1. The Covid Graph – a knowledge graph on COVID-19 (Alexander Jarasch and Dr. Martin Preusse)

The COVIDGRAPH project is a voluntary initiative of graph enthusiasts and companies with the goal to build a knowledge graph with relevant information about the COVID-19 and the SARS-CoV-2 virus.

2. Rapid Response Covid-19 Technical Delivery at Cochrane (Julian Everett)

Cochrane’s mission is to promote evidence-informed health decision-making by producing high quality systematic reviews and other synthesized research evidence. Their work is internationally recognized as the benchmark for high-quality information about the effectiveness of health care, and informs over 90% of the guidelines produced by the WHO. They have recently undertaken a number of initiatives which are collectively transforming their core data and content production pipelines via a mix of machine learning, crowd and knowledge graph technologies. This is reducing the lead time and cycle time for new evidence to be published, greatly enhancing its discoverability, and enabling a near-realtime capability to produce baseline datasets that summarise the current state of knowledge about specific clinical research questions. This is in turn enhancing access to the latest research findings for point-of-care clinical decision making. In this talk, you will learn how Wardley Mapping and Domain Driven Design techniques can be used to guide strategic decisions about where and how machine learning, crowd-sourcing and knowledge graph techniques can best be deployed to solve real-world business problems. It will also explore how to ensure competitive advantage can be maximised within budget limitations via a relentless focus on core domain market differentiators, and techniques for successfully managing the change impacts of machine learning on business workflows.

3. FasterThanCorona – Unboxing 2 Months of Citizen Data (Dr. Christian Kauth)

FasterThanCorona is an incentive to decipher the Coronavirus with citizen data and machine learning, to complement medical research and political decision-making. Are there combinations of pre-existing medical conditions, lifestyle and symptoms that entail a particularly bad course of the COVID-19 disease? By analyzing the progression of the disease within many individuals, we search for computer generated biomarkers (via supervised learning). The number of unreported cases is believed large, due to symptomless, yet infectious disease progression. Can contamination be reduced by clustering data donors to selectively steer testing of potential virus carriers that present mild to no symptoms (via unsupervised learning)?

4. How Animated Time-Series Data bring Metabolic Network Models to Life e.g. of COVID-19 (Dr. Andreas Dräger)

Experimentally obtained time series data can be highly complex and challenging to analyze visually. It is now possible to investigate data and maps differently because detailed network maps of molecular processes are already available for a variety of cell types. To this end, we present an intuitive new approach to overlay such network maps with metabolomic data. In this way, it is possible to investigate the data directly in the context of intracellular processes in which they participate. The new method turns time-series data and network maps into animation videos while exploiting several aspects of human perception. This new method enables us to detect particularly active areas quickly. As examples, we demonstrate the process using yeast cells as well as human platelets and red blood cells. Using appropriate measurement data, the COVID-19 map, on which is currently under development, can also be investigated.

5. ViraTrace – Contact Tracing Beyond First Contacts (Ivan Bestvina)

ViraTrace develops (and helps other contact tracing apps incorporate) a multiple-step risk propagation model which goes beyond the usual contact tracing paradigm. We believe that just notifying everyone in contact with the infected is not enough, and doesn’t deliver the full potential of contact tracing apps. Our approach provides fine-grained risk infection assessment without the loss of privacy, through a fully decentralized and anonymized model. In this case study, we will cover both the general ideas behind our model, but also, more importantly, provide an insight into how it is to work with different contact tracing teams from around the globe, what are the challenges w.r.t healthcare, privacy, politics and culture, and finally how a 2 months old team managed to get their solution integrated into the largest contact tracing app in the world with counting over 100M users.