CovidGraph COVID-19 Knowledge Graph Discover

Who we are

Bringing the world’s health knowledge to research and medical decision makers.

HealthECCO is a non-profit organization whose purpose is to support science and research in the fields of medicine and biology in the search for cures for diseases such as Covid-19, diabetes and more.

We rely on global networking of expertise combined with some of the most advanced IT technologies available today, especially Graph technology.

Our projects are open source and freely accessible to the advancement of global health.

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Our Impact

Our solution will help a diverse set of policy makers to create robust, harmonised health care policies (such as treatment guidelines) faster while accommodating local variations. This will in turn have a huge impact on large cohorts of patients. We can target health authorities responsible for populations that do not have access to high-quality treatment guidelines yet.

For health workers, the key benefit is a much improved basis for decision making. Access to established information sources in combination with case reports and grey literature delivers contextualized insights to improve treatment decisions.


For researchers, we provide a much faster way to access information outside of their speciality. Traditional scientific exchange of information via published reports and clinical trials is too slow to respond to health emergencies.


For policy makers, we help create and disseminate accurate health policies that are timely and appropriate for different geopolitical settings.

Our Community

The team behind HealthECCO has a cross-sector background with domain experts, IT specialists and leaders in the graph community. We understand the needs of our users and possess the capabilities to deliver robust and scalable solutions. The organisations behind HealthECCO as well as our collaboration partners are committed to supporting us throughout the project.

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Our Technology at a Glance

Combining high-tech tools cutting edge technologies like Neo4j, Docker, NLP and emerging front end tools allows us to build a flexible single source of truth that is easy to develop, explore and adapt to fast changing needs.

We apply graph based deep learning methods to predict new relationships in the data. We are able to add context to the results. This is a huge step to overcome one of the major shortcomings of deep learning methods: Lack of interpretability and trust in the results.

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How you can get involved

We have great ideas and ambitions for HealthECCO, CovidGraph and the wider ecosystem of applications. We know that they can make a positive impact on the world. It's not just developers and graph experts that we are looking for.

  • George Anadiotis
    George AnadiotisZDNet

    Open access, discoverability, reproducibility, code, datasets, and knowledge graphs. This is all good news for research, and machine learning research too, obviously. It seems like steps towards a healthier, more productive research ecosystem are being taken. Read more

  • Adrian Bridgwater
    Adrian BridgwaterForbes

    The thing about data, really, is not just quantity or quality… it’s the relationships. [...] Graph database technologies are by no means a panacea for the current crisis or indeed any wildly complex business issue, but you can be assured that the scientific community are using these techniques in the ongoing quest for inoculations, vaccines and a wider cure. Read more

  • Brian McKenna

    Alexander Jarasch, head of data management and knowledge management at the DZD (Deutsches Zentrum für Diabetesforschung), the German Centre for Diabetes Research, hopes the effort that scientists, both data and medical, are currently putting into the battle against the Covid-19 coronavirus will continue into the future. Read more

  • Dr. Ilka Ottleben
    Dr. Ilka OttlebenLaborpraxis

    In der Verknüpfung der Daten steckt großes Potenzial. So lassen sich beispielsweise mit nur wenigen Abfrageschritten (Queries) alle wissenschaftlichen Publikationen/Patente ermitteln, die sowohl einen mit Covid-19 verwandten Virustypen (z.B. H1N1 oder Mers-COV) als auch ein bestimmtes Gen/Protein erwähnen. Read more