The Rockefeller Foundation has announced $3 million in new funding for Global.health (Gh) – the first-of-its-kind, open source platform for scientific pandemic data. This will enable it to expand its international partnerships and update global efforts around pandemic prevention, surveillance and response coordination.
Developed jointly with researchers and engineers in the Department of Biology at the University of Oxford and Boston Children’s Hospital in the USA, Global.health provides access to real-time, anonymous health data on outbreaks of infectious diseases, for the first time. The Gh database already contains more than 100 million detailed, verified, curated, and de-identified SARS-CoV-2 case records from over 130 countries: the most comprehensive repository of COVID-19 data in the world.
Global.health’s mission is to organize the world’s infectious disease data to enable rapid response. We have so far focused on the initial phase of disease outbreaks, such as COVID-19 and monkeypox, and will now be able to expand our international partnerships and build our analytical tools to improve outbreak detection and response more broadly. “
Dr. Moritz Kremer, co-founder of Global Health and Associate Professor in the Department of Biology, University of Oxford
What started as a volunteer-driven data science project at the start of the COVID-19 pandemic, Global.health has grown into a flexible and scalable data platform that sets a new standard for open, granular and standardized case data. This information will be a vital resource for epidemiologists and public health leaders to model and mitigate the spread of emerging infectious diseases.
In 2022, for example, Global.health’s curated and validated monkeypox case dataset became one of the most comprehensive and cited resources in the crucial first 100 days of the global outbreak.
Dr Kramer added: “This new funding from the Rockefeller Foundation will allow us to dig deeper into identifying the data and interventions that have the greatest impact in controlling disease outbreaks at different stages of the pandemic. These are critical steps to advance epidemic prevention and response as threats from climate-induced infectious diseases increase.
This scholarship will enable Global.health to pursue priority initiatives, including:
- Evaluate the impact of different data sources to identify the most useful data points during the early stages of an outbreak (first 100 days).
- Develop open source, scalable and robust algorithms and data pipelines to detect, predict and predict the emergence and prediction of new COVID-19 variants of interest (VOCs) globally.
- Combine human mobility data and network science algorithms to optimally configure and distribute public health interventions during emerging epidemics, outside the constraints of state or state borders.
- Create open source methods and frameworks for epidemic response analytics, to make the outputs directly available to groups involved in the broader pandemic preparedness ecosystem. This will also improve the translation of science into practical applications that are scalable and timely enough for real-world outbreak responses.
- Form collaborative working groups of international teams of scientists, prioritizing low- and middle-income countries, to co-develop practical applications and quickly translate them to real-world impact. This will be achieved through targeted training, conferences, workshops, and funded collaborations, to stimulate collaborative research and development.
“We have learned a tremendous amount from COVID-19 about how to better prepare for and respond to infectious disease outbreaks – and at the top of the list is the importance of strong surveillance, reliable data and rapid response,” said Chikwe Ikoizu, associate. Director-General of the World Health Organization. I welcome the Rockefeller Foundation’s support to expand the reach and impact of Global.health, including continued collaboration with the World Health Organization’s Center for Epidemiological and Epidemiological Intelligence, to allow faster, data-driven decisions at the earliest signs of an outbreak — when that happens that matters most.”