Co-developed by researchers and engineers at the Department of Biology, University of Oxford and Boston Children’s Hospital, USA, Global.health enables access to real-time, anonymized health data on infectious disease outbreaks, for the first time. The G.h database already holds over 100 million detailed, verified, harmonized, and de-identified SARS-CoV-2 case records from more than 130 countries: the most comprehensive repository of COVID-19 data in the world.
Dr Moritz Kraemer, Co-Founder of Global.health and Associate Professor at the Department of Biology, University of Oxford, commented: ‘Global.health's mission is to organize the world’s infectious disease data to enable more rapid responses to them. So far we have been focused on the initial phase 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 wider outbreak detection and response.’
This new funding from The Rockefeller Foundation will allow us to dive deeper into which data and interventions have the most impact for controlling disease outbreaks at different stages of a pandemic. These are critical steps to advancing pandemic prevention and response as the threats from climate-driven infectious diseases are increasing.
Dr Moritz Kraemer, Co-Founder of Global.health and Associate Professor at the University of Oxford
What began as a volunteer-driven data science project at the outset of the COVID-19 pandemic, Global.health 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, 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.
This grant will enable Global.health to pursue priority initiatives, including:
- Evaluating the impact of different data sources to define which data points are most useful during the early stages of an outbreak (first 100 days).
- Developing scalable and robust open-source algorithms and data pipelines to detect and predict the emergence and geographic spread of new COVID-19 Variants of Concern (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 country or state borders.
- Creating open-source methods and frameworks for pandemic response analyses, to make outputs directly available to groups engaged in the broader ecosystem of pandemic preparedness. This will also improve the translation of science into practical applications that are scalable and timely enough for real-world outbreak responses.
- Cultivating collaborative working groups involving international teams of scientists, prioritizing lower- and middle-income countries, to co-develop practical applications and translate them rapidly into real-world impact. This will be achieved through targeted training, conferences, workshops, and funded collaborations, to stimulate collaborative research and development.
For more information see the Global.health (G.h) website.