The Rockefeller Foundation announces a new $ 3 million grant for

The Rockefeller Foundation has announced a new $ 3 million grant for (Gh), a one-of-a-kind open source platform for scientific data on pandemics. This will enable it to expand its international partnerships and modernize the global effort for coordinated pandemic prevention, surveillance and response.

Developed jointly by researchers and engineers from the Department of Biology at Oxford University and Boston Children’s Hospital, USA, allows for the first time access to anonymous, real-time health data on infectious disease outbreaks. The Gh database already contains over 100 million detailed, verified, harmonized and anonymized SARS-CoV-2 case records from over 130 countries – the world’s most comprehensive repository of COVID-19 data.’s mission is to organize global infectious disease data to enable faster responses. So far we have focused on the early stage of disease outbreaks, such as COVID-19 and monkeypox, and we will now be able to broaden our international partnerships and build our analytical tools to improve detection and response to larger outbreaks. ” .

Dr. Moritz Kraemer, co-founder of and Associate Professor in the Department of Biology, University of Oxford

What began as a volunteer-led data science project at the start of the COVID-19 pandemic, has grown into a scalable and flexible 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, the monkeypox case dataset curated and validated by became one of the most comprehensive and cited resources in the first 100 crucial days of the global outbreak.

Dr Kramer added, “This new Rockefeller Foundation funding will allow us to delve into which data and interventions have the greatest impact for controlling disease outbreaks at different stages of a pandemic. These are critical steps in advancing prevention and prevention. response to the pandemic as the threats of climate-related infectious diseases are on the rise. ‘

This grant will enable to pursue priority initiatives, including:

  • Assess the impact of different data sources to define which data points are most useful during the early stages of an outbreak (first 100 days).
  • Development of scalable and robust open source algorithms and data pipelines to detect and predict the emergence and geographic spread of new COVID-19 risk variants (VOCs), globally.
  • Combining human mobility data with network science algorithms to optimally configure and distribute public health interventions during emerging epidemics, beyond the constraints of national or state borders.
  • Creating open source methods and frameworks for pandemic response analysis to make results directly available to groups engaged 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.
  • Cultivate collaborative working groups involving international teams of scientists, prioritizing low- and middle-income countries, to jointly develop practical applications and quickly translate them into 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 great deal from COVID-19 about how to better prepare for and respond to infectious disease outbreaks – and topping the list is the importance of robust surveillance, reliable data and a rapid response,” said Chikwe Ihekweazu, assistant director. General of the World Health Organization. “I welcome the support of the Rockefeller Foundation to expand the reach and impact of, including the ongoing collaboration with the WHO Pandemic and Epidemic Intelligence Hub, to enable faster decision-making based on data at the first signs of an outbreak, when it matters most. ‘

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