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LAHVA: Linked Animal Human Health Visual Analytics

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Title: LAHVA: Linked Animal Human Health Visual Analytics

1
LAHVA Linked Animal Human Health Visual
Analytics

Presented by Ross Maciejewski
Purdue University Regional Visual Analytics Center

2
Visual Analytics

Visual analytics is the science of analytical
reasoning facilitated by interactive visual
interfaces.
People use visual analytics tools and techniques
to
Synthesize information and derive insight from
massive, dynamic, ambiguous, and often
conflicting data.
Detect the expected and discover the unexpected.
Provide timely, defensible, and understandable
assessments.
Communicate assessment effectively for action.

3
Project Goals

Linked Animal-Human Health Visual Analytics
Integrated temporal, geospatial, multi-source,
multi-scale analytic capability
Systemic biological pandemic, syndromic,
chem/bio/nuclear surveillance, management, and
response
Benefits/ impact
Earlier detection of some environmental and
emerging health conditions
Cross-species monitoring reduces false positive
rate and could improve spread factor analysis
By monitoring pets, our system covers areas that
may not have electronic human health surveillance

4
Outline

Introduction
Background
Motivation
LAHVA
Case Studies
Results
Conclusions Future Work

5
Introduction
6
Previous Work

EARS - Early Aberration Reporting System
ESSENCE - Electronic Surveillance System for the
Early Notification of Community Based Epidemics
Biosense

7
Motivation

Data sources vary widely in accuracy and
reliability
Many existing systems do not leverage existing
messaging and vocabulary standards (e.g. HL7,
LOINC)
Current systems generate many false positives
Many geographic areas lack emergency room
coverage

8
Motivation (cont.)

73 of emerging infectious diseases are zoonotic
in origin
Cross system monitoring can provide earlier and
more reliable detection
Banfield sees 5 of all pets in the United
States
Indiana Network for Patient Care (INPC) consists
of five major hospital systems in Indianapolis
and serves more than 390,000 patients per year

9
LAHVA

Consists of three components
Data Management
Statistical Analysis
Visual Analytics

Visual Analytics Interface
Banfield(Pets)
Relational Database
Public Health Officials
INPC(Hospitals)
Cleaning and Transformation
Statistical Analysis
10
Data Management

Data Preparation
Detect and remove errors
Provide feedback to data providers
Privacy-Preserving Data Sharing
Currently uses traditional de-identification
techniques
Applying visual abstractions of data

11
Statistical Analysis

Power Transformation
Square root versus natural log
Data Normalization
Determining an appropriate denominator
Aberration Detection for Sparse, Dependent Data
CUSUM (cumulative sum) model
Bootstrapping
Quantile Measures
Seasonal-Trend Decomposition Based on Loess (STL)

12
Power Transformation

Apply logarithm when mean is proportional to
standard deviation
Apply square root when data follows a Poisson
distribution
Goal is to make mean independent of standard
deviation
Banfield data has many counts of zero, so must
apply either or
was found to yield a skid-free
distribution

13
Data Normalization

Daily counts vary according to seasonal effects,
day of the week effects, etc.
Plots of daily counts are noisy, difficult to
find aberrations
For better analysis, we applied a six month
sliding average window

14
Aberration Detection

CUSUM
Used to find deviation from expected value
Applied in CDC to ER data
Bootstrapping
Used as a robust alternative to parametric
inference
Idea is to sample data with replacement in order
to simulate data distribution

15
Aberration Detection

Quantile Measure
Used to detect unusual variation of cases in
retrospective analysis
For pets within a radius of incidence, we
identify symptomatic encounters over a time
window
There is a distance to the epicenter associated
with each symptomatic encounter
An adverse event with a given epicenter should
cause distribution of these distances to be
shifted downward

16
STL

Seasonal-Trend Decomposition Based on Loess
Time series can be viewed as the sum of multiple
trend components
For each data signal, components are extracted
Can then analyze correlation between components

17
Visual Analytics

Direct access query to database
Pre-computed statistical plots
Factor specification and filtering

18
Visual Analytics
19
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20
Case Study Effects of Seasonal Influenza (Study
Description)

Seasonal Influenza is actively studied via
emergency room chief complaints
Can equivalent flu-like symptoms in companion
animals be used as predictors?

21
Case Study Effects of Seasonal Influenza (Study
Results)

From our results, respiratory illnesses in
dogs seem to proceed those in humans by 10 days.

22
Case Study Assessing Effects of a Chemical
Release (Study Description)

In 2006 industrial wastewater was accidentally
released prompting a public health investigation.
Results of the investigation were self-reported
surveys, no emergency room data was available.
Can companion animal health be used to determine
the effects of such a release?

23
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24
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25
Case Study Assessing Effects of a Chemical
Release (Study Results)
26
Conclusions

Our system links visual and statistical analysis
tools to enhance health-care monitoring
We have provided factor specification and
filtering components.
Two cases studies were chosen to assess the
potential usefulness of such a system and
data-sources

27
Future Work

Directly connect the statistical tools we have
developed into the LAHVA framework.
Add functionality for users to directly select
an area for analysis (for example draw a circle
and determine if the current syndromes in that
area are exceeding the expected value).
Add functionality for spatio-temporal cluster
detection.
Add functionality for time ghosting of
individual pet syndromes.

28
Acknowledgements

Department of Homeland Security
Banfield the Pet Hospital
Indiana Network for Patient Care
National Science Foundation
David S. Ebert and Tim Collins from Purdue
University Regional Visualization and Analytics
Center
Benjamin Tyner, Cheng Zheng and William
Cleveland from Purdue University Department of
Statistics
Larry Glickman, Nita Glickman, George Moore from
Purdue University School of Veterinary Medicine
Rimma Nehme and Mourad Ouzzani from the Purdue
Cyber Center
Shaun Grannis from IU School of Medicine