Bio Surveillance 2.0 Kass-Hout and Di Tada

8/7/2019 Bio Surveillance 2.0 Kass-Hout and Di Tada

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Biosurveillance 2.0Collaboration for Early Disease

Warning and EffectiveResponse

Taha Kass-Hout

Nicols di Tada

Invited by Dr. Barbara Massoudi, PhD, MPHLecture at Emory University Rollins School of Public Health

Public Health Informatics, INFO 503

Atlanta, GA, USA


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Background


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DAY

CASES

Opportunityfor control

Background

Late Detection Response


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DAY

CASES

Opportunityfor control

Background

Early Detection andResponse


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PUBLIC HEALTH MEASURES

Representativeness

Completeness

Predictive Value

Timeliness

Background


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1000 Malariainfections (100%)

50 Malarianotifications (5%)

Get as close to thebottom of the pyramid

as possible

Urge frequent reporting:Weekly daily immediately

Specificity /Reliability

Sensitivity /Timeliness Main attributes

o Representativenesso Completenesso

Predictive value positiveBackground


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Analyze and interpret

Signal as

earlyas possible

Automated analysis/thresholds

Time

Main attributeso Timeliness


Health care hotline

Background


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PUBLIC HEALTH TWOPERSPECTIVES

Case management Individual cases of notifiable

diseases

Relationship networks (contacttracing)

Population surveillance Larger risk patterns

Background


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CASE MANAGEMENT

Questions/problems:

Is a case due to recent transmission?

If so, does the case share any feature

with other, recent cases?

Ways it's being done:

Investigations/interviews Meeting with other investigators

Background


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POPULATION SURVEILLANCE

Questions/problems:

Are more cases happening than expected?

Does an excess suggest ongoing

transmission in a specific region?

Way it's being done:

Semi-automated routine temporal and

space-time statistical analysis

Background


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WHY LOCATION MATTERS CASE MANAGEMENT

If you are studying a case of acertain disease that was justdeclared

It is harder to picture thesituation by looking at somethingas this..

Background


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Background


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Than by looking at this..

Background


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Background


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WHY LOCATION MATTERS POP SURVEILLANCE

If you are studying the spatialdistribution of a set of diseaseclusters

This would seem more difficult..

Background


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Background


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Than this..

Background


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The Problem Space

Current systems design,analysis and evaluationhas been gearedtowards specific data

sources and detectionalgorithms nothumans

We have systems in

place for those threatswe have been facedwith before

The Problem


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Traditional DISEASESURVEILLANCE

In the past two decades focus was on

automatically detecting anomalouspatterns in data (often a single stream)

Modern methods

rely on human input and judgment

incorporate temporal, spatial, andmultivariate information

The Problem


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9/20, 15213, cough/cold,

9/21, 15207, antifever, 9/22, 15213, CC = cough, ...

1,000,000 more records

Huge mass of data Detection algorithm What are we

supposed to do with

this?

Too many

alerts

Traditional DISEASESURVEILLANCE

The Problem


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Our Approach

Human-based

Collaborative and cross-disciplinary

Web 2.0/3.0 platform

Our Approach


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Information Sources

Event-based - ad-hocunstructured reportsissued by formal orinformal sources

Indicator-based -(number of cases,rates, proportion of

strains)

Timeliness, Representativeness, Completeness,

Predictive Value, Quality, Our Approach


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9/20, 15213, cough/cold,

9/21, 15207, antifever, 9/22, 15213, CC = cough, ...

1,000,000 more records

Huge mass of dataFeedback loop

MODERN DISEASESURVEILLANCE

Our Approach

M i C t


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Main Components

Feature extraction, reference andbaseline information

Tags

Multiple Data Streams

User-Generated and Machine LearningMetadata

Comments

Spatio-temporal

Flags/Alerts/Bookmarks

EventClassification,

Characterizationand Detection

Previous Event Training Data

Previous Event Control Data

Metadataextraction

Machinelearning

Social network

Professionalfeedback

Anomalydetection

Collaborative Spaces

Hypotheses generation\testing

Our Solution

Main Components


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Main Components

Our Solution


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Item

Hypothesis

Field Actions and

Verifications

Feedback /

Confirmation

Our Solution


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ADVANTAGES OF MACHINELEARNING

P(malaria) = 22%P(influenza) = 13%

P(other ILI) = 33%

Our Solution


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MACHINE LEARNINGTECHNIQUES

Classifiers

Clustering

Bayesian Statistics Neural Networks

Genetic Algorithms

Our Solution


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HOW TO REPRESENT ADOCUMENT?

cold

fever

Our Solution


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CLASSIFIERS PROBLEMDEFINITION

Map items to vectors (Featureextraction)

Normalize those vectors

Train the classifier

Measure the results with new

information Feedback the classifier

Separate classes in feature space

Our Solution


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CLASSIFIERS - SVM

Our Solution


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SVM NON LINEAR?

: x(x)

Map to higher-dimension space

Our Solution

SVM FILTERING OR


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SVM FILTERING ORCLASSIFYING

Classifier

Document1

Document2

Document3

Positives

Negatives

TrainingDocument

TrainingDocument

Our Solution


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CLUSTERING PROBLEMDEFINITION

Map items to vectors (Featureextraction)

Normalization

Agglomerative and Partitional

Our Solution


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CLUSTERING -AGGLOMERATIVE

Our Solution


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CLUSTERING - PARTITIONAL

Our Solution


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BAYESIAN STATISTICS

P(A|B)=P(B|A).P(A)

P(B)

Probability ofdisease A (flu)

once symptomsB (fever) are

observed

Probability offever once fluis confirmed

Probability offlu (prior ormarginal)

Probability offever (prioror marginal)

Our Solution


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NEURAL NETWORKS

Given a set of stimulus, train asystem to produce a given output

Our Solution

NEURAL NETWORKS


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Hidden Layer

Output Layer

Input Layer

NEURAL NETWORKS -STRUCTURE

[]

[]

{I0,I1,In}

{O0,O1,On}

Weight

Hn= (I

i.

i=0

I

win)

Our Solution

NEURAL NETWORK


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NEURAL NETWORK -APPLICATION

Event?

Our Solution

GENETIC ALGORITHM


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GENETIC ALGORITHM -BASICS

Define the model that you wantto optimize

Create the fitness function

Evolve the gene pool testingagainst the fitness function.

Select the best individual

Our Solution


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GENETIC ALGORITHM


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GENETIC ALGORITHM MODEL FITNESS

Fitness = 1/Area

Our Solution

GENETIC ALGORITHM


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GENETIC ALGORITHM PROCESS

1.Create an initial population ofcandidates

2.Use operators to generate new

candidates (mating and mutation)3.Discard worst individuals or select best

individuals in generation

4.Repeat from 2 until you find a

candidate that satisfies the solutionsearched

Our Solution

GENETIC ALGORITHM


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(4,5,6,3,5)(4,3,6,2,5)

GENETIC ALGORITHM -PROCESS

(5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2)

(2,3,4,6,5) (3,4,5,2,6)

(3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6)

(4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4)

(5,3,2,6,5)

(3,4,4,6,2)

(5,3,2,6,5)

(3,4,4,6,2)

Our Solution

RESULTS IMPROVED


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RESULTS IMPROVEDSURVEILLANCE

Our Solution


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Our Solution

InSTEDD Evolve


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Our Solution

InSTEDD Evolve


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Our Solution

InSTEDD Evolve


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Our Solution

InSTEDD Evolve


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Our Solution

InSTEDD Evolve


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Acknowledgement


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Documents

Bio Surveillance 2.0 Kass-Hout and Di Tada