III de Madrid 

Mohsen Bahrami  
Massachusetts Institute of Technology 

Piotr Sapiezynski 
Khoury College of Computer Sciences, Northeastern University 

Global Data Science Project for COVID-19