Effects of Physical Distancing Measures
UNICEF's big data initiative, Magic Box, studies and predicts the effects of mobility on Covid-19 responses
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.
Magic Box
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:
- Building tools to monitor and evaluate aggregated metrics of physical distance and human mobility, with special attention to the bias of big data
- Developing methodologies to understand the suitability of containment measures (including mobility restrictions) for the most vulnerable contexts — low income settings, slums, refugee camps, etc.
- Disaggregating the impact of secondary effects across age, gender, and socioeconomic status
Reports
10 Oct 2020
This report investigates how the timing of physical distancing policies introduced by governments to mitigate COVID-19 transmission affects both human mobility (measured through aggregated mobile phone locations) and COVID-19 cases. We look at 10 UNICEF countries and compare to 2 non-UNICEF. Our results suggests that strict measures can be effectively used, only once, and cannot last for several months without losing effectiveness.
05 June 2020
This report shows an analysis on the changes in human movements as a result of governmental physical distancing policies related to COVID-19. It analyzes data from 10 programme countries and aims to understand some fundamental differences in the effects of those policies according to poverty and urban-rural contexts.
01 May 2020
This report includes insights for additional programme countries: Myanmar, Mozambique, and Ukraine. Moreover, the report includes an air pollution analysis for 10 programme countries, showing how air pollution has changed due to containment measures. The report also contains an extended exploration section including: the impact on work (USA, Spain), the sustainability of containment measures (Germany), and disaggregating mobility (into age and gender) in Tokyo, showing that young people are the ones that are most affected by lockdowns.
14 April 2020
Along with updates to the previous countries, this deck includes insights for Programme Countries Colombia and Cote d'Ivoire. It also contains an exploration section for countries currently experiencing an epidemic (USA) and countries ahead of the epidemic curve (Germany).
3 April 2020
Magic Box now includes analysis of data for 5 UNICEF Programme Countries: India, Indonesia, Malaysia, Mexico, and Nigeria; showing major change on movement behavior correlating with implemented policies.
30 March 2020
This report studies changes in movement across Germany and the USA, along with behavioral changes in the USA, to begin exploring how well physical distancing measures are working. It also uses human mobility patterns and disease characteristics toward epidemic forecasting for Spain and Brazil.
03 Mar 2020
This reports explains its usage of global flight traffic in predicting the spread of coronavirus, and details how UNICEF has shifted efforts towards understanding the secondary impact of responses such as school and factory closures, and even panic, on societies. We outline doing proofs-of-concept to quantify these and discuss scaling up our data science capacity.
The MagicBox Science Team
Manuel Garcia-Herranz, Chief Scientist
Dohyung Kim, Principal Researcher and Remote Sensing Lead
Vedran Sekara, Principal Researcher and Machine Learning Lead
Our Collaborators and Partners
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
David Pastor Escuredo
UNICEF Colombia
Marton Karsai
Department of Network and Data Science, Central European University
Ludovico Napoli
Department of Network and Data Science, Central European University
Data and Funding Partner
Vodafone
Data Partners
Cuebiq
Telefonica
Amadeus