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Carnegie Mellon University's presentation on how to model disease threat levels
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BIOWAR Objective
Automated tools for evaluation of response policies, data e~cacy, attack severity, and detection tools relating to weaponized biological attacks
Tasks - Develop prototype computational
model of responses to weaponized biological attacks at the city level
- Generation of artificial data for early detection studies
- Illustrations of use - 'What IF" - Initial data integration and
validation c Biomedieal Secunty Institute 2001
Approach - Combine network,
epidemiological, and geographical components into adaptive multi-agent network model that can be used as a "what ir analyzer
Progress - Initial alpha prototype model
capable of generating high level general behavior
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Base Scenario
Description ~Profile
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-~-Cky~ What if Scenario.
O Bt0medical Secunty Institute 2001.
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Why Use Computational Modeling and Analysis?
Ethical: Cannot test response policies on real populations Preparatory: Can create hypothetical weapons with more
potency than existing ones - Can examine wide range of scenarios
Cost effective: Creating new technologies, procedures and legislation for data collection is expensive
Faster: Real time evaluation of existing systems is too time consuming
Appropriate: Complex non-linear dynamic system Flexible: Response to novel situations requires rapid evaluation
of previously unexamined alternatives
IC Biomedical Security Institute 2001 .
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Tasks Evaluation of existing computational models Design and develop prototype system for examining weaponized
biological attacks on city level populations - Combine epidemiological and communication network models bZJ - Create disease models - Create realistic agent models ii - Create symptom testing models
prototype
- Be able to read/write to shared NEDSS database - Scale model to city size - Link to geographical location data model and presentation (Arcvlew)
Initialize model with real-world data - Physical location, Census, Demographics, Social network, Cognitive
biases Develop and illustrate 'What If' capabilities Initial validation using influenza data
e Blomed1cal Security Institute 2001 .
Limitations of Existing Models Epidemiological models assume uniformity of population - networks Social network I communication mod~ls ignore disease Existing agent-based models cognitively, socially and
geographically unrealistic Lack of connection to real large scale data
Challenges to Be Met Combine epidemiological, network and geographical location models Create cognitively and geographically realistic agent-based model Create a flexible enough system to explore a wide range of
unanticipated scenarios Data integration and validation
C> Biomechcal Secunty lnsbMe 2001
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Approach Multi-Agent Network Model
- Cognitive realistic Socially realistic - Embedded in to social, knowledge and task networks Integrated with geographic model
- Organizational/Unit network - Communication technologies
Hybrid of many models Spatial - Cost
- Disease Network Epidemiological
Examine
- Agent
Existing standard diseases viral and non viral - influenza - Weaponized contagious - pneumonic plague, smallpox - Weaponized non-contagious - anthrax
What If Analysis e Biomedical Security lnsutute 2001
BIOWAR Design ~---------------------.... derection privacy
Agent Model
--
C Biomedical Security Institute 2001 .
Stared BSS Dal.ii base
NEDSS Co111>lian1
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Parameterization & Initialization of Bio War
Integration - Utilizing Real Data for Parameterization - Disease Data
Lethality, Type, Symptoms, Timing
- Cognitive Data Demographic Data
- Geographic Data - Behavioral Data
Multi-source
Public
4-' BiomedJCal Security tnstrtute 2001
Data Source - Disease Data
Archival, Medical Journals, Historical Accounts
- Cognitive Data Human experimental and field studies In cognitive science
- Demographic Data Census GSS
- Geographic Data Maps, Census
- Behavioral Data Human experimental and field studies in sociology, anthropology, psychology
Verification & Validation BloWar - Simulated Data
- General Behavior Cross-sectional Over time
- Virtual Response Data
Validation - Real Data - General behavior
Herd immunity
- Influenza Grade School Absenteeism ER reports Pharmacy purchases Death reports
Absenteeism ER visits Pharmacy Death rate Web hits Cost
Inform future data collection More Options ~ Samples, Incomplete
Validate and tune model C Biomedical Secunty Institute 2001.
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BioWar Alpha Prototype: Illustrative Results Single Virus Influenza Attack
B10War lnfected Influenza Actual
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-
1 Absenteeism --
Summary of Plans Develop BIOWAR Combine network, epidemiological, geographical, disease,
symptom, cost components into adaptive multi-agent what if analyzer
Scale system to city level Illustrate use of BIOWAR
- Evaluate possible early response policies - Evaluate relative efficacy of different early detection data sources
and privacy policies - Evaluate relative severity of different types of attacks
Generation of artificial data for early detection studies - Anthrax - Pneumonic Plague - Smallpox - Influenza
Initial data integration and validation o Boomedical Secunty lnstitule 2001.
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