Helping Organizations Respond to Health Emergencies

By Laura McGorman and Alex Pompe, Data for Good

When planning public health campaigns or responding to disease outbreaks, health organizations need information on where intended beneficiaries live, as well as real-time insights from the field. However, in much of the world, information from the most recent census is often out of date and timely insights from remote communities are scarce.

Today, we are 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. The maps we’re introducing — population density maps complete with demographic estimates, movement maps and network coverage maps — will help our health partners better understand where people live, how they are moving and whether they have connectivity. All of these maps, when combined with information from health systems, can improve the way organizations deliver supplies and respond to outbreaks.

“Epidemics pose a growing threat to lives and livelihoods,” said Vanessa Candeias, Head of Shaping the Future of Health and Healthcare at the World Economic Forum. “Mitigating their risk and impact requires every tool in the toolbox.”

Our 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.

High-Resolution Population Density Maps Including Demographic Estimates

Two years ago, we began working with Columbia University to use satellite imagery and census data to build the world’s most detailed population maps to improve connectivity projects. After working with groups such as the American Red Cross and its Missing Maps project, we realized these maps could also have profound applications in public health.

Today, we’re releasing high-resolution maps that 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. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information. By combining these publicly and commercially available datasets with Facebook’s AI capabilities, we have created population maps that are 3X more detailed than any other source.

Health organizations are already using our AI-powered maps to help allocate resources to the areas where intended beneficiaries live, like in Malawi, where high-resolution maps helped the American Red Cross and its Missing Maps project identify areas with and without concentrations of people. This helped the Red Cross and other on-the-ground teams make more efficient decisions about where to deploy 3,000 health workers to deliver vaccination messaging during a measles campaign.

Tanzania map_women reproductive age
The above map shows the distribution of women of reproductive age (ages 18-49) spread across Tanzania, drawn from a combination of census data and satellite imagery.

Movement Maps

Public health officials often have challenges predicting where disease outbreaks, like malaria or cholera, will strike. However, cutting-edge research has found that pairing health system information with data on human mobility can yield valuable insights about diseases spread by human-to-human contact.

“Population movements are crucial for the spread of many infections — from influenza to measles, said Dr. Adam Kucharski, Assistant Professor in Mathematical Modeling at the London School of Hygiene & Tropical Medicine. “But historically it’s been very difficult for disease researchers to get information on these movement patterns.”

Greater London map
The above map shows movement between Greater London and surrounding areas by day. These data are useful to public health organizations looking at the spread of infectious diseases, such as the flu.

Our 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.

“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,” said Caroline Buckee, Associate Professor of Epidemiology at Harvard TH Chan School of Public Health.

Network Coverage Maps

Over the past decade, the increased availability of mobile health technologies has dramatically expanded access to health information. However, many countries still have areas with little or no access to the internet. Meanwhile, public health officials have limited resources to conduct house-to-house visits, as well as limited information on whether target populations can be reached online.

Because the majority of people use Facebook on mobile phones that rely on cellular networks, we can create real-time maps that show health organizations whether people can be reached with an online message in advance of activities like vaccination days or bed net distributions. This allows public health teams to prioritize where other forms of communication are required, reaching the maximum number of people at the lowest possible cost.

Democratic Republic of the Congo 3G network coverage map
The above map shows the density of 3g network coverage availability across areas in the Democratic Republic of the Congo affected by the most recent Ebola outbreak.

We hope these disease prevention maps are a valuable resource for public health organizations and that this work helps improve the delivery of health services and the response to epidemics.

This effort is also another welcome step forward in unlocking the full potential for public- and private-sector data to be combined to predict and forecast epidemics,” said Candeias of the World Economic Forum, said. In line with Sustainable Development Goal three on improving health for all, we look forward to our partners using these maps to improve health outcomes for communities around the world.

Our high-resolution population density maps can be found on our page on Humanitarian Data Exchange. New partners interested in using movement or network coverage data maps can reach out to To learn more, visit our Data for Good website.



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