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Modeling Human Response to
Threats and DisastersJohn H. Sorensen
Oak Ridge National LaboratoryMay 29-30, 2003
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Major Modeling Thrusts in
Disaster Research
Warning Response Warning Diffusion
Evacuation Behavior Protective Action Effectiveness Psycho-Social Impacts
Intelligent Consequence Management
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Warning Response Research
Started in the 1950s Driven by the shadow of nuclear war
If we sound the sirens, what will people do? Series of studies - tornado, hurricane, flood,
explosion, air raid sirens, alien invasions Major findings
People seek more information People converge on event
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Warning Response Process
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Factors Increasing Response
Receiver Characteristics Visual and other cues
Family and network Female Younger Majority High SES Non-fatalistic
Sender Characteristic Message source
Message channel Message style
Clear Specific
Accuracy Certain Consistency
Message Content
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Alternative Responses toNatural and Technological
Hazards Do nothing/ denial Confirm warning/ seek information
Evacuate/ temporary relocation Seek protective shelter/ stay home/ isolation Respiratory protection
Decontaminate Seek medical attention Help others
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Modeling Diffusion of Warning
3025201510500.0
0.2
0.4
0.6
0.8
1.0
SIRENS
TONE ALERT
TELEPHONE
MEDIA
SIRENS/TONE ALERTS
SIRENS/TELEPHONES
Time (min)
P o r t i o n r e c e
i v i n g w a r n
i n g s
Diffusion Of Warnings
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240180120600
10
20
30
40
50
60
70
80
90
100
SIRENS
ROUTE
INFORMAL
MEDIA
Normalized Warning Diffusion by Source
Time
C u m u l a
t i v e
P e r c e n
t
1 am 2 am 3 am 4 am
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240230220210200190180170160150140
0
20
40
60
80
100
Sirens
Simulated Si ren
Simulated Vs Observed at Nanticoke
Time
2:20 3:00 3:30 4:00
C U M
. %
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1201008060402000
10
20
30
40
50
60
70
80
90
100
Area OneArea Two
Area Three
Mobilization Time By Order of Evacuation
Time (min)
C u m u
l a t v e
P e r c e n
t
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PROTECTIVEACTION
EFFECTIVENESS
ACCIDENTSCENARIO
DECISIONTO
WARN
WARNINGSYSTEM
DISPERSIONCODE
DOWN-
WINDCONCENTRATION
METCONDITIONS
EXPECTEDDOSE
PROTECTIVE ACTION EFFECTIVENESS MODEL
PROTECTIVEACTION
HUMANRESPONSE
DOSEREDUCTION
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Intelligent Consequence
Management New sensor networks or links to existing sensor
networks designed to detect and monitor thethreats of concern
High-speed communications and data exchange Real-time simulation models running on high-
speed machines
Faster than real-time predictive capabilities Advanced decision support tools that can process
data and simulation outputs into a format useful todecision-makers
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ORNL LDRD
Dynamic evacuation modeling Utilize deployable road sensor tape or
existing monitors First evacuation model with dynamic traffic
assignment
Can update simulations using real time data Linked to GIS
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Alerting Sensor Sound Preparedness Alert
Intelligent Consequence Management Architecture for Rad/Chemical Incident
Notify Emergency Response Team
Activate Monitors Choose Protection Plan
ProtectionAction ES
ProtectiveActionLibrary
Data Archive
DispersionScenarioLibrary
Evacuate Go To Safe Room
Generate Evacuation Plan Generate EvacuationTo Safe Room Plan
Distribute Plans(Electronically)
ClassifyEvent
Activate WarningWith EvacuationInstructions
RT Traffic Counters
DataArchive
Generate Sampling Plans
Generate Search & Rescue Plan
Distribute to EmergencyResponse Team
Generate EmergencyResponse Plan
Send in Response Team
Initiate Search & Response
Initiate Sampling
Initiate Decontamination
Activate FieldMonitoring
Send Data
Outdoor DispersionModel
Run Evacuation andShelter Models
System componentsto be tested are inred
AccidentLibrary
DamageAssessment
Run EconomicModel
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Major Questions
How will warnings be issued to publics once a bioevent is identified?
To what degree will human behavior in a bioevent be similar to other hazards?
Will bio events elicit a different types of humanresponse than observed for other hazards?
What are the relevant parameters to model in a bioevent?