How Modelers can Help Policymakers before and during Health Crises

1. How Modelers can Help Policymakers before and during Health Crises Fred Roberts Rutgers University

2. 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

3. 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

4. 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.

5. TOPOFF 3 Scenario: simulated biological attack. Vehicle-based biological agent. Vehicle left in parking lot at Kean University. Agent later identified as pneumonic plague.

6. 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.

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

8. 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.

9. 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

10. 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?

11. 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.

12. 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

13. 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.

14. 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

15. 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?

16. 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.

17. 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

18. 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.

19. 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!

20. 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

21. 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.

22. 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?

23. 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.

24. 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

25. 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?

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. 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

33. 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.