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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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

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C Biomedical Security Institute 2001 .

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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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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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