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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.

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gaming future health crises

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

developing games

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


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.

topoff 35


Scenario: simulated biological attack.

Vehicle-based biological agent.

Vehicle left in parking lot at Kean University.

Agent later identified as pneumonic plague.

topoff 36


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 37


TOPOFF 3 in NJ also involved a mock cyber attack in NJ and a chemical weapon attack in Connecticut.

topoff 3 general observations

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.

topoff 3 general observations9

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

topoff 3 general observations10

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?

topoff 3 general observations11

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.

topoff 3 concept of pod

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

topoff 3 concept of pod13

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.

topoff 3 concept of pod14

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

topoff 3 concept of pod15

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?

topoff 3 concept of pod16

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.

topoff 3 concept of pod17

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

topoff 3 concept of pod18

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.

topoff 3 communications

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


In TOPOFF 3, some volunteers

got their information from

google searches!

topoff 3 communication

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 attacks

topoff 3 communication21

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.

topoff 3 communication22

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?

topoff 3 closing comment

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.


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.



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?

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

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


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

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

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


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

acknowledgements topoff collaborators

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

topoff reference

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), 41-51.