This document discusses how graph databases can be used for contact tracing during a pandemic. It describes how a contact tracing graph can be constructed to model interactions between individuals and determine who may have been exposed. Centralized and decentralized contact tracing approaches are compared. The document also demonstrates how graph queries and algorithms like PageRank and betweenness centrality can be applied to the contact tracing graph to identify high-risk individuals and communities for virus spread. A demo of a synthetic contact tracing graph is presented.

1. Graphs in Life Sciences
Rik Van Bruggen
rik@neo4j.com
@rvanbruggen

2. Introduction to Neo4j
What We Do

3. 7/10
20/25
7/10
Adoption
Top
Retail Firms
Top
Financial Firms
Top
Software Vendors
Customers Partners
Neo4j - The Graph Company
The Industry’s Largest Dedicated Investment in Graphs
Creator of the Market Leading
Neo4j Graph Database Platform
~420 employees
HQ in Silicon Valley, other
offices include London, Munich,
Paris & Malmö
~550 Global Enterprise
Customers
have launched a Neo4j Trial
76% of the
Industry Leaders use Neo4j

4. The Rise of Connections in Data
Networks of People
Know
s
Knows
Knows
Knows
Business Processes
Bought
Bought
Viewed
Returned
Bought
Knowledge Networks
Plays
Lives_in
In_sport
Likes
Fan_of
Plays_for
Data connections are increasing as rapidly as data volumes

5. Harnessing Connections Drives Business Value
Enhanced Decision
Making
Hyper
Personalization
Massive Data
Integration
Data Driven Discovery
& Innovation
Product Recommendations
Personalized Health Care
Media and Advertising
Fraud Prevention
Network Analysis
Law Enforcement
Drug Discovery
Intelligence and Crime Detection
Product & Process Innovation
360 view of customer
Compliance
Optimize Operations
Connected Data at the Center
AI & Machine
Learning
Price optimization
Product Recommendations
Resource allocation
Digital Transformation Megatrends

6. Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time

7. Improving Analytics, ML & AI for Enterprises
Caterpillar’s AI Supply
Chain & Maintenance
German Center for
Diabetes Research (DZD)
Financial Fraud
Detection & Recovery Top 10
Bank

8. Neo4j Technology
8

9. Neo4j is an enterprise-grade native graph database and tools:
• Store, reveal and query data relationships
• Traverse and analyze any levels of depth in real-time
• Add context and connect data to support emerging AI applications
Native Graph Technology
•
•
•
•
•
•
•
•

10. Labeled Property Graph - Simply Powerful

11. Graph Databases: Designed for Connected Data
TRADITIONAL
DATABASES
BIG DATA
TECHNOLOGY
Store and retrieve data Aggregate and filter data Connections in data
Real time storage & retrieval Real-Time Connected Insights
Long running queries
aggregation & filtering
“Our Neo4j solution is literally thousands of
times faster than the prior MySQL solution, with
queries that require 10-100 times less code”
Volker Pacher, Senior Developer

12. Graph databases: Designed for connected data
Connections in data
Real-Time Connected Insights
“Our Neo4j solution is literally thousands of
times faster than the prior MySQL solution, with
queries that require 10-100 times less code”
Volker Pacher, Senior Developer
Longer termbenefits:
● Ease of modelling
○ Natural fit for complex domains
○ Business savvy
○ Easy to understand and reason
● Model flexibility in an Agile world
○ Ease of maintenance

13. Immediate benefits
● Speed, speed, and more speed
○ enabling workloads that were thought
to be impossible before
○ real-time what was batch before
● hardware savings:
○ a few servers replacing
dozens/hundreds
● operational savings:
○ easier to run and operate
Connections in data
Real-Time Connected Insights
“Our Neo4j solution is literally thousands of
times faster than the prior MySQL solution, with
queries that require 10-100 times less code”
Volker Pacher, Senior Developer
Graph databases: Designed for connected data

14. Real-World
Success Stories

15. Highly Valuable Connected Data Use Cases
Drive Enterprise Adoption
Network &
IT Operations
Fraud
Detection
Identity & Access
Management
Knowledge
Graph
Master Data
Management
Real-Time
Recommendations

16. Graphs
in
Life Sciences

17. I will skip the part about ...
See https://www.s-cubed-global.com/news/covidgraph-nerds-response-to-the-pandemic

18. Graphs for
Contact Tracing

19. What do we mean?

20. Who goes into Isolation/Quarantine?

21. Different options

22. How does Contact tracing work?

23. Centralised or decentralised?

24. Centralised or Decentralised
Centralised:
• more privacy sensitive
• easier to see anomalies and
detect potentially
dangerous situations
• more valuable to see
population-wide patterns,
as well as individual health
risk benefits
• more predictive value
Decentralised:
- less privacy sensitive
- limited views possible wrt
population-wide patterns
- immediate added value to
the individual and their
health risks

25. Graph theory & Contact tracing
•
Networks and epidemic
models

26. The Model

27. A synthetic dataset

28. The Queries

29. Free for experimentation

30. Sick person visits same place as healthy person

31. Overlapping time in place

32. Most overlap = highest risk

33. Using inference: MEETS

34. match path = (p1:Person {healthstatus: "Sick"})-[m:MEETS]->(p2:Person {healthstatus: "Healthy"})
where p1.confirmedtime < m.meetingstarttime
return path
limit 100

35. The Analytics

36. Graph Data Science

37. Graph data science algorithms

38. Pagerank:
which nodes are likely to be
more important

39. Betweenness:
which nodes are likely to
contribute to spreading
across communities

40. Community Detection:
which parts of the graph
"belong" together

42. Recorded demo - part 1

43. Recorded demo - part 2

44. Recorded demo - part 3

45. Recorded demo - part 4

46. (synthetic)
Contact tracing
Demo

47. Let’s Do Something
Amazing Together