Facebook is launching three types of maps that will help nonprofit organizations and universities working in public health get ahead of disease outbreaks and reach vulnerable communities more effectively.
Population density maps complete with demographic estimates, movement maps and network coverage maps when combined with information from health systems, can improve the way organizations deliver supplies and respond to outbreaks, says Laura McGorman, Facebook’s Policy Lead, Data for Good.
The maps will help the platform’s health partners better understand where people live, how they are moving and whether they have connectivity.
Facebook’s initial partners for this effort, include: Direct Relief, FHI360, Harvard School of Public Health, the Institute for Health Metrics Evaluation at the University of Washington, International Medical Corps, the London School of Hygiene & Tropical Medicine, Malaria Atlas Project, the MRC Centre for Global Infectious Disease Analysis at Imperial College London, Northeastern University, Sabin Vaccine Institute, UNICEF, Wadhwani AI, the World Bank, and the World Economic Forum.
How it works
The movement maps aggregate information from people who are using Facebook on their mobile phones with location services enabled, providing real-time snapshots into mobility patterns.
Partner organizations can combine this data with information on specific cases of diseases to glean insights about where the next case of cholera or drug-resistant malaria is likely to occur.
The improved insights from these forecasting models allow health systems to get ahead of an outbreak by prepositioning treatments where they’re likely to be needed most.
The high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics, including the number of children under five, the number of women of reproductive age, as well as young and elderly populations, at unprecedentedly high resolutions.
Facebook says the maps are three times more detailed than any other source. However, they weren’t built using Facebook data, rather they rely on combining the power of machine vision AI with satellite imagery and census information.