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How Modelers can Help Policymakers before and during Health Crises. Fred Roberts Rutgers University. 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.
One way to prepare for future health crises is to “game” them.
Modelers can help to:
Play in games
Analyze the results
This is a hot area in computer science as many “exercises” can be “virtual”
Computer game design
Immersive games (MIT epi game)
Theories of influence and
persuasion from behavioral
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.
Scenario: simulated biological attack.
Vehicle-based biological agent.
Vehicle left in parking lot at Kean University.
Agent later identified as pneumonic plague.
Local hospitals involved – patients streaming in.
All NJ counties became Points of Dispensing (PODS) for antibiotics.
One POD was at the Rutgers Athletic Center.
TOPOFF 3 in NJ also involved a mock cyber attack in NJ and a chemical weapon attack in Connecticut.
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.
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.
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?
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.
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
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.
Modeling the POD:
How do you handle panic within the POD?
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
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?
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.
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
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?
Clearly, modelers needed to make precise the advantages of different POD concepts.
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
In TOPOFF 3, some volunteers
got their information from
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 attacks
Role of the media is important
In TOPOFF 3, there was a Virtual News
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.
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?
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.
New Orleans hurricane 2005
Turkey earthquake 1999
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
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
“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), 41-51.