Over the past two years, dozens of organizations used Facebook's Data for Good products to improve wellbeing and save lives. Their most profound applications include disaster preparedness projects, vaccination campaigns, and effective deployment of services during times of natural disaster.
Movement Range Maps
COVID-19 Mobility Data Analysis in India
Researchers from the Indian Institute of Technology – Tirupati (IIT-T) partnered with Facebook to create data-driven tools to help policymakers and public health experts to make informed decisions and interventions. IIT-T used Facebook movement maps and population density maps to analyze population movement and disease containment. IIT-T shared daily, visual reports with the state governments of Andhra Pradesh and Odisha on high-mobility regions, geographies with high outflows of people, and areas with high population density to help governments make decisions about interventions in red zones to help slow the spread of disease. IIT-T then developed a real-time COVID-19 response dashboard to visualize and understand the effectiveness of the lockdown imposed due to COVID-19.
Topline Report on COVID-19 Vaccination in the United States
This report presents data collected from January 10 to February 27, 2021 from more than 1.9 million Americans. We highlight below national- and state-level trends on self-reported vaccinations (hereafter “uptake”) and vaccine-related attitudes by key population groups to inform potential ways to combat vaccine hesitancy in the United States.
Social Connectedness Index
Predicting County-Level COVID-19 Cases using Spatiotemporal Machine Learning: Modeling Human Interactions using Social Media and Cell-Phone Data
In this study, we first compare the power of Facebook’s social connectedness with cell phone-derived human mobility for predicting county level new cases of COVID-19. Our experiments show that social connectedness is a better proxy for measuring human interactions leading to new infections. Comparing our predictions with a baseline currently used by the CDC indicates an average 58% improvement in prediction over two-to-four week prediction horizons, pointing to the strong predictive power of our model.
Social Connectedness Index
Salience and Households’ Flood Insurance Decisions
Flooding is one of the most costly natural disasters faced by US households, yet policymakers are puzzled by the low take-up rates for flood insurance. In this paper, I argue that households’ insurance purchases are affected by the low salience of flood risk. Leveraging novel transaction-level data, I use two empirical strategies to support my hypothesis. My results suggest that behavioral frictions have a major impact on households’ insurance decisions.