Disease Prevention Maps

Facebook Disease Prevention Maps are designed to help public health organizations close gaps in understanding where people live, how people are moving, and the state of their cellular connectivity, in order to improve the effectiveness of health campaigns and epidemic response. These datasets, when combined with epidemiological information from health systems, assist nonprofits in reaching vulnerable communities more effectively and in better understanding the pathways of disease outbreaks that are spread by human-to-human contact.

Features

Privacy-Preserving

All maps that use Facebook data are aggregated, and privacy-preserving measures are applied.

Global Reach

Movement Maps and Network Coverage Maps are derived from Facebook data that is global in nature.

Timeliness

Our Disease Prevention Maps can be generated quickly as the situation on the ground unfolds. This allows agencies to respond to changing circumstances.

One of the most important pieces of information we need to respond to epidemics is where people are moving. This kind of data can be integrated into our epidemiological models to help us estimate how quickly a disease might spread, and where to put resources to contain it.”

Caroline Buckee, Assistant Professor of Epidemiology, Harvard School of Public Health

Who Uses Disease Prevention Maps

Universities and researchers

Researchers and universities can combine information from health systems with aggregated and anonymous Facebook movement data to glean insights about where the next case of cholera or drug-resistant malaria is likely to occur.

This map shows movement between Greater London and surrounding areas by day. These data are useful to public health...

Map Types

Movement Maps

Aggregate information from people using Facebook on their mobile phones with Location History enabled, showing movement between two points. The methodology can be found here
Network Coverage Maps

Show where people on Facebook have cellular connectivity. All data is privacy-protected, aggregated, and visualized as a polygon of network coverage by speed. More information can be found here
Co-location Maps

These improved co-location insights help researchers better understand general movement patterns like commute patterns, and the probability that people in areas with disease outbreaks will come in contact with new populations. These maps are important to understand the actual chance that a disease will be spread by human-to-human contact.
Travel Patterns

Travel Patterns show comparisons of the number of Facebook users moving large distances, like air or train travel. These comparisons are based only on users who have opted into sharing precise location data from their device with our mobile app through location services. We are initially looking exclusively at international travel. The counts of travel patterns are updated daily. A minimum of 1000 unique location services users is required for us to surface the metric in order to minimize re-identification risk.

Case Studies

This paper presents a large-scale analysis of the impact of lockdown measures introduced in response to the spread of novel coronavirus disease 2019 (COVID-19) on socioeconomic conditions of Italian... This paper presents a large-scale analysis of the impact of lockdown measures introduced in response to the spread of novel coronavirus disease 2019 (COVID-19) on socioeconomic conditions of Italian citizens. We leverage a massive near–real-time dataset of human mobility and we model mobility restrictions as an exogenous shock to the economy, similar to a natural disaster.

Disease Prevention Maps

Economic and social consequences of human mobility restrictions under COVID-19

This paper presents a large-scale analysis of the impact of lockdown measures introduced in response to the spread of novel coronavirus disease 2019 (COVID-19) on socioeconomic conditions of Italian citizens. We leverage a massive near–real-time dataset of human mobility and we model mobility restrictions as an exogenous shock to the economy, similar to a natural disaster.

Researchers analyzed key mobility trends before and during COVID-19’s lockdown in Italy, one of the first and more affected countries. This research looks at how many people stayed home during... Researchers analyzed key mobility trends before and during COVID-19’s lockdown in Italy, one of the first and more affected countries. This research looks at how many people stayed home during lockdown; what happened between the announcement and the actual implementation of restriction measures; and how population changed during lockdown, checking if people left cities and moved to marginal areas or urban belts.

Disease Prevention Maps

Mobility in Italy Under Lockdown

Researchers analyzed key mobility trends before and during COVID-19’s lockdown in Italy, one of the first and more affected countries. This research looks at how many people stayed home during lockdown; what happened between the announcement and the actual implementation of restriction measures; and how population changed during lockdown, checking if people left cities and moved to marginal areas or urban belts.

The Social Analytics Lab at the MIT Initiative on the Digital Economy (IDE) just released a comprehensive study of the cost of uncoordinated responses to COVID-19. We combined daily, county-level data... The Social Analytics Lab at the MIT Initiative on the Digital Economy (IDE) just released a comprehensive study of the cost of uncoordinated responses to COVID-19. We combined daily, county-level data on shelter-in-place and business closure policies with movement data from more than 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States.

Disease Prevention Maps

The Cost of Uncoordinated Responses to COVID-19

The Social Analytics Lab at the MIT Initiative on the Digital Economy (IDE) just released a comprehensive study of the cost of uncoordinated responses to COVID-19. We combined daily, county-level data on shelter-in-place and business closure policies with movement data from more than 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States.

Facebook uses anonymized mobile phone location data to understand the probability of "colocation" between administrative areas over time -- a proxy for social contact. Use this dashboard to explore... Facebook uses anonymized mobile phone location data to understand the probability of "colocation" between administrative areas over time -- a proxy for social contact. Use this dashboard to explore the network of colocation probabilities in the UK. Colocation is defined as two individuals within the same ~0.6 by ~0.6 km area during the same five minute period in a given week.

Disease Prevention Maps

London School of Hygiene & Tropical Medicine creates dashboards visualizing trends in Facebook Colocation Data for COVID19 response

Facebook uses anonymized mobile phone location data to understand the probability of "colocation" between administrative areas over time -- a proxy for social contact. Use this dashboard to explore the network of colocation probabilities in the UK. Colocation is defined as two individuals within the same ~0.6 by ~0.6 km area during the same five minute period in a given week.

In the News

Facebook presenta una iniciativa para ayudar a planificar campañas de salud pública y anticiparse a brotes de enfermedades Spain: Zona Movilidad Facebook lanza mapas de América Latina en alta resolución para ayudar en salud e infraestructura Argentina: Noticias de Taiwan's National Tsing Hua University announces research partnership with Harvard University and Facebook on COVID-19 Central News Agency, Taiwan Gefährliche Freundschaften in Zeiten von Covid-19 Frankfurter Allgemeine Évolution des mobilités et diffusion du Covid-19 en France : ce que les données Facebook dévoilent The Conversation

Get Involved

Check out our high resolution population density maps, including demographic estimates, available for download on the United Nations Office for the Coordination of Humanitarian Affairs (OCHA)’s Humanitarian Data Exchange. Each country-level dataset contains the demographic estimates. If you're interested in working with movement or network coverage maps, please email diseaseprevmaps@fb.com.

Explore

Disease Prevention Maps

Features

Privacy-Preserving

Global Reach

Timeliness

Who Uses Disease Prevention Maps

Universities and researchers

Map Types

Movement Maps

Network Coverage Maps

Co-location Maps

Travel Patterns

Case Studies

Economic and social consequences of human mobility restrictions under COVID-19

Mobility in Italy Under Lockdown

The Cost of Uncoordinated Responses to COVID-19

London School of Hygiene & Tropical Medicine creates dashboards visualizing trends in Facebook Colocation Data for COVID19 response

In the News

Get Involved