How Modelers can Help
Policymakers before and during
Health Crises
Fred Roberts
Rutgers University
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Gaming Future Health Crises
•One way to prepare for future health crises is to
“game” them.
•Modelers can help to:
–Develop games
–Play in games
–Analyze the results
of games
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Developing Games
•This is a hot area in computer science as many
“exercises” can be “virtual”
•It involves
–Computer game design
–Immersive games (MIT epi game)
–Artificial intelligence
–Machine learning
–“Virtual reality”
–Theories of influence and
persuasion from behavioral
science
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TOPOFF 3
•TOPOFF 3 was an exercise held in April 2005 in
New Jersey (and elsewhere)
•Goal: provide federal, state, and local agencies
a chance to exercise a coordinated response to a
large-scale bioterrorist attack.
•Some university faculty were invited to be
official observers.
•We helped with “after-action reports” and made
recommendations.
•We didn’t get involved early enough to interact
(as modelers) with policy makers or even exercise
designers.
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TOPOFF 3
•Scenario: simulated biological attack.
•Vehicle-based biological agent.
•Vehicle left in parking lot at Kean University.
•Agent later identified as pneumonic plague.
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TOPOFF 3
•Local hospitals involved – patients streaming in.
•All NJ counties became Points of Dispensing
(PODS) for antibiotics.
•One POD was at the Rutgers Athletic Center.
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TOPOFF 3
•TOPOFF 3 in NJ also involved a mock cyber
attack in NJ and a chemical weapon attack in
Connecticut.
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TOPOFF 3: General Observations
•Totally scripted or playbook exercise.
•Lacked random introduction of surprise or
contradictory information.
–Would models have helped the designers here?
•No flexibility for game controller to change
agenda – even after the identity of the biological
agent was disclosed a week before the event
started.
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TOPOFF 3: General Observations
•Very quick identification of the agent as plague –
less than 24 hours.
–Would modeling have helped here?
–Pneumonic plague takes 2-3 days before symptoms
appear
•No “chaos” of responding to an unknown
biological agent.
Pneumonic plague
in India
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TOPOFF 3: General Observations
•Lack of truly significant random perturbations
–Underscores importance of randomness in modeling
responses to health events
•No inconsistent information that might lead to
refutation of initial hypothesis
–Would modeling have helped develop a better
exercise in this sense?
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TOPOFF 3: General Observations
•People were being shipped off to hospitals
without any idea (in the “script”) of what the
contaminant might have been.
–Models might help us understand the danger of such
a decision.
•Idea of quarantine on Kean University campus
was not considered.
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TOPOFF 3: Concept of POD
•In a POD: We bring together large numbers of
people to receive their materials in one location.
–Hand out antibiotics
–Hand out educational materials about the disease and
the medicine
•How do you get them there?
–Modeling issues – traffic congestion, parking, etc.
–Our input to after-action report noted that this was
not considered
–Our ideas were included in the AA report
–Policy makers should be interested
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TOPOFF 3: Concept of POD
•Modeling the POD:
–How do you get enough volunteers?
–How do you get food to the volunteers? The
patients?
–Who gets priority? Triage.
–Our input to AA report also mentioned importance of
these issues.
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TOPOFF 3: Concept of POD
•Modeling the POD:
–How do you handle panic within the POD?
–Pushing, shoving.
–People on long lines.
–People on lines getting sick.
–In our observation: TOPOFF 3
had none of these elements.
–Modeling challenge: social
responses to health events
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TOPOFF 3: Concept of POD
•Disease Model Flaws
–What if agent was a contagious communicable
disease before an individual displayed symptoms?
–In case of pneumonic plague, infection via droplets –
so importance of triage. But what if your triage isn’t
perfect and an infected individual exposes others in
the POD?
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TOPOFF 3: Concept of POD
•POD Loading Issues:
–What is maximum capacity of a POD?
–How many workers are needed?
–How much time is it reasonable to keep patients
there?
–How to handle short preparation time before masses
of people arrive?
–What is adequate time to screen individuals?
–How do you prevent a secondary attack if a mass of
people are gathered in one place?
–These are all modeling issues.
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TOPOFF 3: Concept of POD
•Some conclusions about PODS:
–The most successful POD violated the rules.
–It was a Point of Distribution, not a Point of
Dispensing.
–Medicines were distributed to a few people in large
quantities.
–They in turn redistributed the drugs to others – away
from the POD.
–Record keeping in advance helped distributors know
where to go and whom to give drugs to
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TOPOFF 3: Concept of POD
•Some conclusions about PODS:
–The most successful POD serviced 67,000 people in
4 hours. This was the one that wasn’t really a POD.
–The others serviced 500 to 1000.
–Decentralization could be a key – avoid mass
movement of people
–Advantages of dispensing drugs and information in
local communities.
–But: is decentralization always best?
–Modeling challenges
–Clearly, modelers needed to make precise the
advantages of different POD concepts.
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TOPOFF 3: Communications
•Communications are critical in a crisis.
•What are the best communication paths between
command centers and those on the firing line?
–This too can be modeled.
•What protocols can be developed for who can
call whom and in what order?
–This involves algorithm
development.
•In TOPOFF 3, some volunteers
got their information from
google searches!
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TOPOFF 3: Communication
•Secondary attacks are a serious threat.
•Issues of evacuation or “stay in place”
–What is role of the larger employers?
–Can we model using them as Points of Dispensing?
–Policy makers clearly taking note of this idea.
•Cyber attacks are a real danger.
–Much information at PODS was obtained via the
Internet
–Modeling cyber attacks – a major research challenge
–We continue to talk to policy makers about cyber
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attacks
TOPOFF 3: Communication
•Role of the media is important
–In TOPOFF 3, there was a Virtual News
Network (VNN)
–However, VNN reporters were unprotected at
various sites
–VNN was primary source of information for many.
–Model how best to use different media – including
printed materials dispensed at churches, supermarkets,
etc.
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TOPOFF 3: Communication
•Risk communication is important
–We viewed the Governor’s press conference.
–No sense of urgency as in real emergency
–Could impact of different uses of language and
different sets of instructions have been modeled?
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TOPOFF 3: Closing Comment
•Officials in NJ and at FEMA were very
interested in our observations.
•They seemed quite open to more technical
analysis of the exercise.
•Modeling in advance might have helped make a
better exercise.
•Modeling certainly could help in analyzing the
results of an exercise.
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Behavioral Responses to Health Events
•Governments are making detailed plans for how to
respond to future health “events” such as pandemic
influenza, a bioterrorist attack with the smallpox
virus, etc.
smallpox
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Behavioral Responses to Health Events
•As noted, a major flaw in TOPOFF 3 was to
“game” (potentially chaotic) behavioral responses.
•A major unknown in planning for future disease
outbreaks is how people will respond.
Will they follow instructions to stay home?
Will critical personnel report to work or take
care of their families?
Will instructions for immunization be followed?
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Behavioral Responses to Health Events
•Models in epidemiology typically omit
behavioral responses.
Hard to quantify.
Hard to measure.
•Leads to challenges for behavioral scientists.
•Leads to challenges for modelers
•Leads to challengers to the interface between
modelers and policy makers
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Behavioral Responses to Health Events
•We can learn some things from the study of
responses to various disasters:
Earthquakes
Hurricanes
Fires
Etc.
New Orleans hurricane 2005
Turkey earthquake 1999
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Behavioral Responses to Health Events
Some Behavioral Responses that Need to be
Addressed:
•Compliance:
Quarantine
Resistance
Willingness to seek/receive treatment
Credibility of government
Trust of decision makers
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Behavioral Responses to Health Events
Some Behavioral Responses that Need to be
Addressed:
•Movement
•Rumor
•Perception of risk
•Person to person interactions
•Motivation
•Social stigmata (discrimination against social
groups)
•Panic
•Peer pressure
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Behavioral Responses to Health Events
Some Challenges:
•How do we measure some of these factors?
•How do we bring them into mathematical models?
•How do we test out our ideas and make them useful
in practical decision making.
•Hard to decide these things without a dialogue
between modelers and policy makers
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Acknowledgements
•NJ Dept. of Health and Senior Services
•NJ Office of Homeland Security and
Preparedness
•New Jersey State Police
•New Jersey State Police Office of Emergency
Management
•New Jersey Office of Attorney General
•Dept. of Homeland Security FEMA
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Acknowledgements: TOPOFF
Collaborators
•Paul Lioy, UMDNJ
•Brendan McCluskey, UMDNJ
•Mary Jean Lioy, Rutgers
•Audrey Cross, Columbia
•Lee Clarke, Rutgers
•Louise Stanton, Rutgers
•William Tepfenhart, Monmouth
•Mary Ellen Ferrara, Monmouth
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TOPOFF Reference
“TOPOFF 3 comments and recommendations by
members of New Jersey Universities Consortium
for Homeland Security Research” (P.J. Lioy, F.S.
Roberts, B. McCluskey, M.J. Lioy, A. Cross, L.
Clarke, L.L. Stanton, W. Tepfenhart, E. Ferrara),
Journal of Emergency Management, 4 (2006), 4151.
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