grEPI is a digital public good for health, a free and open-access software product whose aim is to serve as a living, global, standardized repository (library) of epidemiological parameter estimates by enabling user-friendly online interface for exploring, retrieving and providing data throught web application features as well API services as machine-readable interface for retrieving desired data programatically.
While the World Health Organization hosts grEPI on its dedicated cloud infrastructure, the product is being designed, developed and maintained by the Epidemiological Parameters Community of Practice under the Collaboratory initiative.
Envisioned as a living resource that continuously evolves through community feedback loops and contributions, grEPI, in its initial proof-of-concept version, brings together epidemiological parameter estimates extracted from the scientific literature by the subject matter experts and provides a central view with standardized vocabulary such as disease and pathogen context as well as methodological details, along with references to the corresponding data sources. By providing transparent and adjustable view on epidemiological parameter estimates and related data sources, grEPI aims to enable quick identification of relevant epidemiological parameter estimates and support more efficient, reproducible, and consistent epidemiological analyses, strengthening evidence-based decision-making in public health.
To address the main challenge of data heterogeneity and significant amount of time and effort needed to adjust the data to be able to use it further, grEPI is currently adhering to the WHO International Classification of Diseases(ICD-11), legally cleared WHO Geo Data standard for Health Boundaries for Administrative Level 0 (Countries, Territories, Areas) and is also implementing best practices for classifying and categorizing specific properties of an epidemiological parameter estimate or a literature item.
Using standardized actively maintained vocabulary increases the data quality and enables better understanding of the context.
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Using grEPI main features such as browsing and exporting epidemiological parameter estimates and literature in both web application and API does not bear any licensing cost and does not require creating of an application account. As a digital public good for health, access to grEPI is free and open. One only needs to be connected to the Internet for being able to use grEPI in their web browser.
Using grEPI web application interface does not require any specific technical background or skillset. The interface is designed to be as user-friendly as possible and to enable smooth user journeys to the desired outcomes.
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grEPI´s design approach is to enable quick adaptation and response to the changes in the field as well as to serve as a global digital public good for health. grEPI is not disease or pathogen specific, limited to specific epidemiological parameter categories, geographies or any specific context. The underlying relational database is built to support relations between specific entities and properties rather then hardcoding the specific context for a specific use case.
Reference data module serves as a core feature in grEPI web application for enabling quick configuration of new specific contexts or applying changes to the existing ones by using user-friendly interface.
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Fit epidemic models via MLE or Bayesian inference.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris. Generate probabilistic forecasts from a fitted grepi model object with uncertainty quantification.
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Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt. Produce publication-ready ggplot2 visualisations of model fits, forecasts, and parameter posteriors.
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Basic SIR/SEIR model fitting via maximum likelihood estimation.
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