Modeling Human Response to
Threats and Disasters
John H. Sorensen
Oak Ridge National Laboratory
May 29-30, 2003
Major Modeling Thrusts in
Disaster Research
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Warning Response
Warning Diffusion
Evacuation Behavior
Protective Action Effectiveness
Psycho-Social Impacts
Intelligent Consequence Management
Warning Response Research
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Started in the 1950’s
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
Warning Response Process
Factors Increasing Response
• Receiver Characteristics
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Visual and other cues
Family and network
Female
Younger
Majority
High SES
Non-fatalistic
• Sender Characteristic
– Message source
– Message channel
– Message style
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Clear
Specific
Accuracy
Certain
Consistency
– Message Content
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Alternative Responses to
Natural 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
Modeling Diffusion of Warning
Diffusion Of Warnings
Portion receiving warnings
1.0
0.8
SIRENS
0.6
TONE ALERT
TELEPHONE
0.4
MEDIA
SIRENS/TONE ALERTS
SIRENS/TELEPHONES
0.2
0.0
0
5
10
15
T ime (min)
20
25
30
Normalized Warning Diffusion by Source
100
Cumulative Percent
90
80
70
60
50
40
SIRENS
ROUTE
INFORMAL
MEDIA
30
20
10
0
1 am
60
120
180
2 am
3 am
T ime
240
4 am
Simulated Vs Observed at Nanticoke
100
CUM. %
80
60
40
Sirens
Simulated Siren
20
0
2:20
14
0
15
0
16
0
17
0
18
0
19
0
3:00
20
0
21
0
3:30
T ime
22
0
23
0
24
0
4:00
Mobilization Time By Order of Evacuation
100
90
Cumulatve Percent
80
70
60
50
40
Area One
Area Two
Area Three
30
20
10
0
0
20
40
60
T ime (min)
80
100
120
PROTECTIVE ACTION EFFECTIVENESS MODEL
ACCIDENT
SCENARIO
MET
CONDITIONS
DECISION
TO
WARN
PROTECTIVE
ACTION
EFFECTIVENESS
DISPERSION
CODE
WARNING
SYSTEM
HUMAN
RESPONSE
PROTECTIVE
ACTION
DOWNWIND
CONCENTRATION
DOSE
REDUCTION
EXPECTED
DOSE
Intelligent Consequence
Management
• New sensor networks or links to existing sensor
networks designed to detect and monitor the
threats of concern
• High-speed communications and data exchange
• Real-time simulation models running on highspeed machines
• Faster than real-time predictive capabilities
• Advanced decision support tools that can process
data and simulation outputs into a format useful to
decision-makers
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
Intelligent Consequence Management Architecture for Rad/Chemical Incident
Alerting Sensor
Sound Preparedness Alert
Notify Emergency Response Team
Activate Monitors
Data Archive
Outdoor Dispersion
Model
Generate Evacuation Plan
Classify
Event
Accident
Library
Send Data
Activate Field
Monitoring
Distribute Plans
(Electronically)
Distribute to Emergency
Response Team
Send in Response Team
Initiate Search & Response
System components
to be tested are in
red
Choose Protection Plan
Evacuate
Dispersion
Scenario
Library
Protective
Action
Library
Protection
Action ES
Damage
Assessment
Go To Safe Room
Run Economic
Model
Generate Evacuation
To Safe Room Plan
Run Evacuation and
Shelter Models
Activate Warning
With Evacuation
Instructions
Data
Archive
RT Traffic Counters
Generate Emergency
Response Plan
Generate Sampling Plans
Initiate Sampling
Initiate Decontamination
Generate Search & Rescue Plan
Major Questions
• How will warnings be issued to publics once a bio
event is identified?
• To what degree will human behavior in a bio event
be similar to other hazards?
• Will bio events elicit a different types of human
response than observed for other hazards?
• What are the relevant parameters to model in a bio
event?