Facebook Data for Good has a number of tools and initiatives that can help organizations respond to the COVID-19 pandemic. Here’s how you can make use of these tools:
- Download our publicly available datasets
- Work with our non-public data if you are a researcher or a nonprofit:
- Look at our data visualizations and dashboards
- Connect with the COVID-19 Mobility Data Network by emailing firstname.lastname@example.org. CMDN is a network of infectious disease epidemiologists working with tech companies to use aggregated mobility data to support the COVID-19 response.
- Apply for research funding through the Trinity Challenge. Applications are being accepted for ideas to safeguard our health and economic systems from the threat of future global health emergencies with a focus on identification, response, and recovery. There is £10m in funding available to support the best solutions.
More About Each Tool
High Resolution Population Density Maps
- Download this dataset for 169 countries right now on UN OCHA’s Humanitarian Data Exchange here or on Amazon AWS Open Data Sets.
- These are the most accurate population datasets in the world.
- You can watch a video explaining how these maps were made here.
- You can watch a tutorial video here for how to work with Population Density Maps in QGIS, a geospatial software.
- Check out this tutorial which will walk you through the creation of a map and dataset which identifies potential problem areas for COVID-19 vulnerable populations using High Resolution Population Density Maps and Movement Range Maps.
- In addition to releasing a map of the total population density for each country, we also release demographic data showing populations of (1) men (2) women (3) children (ages 0-5), (4) youth (ages 15-24) (5) women of reproductive age (ages 15-49) and elderly (ages 60+).
- These maps are built using census data and satellite imagery, and do not use any Facebook data. The methodology is here.
- The World Bank used these High Resolution Population Density Maps to plan for better COVID resource allocation in Spain. Read more here.
- For a list of all Case Studies using High Resolution Population Density Maps, go here.
- Download the public API
- Submit a request to work with the non-aggregated data if you are a researcher. Check out the Request for Data Access page before submitting your request.
- Look at the Symptom Map visualization
- This symptom map is designed to help policymakers and health researchers better monitor and forecast the spread of COVID-19 and improve their response to the pandemic.
- In partnership with Carnegie Mellon University and University of Maryland, Facebook users are invited to take a survey conducted by the universities to self-report how they’re feeling and any symptoms they may be experiencing.
- All publicly available information is aggregated and privacy preserving measures are applied.
- Facebook does not receive, collect or store individual survey responses.
- CMU and UMD do not learn who took a survey.
- To ensure that the survey sample more accurately reflects the characteristics of the population represented in the data, Facebook shares a single statistic known as a weight value that doesn’t identify a person but helps researchers correct for any sample bias. Facebook doesn’t share who took the survey with our academic partners, and they don’t share individual survey responses with us.
- The Institute for Health Metrics and Evaluation used these surveys to evaluate mask use in the United States. Read more here.
Movement Range Maps
- Download the publicly available files, updated daily
- Look at the Movement Range Maps visualization
- Select the data layers ‘Percentage of People Staying in Place’ or ‘Change in Movement’
- Movement Range Maps inform researchers and public health experts about how populations are responding to physical distancing measures.
- These datasets have two different metrics: Change in Movement and Stay Put.
- The Change in Movement metric looks at how much people are moving around and compares it to a baseline period that predates most social distancing measures.
- The Stay Put metric looks at the fraction of the population that appears to stay within a small area surrounding their home for an entire day.
- Check out this tutorial which will walk you through the creation of a map and dataset which identifies potential problem areas for COVID-19 vulnerable populations using Movement Range Maps and High Resolution Population Density Maps.
- A differential privacy framework is applied to this dataset. Read more here.
- Governor Gavin Newsom praised Movement Range data in a press conference regarding the State of California’s COVID19 response efforts.
Preventive Health Survey
- Download aggregate data from Massachusetts Institute for Technology’s (MIT) Public API
- Check out the public dashboard from Johns Hopkins University (JHU) Center for Communication Programs
- Submit a request to work with the non-aggregated data if you are a researcher
- The COVID-19 Preventive Health Survey is designed to help policymakers and health communities better understand the effectiveness of and adherence to COVID-19 policies, such as stay-at-home orders and wearing masks, as well as inform future policies and communications in response to the pandemic.
- In partnership with MIT and JHU, Facebook users are invited to take a survey, conducted by MIT, to self-report their adherence to preventive measures, such as washing hands and wearing masks, and what they know about COVID-19, including symptoms of the disease, risk factors and how their community is handling the pandemic.
- All publicly available information is aggregated.
- Facebook does not receive, collect or store individual survey responses.
- MIT does not learn who took a survey.
Social Connectedness Index
- Download the Social Connectedness Index here on OCHA’s Humanitarian Data Exchange
- The Social Connectedness Index measures the strength of connectedness between two geographic areas as measured by Facebook friendship ties.
- These connections can reveal important insights about economic opportunities, social mobility, trade and more.
- In the context of COVID-19, this serves as another helpful input for disease modeling, and helps researchers understand where areas hit hardest by COVID-19 might seek support.
- Details on privacy and the methodology can be found on our website here, or in the paper Social Connectedness: Measurement, Determinants, and Effects published in the Journal of Economic Perspectives.
- Researchers at NYU used the Social Connectedness Index to show that areas with stronger social ties to two early COVID-19 “hotspots” (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. Read more here.
Survey on Gender Equality at Home
- Facebook’s Survey on Gender Equality at Home generates a global snapshot of women and men’s access to resources, their time spent on unpaid care work, and their attitudes about equality.
- Read about the methodology here.
- Read the Survey on Gender Equality at Home full report here.
- Download the data here. The forecasts are currently available for all counties in the United States.
Facebook AI builds adaptive models and collaborates with experts to help the world better understand the spread of the virus. The COVID-19 Forecasts produced by the models gives researchers and public health experts information that can help them with resource planning and allocation and early outbreak detection. These forecasts are developed using public, non-Facebook data, and serve as a tool to support our global efforts to keep people informed as the pandemic evolves. Our data-driven methods achieve strong performance when compared to other state of the art forecasts.
- Read about the methodology here.
- Coming soon!
Disease Prevention Maps
- Email email@example.com if you are a nonprofit or researcher interested in working with this data
Facebook Disease Prevention Maps are designed to help public health organizations close gaps in understanding how people are moving or the state of their cellular connectivity in order to improve the effectiveness of health campaigns and epidemic response. These maps were launched in May 2019 and have been used for other health crises including the cholera outbreak in Mozambique.
There are four map types under Disease Prevention Maps:
- Travel Patterns (launched for COVID19): Show comparisons of the number of Facebook users moving large distances, like air or train travel. We are initially looking at international travel.
- Co-location Maps (launched for COVID19): Reveal the probability that people in one area will come in contact with people in another, helping illuminate where COVID-19 cases may appear next. These maps look at two locations and measure the rate at which Randomly Chosen Person A and Randomly Chosen Person B are colocated in the same place over a certain period. As a concrete example: if you choose a random person from Los Angeles County and a random person from San Francisco County, what is the probability that they spent at least 15 minutes in the same level 14 Bing tile anywhere in the world during the week 2019-10-25 to 2019-10-31?
- Movement Maps: Aggregated information showing movement between two points from people using Facebook on their mobile phones
- Network Coverage Maps: Network Coverage Maps: Aggregated information show where people on Facebook have cellular connectivity
- All information is aggregated and privacy preserving measures are applied. The highest resolution are tiles that are six American football fields on a side. This allows nonprofits and researchers to analyze patterns at scale while preventing re-identification.
- We also only generate mobility datasets during natural disasters and public health emergencies for specific windows of time and share them with nonprofits and universities that have signed data license agreements. These partners are then onboarded to our tools for which access is administered at a user level.
- Movement Maps aggregate information only from people using Facebook on their mobile phones with Location History enabled. The methodology can be found here.
- Network Coverage Maps are visualized as a polygon of network coverage by speed. The methodology can be found here.
- Researchers summarize the initial impact of the lockdown in India for a representative sample of mostly poor and non-migrants workers in Delhi. Using Facebook mobility data, they show that intra-city movement dropped 80 percent following the announcement.
- More Disease Prevention Maps case studies can be found here.
CrowdTangle COVID-19 Live Displays
- These public displays can be accessed by anyone at any time here: https://apps.crowdtangle.com/public-hub/covid19.
- CrowdTangle COVID-19 Live Displays are very useful for monitoring trending public posts on Facebook and Instagram related to COVID-19.
- CrowdTangle is focused entirely on public data.
- The easiest way to think about CrowdTangle is this: if you can see it natively on the platform without any special permissions to the page or account, we can show it to you in our tool.
- CrowdTangle gathers information that is publicly available on Facebook, Instagram, and Reddit. That information is aggregated, benchmarked, and served to you in an easy to read visual format.
- Read more about how these displays were built here.
- Coming soon!
COVID-19 Mobility Data Network
- Join the COVID19 Mobility Data Network by emailing firstname.lastname@example.org, if you are a qualifying nonprofit or university researcher
- Public sector officials interested in being connected with COVID19 Mobility Data Network researchers can email email@example.com
- The COVID19 Mobility Data Network is a network of infectious disease epidemiologists at universities around the world working with technology companies (like Facebook) to use aggregated mobility data to support the COVID19 response.
The participants in the COVID19 Mobility Data Network share a deep commitment to privacy values and data protection as well as best practice principles related to data governance and ethics. The following principles guide the Network’s effort:
- The use of data, including data sharing, aggregation, and analysis, for Covid19 response must speak to a clear need articulated by public health authorities, and for no other purpose.
- The use of data must both be in compliance with existing laws and adhere to best practice principles of data governance.
- Data should be aggregated to the lowest feasible resolution possible while maintaining its desired utility.
- The use of data must be transparent, inclusive, and safeguarded against unintended consequences.
The Network itself will not be the recipient of data. The use of data facilitated by the Network will be subject to the various approval and oversight mechanisms provided by the academic home institutions of the individual researchers participating in the Network. The Network facilitates the sharing of consolidated daily situation reports with government health officials and provides analytic support. It does not share the underlying data sets with governments, third parties, or the public at large. Participants in the Network embrace appropriate legal, organizational, and computational safeguards to minimize – and carefully manage any remaining – data privacy risks associated with this research effort in general and the use of aggregated data in particular.
- Direct Relief is a nonprofit and a leading partner within the COVID19 Mobility Data Network. By analyzing Facebook Movement Range data, Direct Relief was able to share insights with the State of California. Governor Gavin Newsom praised these insights in a press conference regarding the State of California’s COVID19 response efforts.