Discovery Analytics: Tracking Ebola Spread

2015 LexisNexis® Risk Solutions HPCC Engineering Summit
Presenter: Jesse CPB Shaw, Consulting Software Engineer
September 29, 2015
Delray Beach, FL
Accelerating the
Pace of Innovation
Discovery Analytics: Tracking Ebola Spread

Discovery Analytics: Tracking Ebola Spread : Overview/Agenda
Monstrous data.
Knowledge engineering?
Moving mountains!
Finding gold.
Discovery Analytics: Tracking Ebola Spread 2

4Everything Is Going To KEL
Digging Out - Exploring Massive Data
Our Data
Product Development

Digging Out – Big Data Is Getting Bigger Every Second
Vesuvius
So cute.

Big data becomes…
MASSIVE…
with each passing cycle.

8Discovery Analytics: Tracking Ebola Spread
C++ ECL

KEL
Data Ingest
Data Delivery
ECL So cute.
Data Aggregation

KEL
2,000 (tedious) Lines
300,000 (I’m-not-working-here-anymore tedious) Lines
C++ECL
120 Lines
KEL

KEL
KEL allows us to control data STRUCTURE
and INTERACTIONS through ENTITES and
ASSOCIATIONS.
Data knowledge becomes PERSPECTIVE
driven.

Entities, Associations, and Perspective
en·ti·ty - a thing with distinct and independent existence.
The ENTITES derived from a medical claim:
• A Person
• Patient
• Policy Holder
• Referring Provider
• Attending Provider
• A Facility
• Billing
• Provider
• The Claim
• Details

as·so·ci·a·tion - a connection or cooperative link between entities.
ASSOCIATIONS bind ENTITIES together.
• Two providers found on the same claim
• Addresses found in the same geographic boundary
• People found on the same property deed
• Buyers
• Sellers
• People found at the same address
These connections create the GRAPH

per·spec·tive - a point of view or way of regarding something.
The GRAPH provides incredible data aggregation flexibility.

Digging Out – Answer Important Questions!
Where are the Jesse Shaws?
Person := ENTITY(FLAT(UID=did, fname, lname, st, city_name, zip),MODEL(*));
USE header.File_Headers(FLAT,Person);
Person: => isJS := IF(fname = 'JESSE' AND lname = 'SHAW', TRUE, FALSE);
QUERY: StatesJS <= Person(isJS=TRUE){st, cnt := GROUP:Count};

Discovery Analytics With KEL:
Breast Cancer Molecular Pathology

Example Shell – Spotlight On Genetic Testing

Example Shell – BRCA Proof of Concept
May 2013, Angelina Jolie publically announced she was having a
preventive double mastectomy.
• She tested positive for BRCA1 and BRCA2 mutations.
• Her mother died from breast cancer.

• Expected October seasonality
• Search volume decreased and normalized
• Overall search volume decreased suggesting
• Public knowledge appreciated
• Public desensitization to the issue
• Peak search volume narrowed suggesting
• Shorter public attention span
• Desensitization to the issue

The KEL data structure was comprised of just 4 ENTITES:
• Person
• Facility
• Claim
• Claim Details
A time series was constructed based on:
• BRCA CPT CODES
• YEAR
• MONTH

QUERY: BrcaMonths <= Claim(ClaimDetails.dtl.Brca=TRUE)
{year :=YEAR(received_date), month := MONTH(received_date), Brca_count := GROUP:Count};
0
500
1000
1500
2000
2500
3000
3500
4000
BRCA BCAM Jolie Myriad

0
1000
2000
3000
4000
5000
6000
7000
8000
Jan-12
Feb-12
Mar-12
Apr-12
May-12
Jun-12
Jul-12
Aug-12
Sep-12
Oct-12
Nov-12
Dec-12
Jan-13
Feb-13
Mar-13
Apr-13
May-13
Jun-13
Jul-13
Aug-13
Sep-13
Oct-13
Nov-13
Dec-13
Jan-14
Feb-14
Mar-14
Apr-14
May-14
Jun-14
Jul-14
Aug-14
Sep-14
Oct-14
Nov-14
Dec-14
Jan-15
Feb-15
Mar-15
Apr-15
BRCA Mastectomy

Discovery Analytics With KEL:
Predicting Ebola Spread

FAU – Rapid Response Model
The Team:
Dr. Borko Furht
Dr. Dingding Wang
Dr. Ankur Agarwal
Dr. Hari Kalva

FAU– Disease Propagation Proof of Concept
Cohesivity and Cluster Connectivity
High social network cohesion has been shown to correlate positively with an increase in the probability of
idea dispersion. This leads to the question whether a highly cohesive physical network would be tuned to
positively effect disease propagation.

Two Initial goals:
Create people clusters based on proximity
Build a simple, weighted score approximating physical contact
Bonus!
Create a mechanism to crawl clusters based on edge weights

Historic address creation
Use address history to find a person’s most recent address.

Cluster creation
Use the public record graph to calculate distances
between a person and their relatives.

Cluster Dimensions
• Total Cluster Size
• First Degree Count
• Second Degree Count
• Average Degree
• Neighbor Count
• Immediate Count
• Moderate Count
• Extended Count
Simple Score Weights
Neighbor Count * 4 +
Immediate Count * 3 +
Moderate Count * 2 +
Extended Count * 1 +

FAU – Disease Propagation Proof of Concept
Controlling Backtracking
KEL allows for the creation of node traversal rules through a pattern definition.
GLOBAL: Relations(#1,#2,#dist1) => D1(#1,#2);
GLOBAL: Relations(#1,#2,#dist1),
Relations(#2,#3,#dist2),
#1<>#3 => D2(#1,#2,#dist1, COUNT(#3));
GLOBAL : D2(#1,#2,#dist1,#cnt1),
D2(#2,#3,#dist2,#cnt2),
#1<>#3,
NOT D2(#1,#3)
=> D2Paths(#1,#3,#dist1+#dist2,#cnt1+#cnt2,#2);

FAU – Disease Propagation Proof of Concept
Controlling Backtracking
KEL allows for the creation of node traversal rules through a pattern definition.
Resulting Set for D2Paths:
Node 1 Node 2 Node 3 Distance Leaves
A B D 5 1
A C D 4 1

Root Sink Path Distance D1 Count Path Length PCT of Graph
4 184, 5, 7, 9, 10, 13, 17, 18 7 10 8 100%
4 184, 8, 12, 16, 18 7 6 5 61%
4 184, 8, 15, 18 15 6 4 56%
Most infectious, Shortest path…
Using distance edge weights, find the path with the
most first degree nodes.

In Summary
Copyright © 2015 LexisNexis. All rights reserved.
Contact information:
Phone: 320-203-6615
Email:jesse.shaw@lexisnexis.com
Twitter:@jessecpbshaw
KEL can be found on the HPCC System’s website at:
https://hpccsystems.com/download/free-modules/kel-lite

Discovery Analytics: Tracking Ebola Spread

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