Pandemic Flu Modeling Using @Risk
Planning for the Unknown And
The Fallacy of “The Number”
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Agenda
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What is Pandemic Influenza
Planning Challenges
The Solution and the Role of @Risk
Q&A
Background
Katrina, Rita, Gustav and Ike
Characteristics of Pandemic
Influenza
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Katrina and Siblings
• Katrina and Rita Family Assistance Center
▫ Managing Missing Persons, Fatalities and DNA
▫ Use of @Risk and Neural Tools
• Gustav and Ike
▫ Use of @Risk
to avoid
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Pandemic Flu: If or When?
• “This is the one health threat
we’re preparing for that we
know will happen”
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▫ Bill Raub, ASPHEP
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What is Pandemic Influenza?
• The most extreme example of an acute infectious
disease outbreak
▫ Greater potential to cause rapid increases in death and
illness than virtually any other natural health threat.
• An explosive global event in which most, if not all,
persons worldwide are at risk for infection and
illness
• The ability to infect, within one year, one third or
more of large populations and lead to tens of
millions of deaths worldwide.
• Planning and preparedness before the next
pandemic are critical for an effective response.
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Types of Influenza
• Seasonal (or common) flu
▫ Respiratory illness that can be transmitted person to person. Most
people have some immunity; vaccine available.
▫ It is “left over” from previous pandemics.
• Avian (or bird) flu
▫ Caused by influenza viruses that occur naturally among wild birds.
▫ The H5N1 variant is deadly to domestic fowl and can be transmitted
from birds to humans.
▫ No human immunity and no vaccine.
• Pandemic flu
▫ A virulent human flu (often from mutation of an avian flu) that causes a
global outbreak (pandemic) of serious illness.
▫ Since little natural immunity, the disease can spread easily from person
to person.
▫ Currently, there is no pandemic flu.
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Common Influenza Disease
• It is a common but frequently serious disease characterized by
fever, fatigue, body pain, headache, dry cough and sore throat.
• Familiarity of seasonal influenza epidemics in the U.S. has
lead to a great under-appreciation of their true health impact.
▫ 36,000 yearly deaths in the U.S.
▫ 114,000 hospitalizations
• Between $1 and $3 Billion in direct costs every year
▫ Due to the secondary complications of influenza infection such as
pneumonia, dehydration, and worsening of chronic lung and
heart problems.
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20th Century Influenza Pandemics
• 1918 – Spanish Flu
▫ Worldwide 20-50 million deaths
▫ 500,000 deaths in US
• 1957-1958 – Asian Flu
▫ Worldwide 1-2 million deaths
▫ 70,000 deaths in US
• 1968-1969 – Hong Kong Flu
▫ Worldwide 700,000 deaths
▫ 34,000 deaths in US
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Features of Pandemic Influenza
• Expected but unpredictable
• Outbreaks can be expected to occur simultaneously
• Effect of the pandemic on individual communities will be relatively
prolonged (weeks to months)
• Up to 1/3 of population affected at any given time
• Health care workers and other first responders may be at higher risk
of exposure and illness than the general population
• Effective prevention and therapeutic measures, including Vaccines
and Anti-virals will be delayed and in short supply.
• Sudden and potentially significant shortages of personnel in other
sectors who provide critical public safety services should be
expected.
Planning Factor: 50% of staff out for 2 months
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Lessons from Past Pandemics
• Occur unpredictably, not always in winter
• Variations in mortality, severity of illness and
pattern of illness
• Rapid surge in number of cases over brief period
of time, often measured in weeks
• Tend to occur in waves
▫ Each wave lasts about 2-3 months
▫ Subsequent waves may be more or less severe
▫ Generally occur 3-12 months after previous wave
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Forecasted Impact in the US
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Up to 200 million persons Infected
Between 38-89 million clinically ill
Between 18-42 million requiring outpatient care
Between 314,00 – 733,000 hospitalized
Between 89,000 – 207,000 deaths
What’s missing?
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Planning Components
• Incorporate Pandemic Influenza into the State
All Hazards Plan
▫ COOP Plans for all government agencies and
private industry
• C3
▫ Command, Control, and Communications
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Pandemic Influenza Surveillance
Healthcare Planning – Critical Infrastructure
Infection Control
Clinical Guidelines
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Planning Components
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Vaccine Distribution and Use
Antiviral Drug Distribution and Use
Point of Distribution Clinics
Alternative Care Sites
Community Disease Control and Prevention
Managing Travel-Related Risk of Disease
Transmission
• Public Health Communications
• Workforce Support
• Mass Fatality Management
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Pandemic Influenza
“The pandemic influenza clock is ticking… we just
don’t know what time it is….”
Ed Marcuse, ACIP
What’s the Result?
Increasing pressure for “the
number” in all plans and at all
levels of government
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The Problem
• Planning for management of the expected
number of infected persons and fatalities
• What makes this event unusual?
▫ No “calvary”
▫ Supply chain and logistics disruption
▫ Large numbers dying in homes and other “noncontrolled” environments
• Lots of “unknowns” and “uncertains”
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The Traditional Method
• Establish the “planning factors”
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Attack rates
Mortality rates
Infrastructure characteristics
Workforce characteristics
• Draw conclusions
▫ Direct influence on downstream requirements
• What’s missing?
▫ Undocumented assumptions
▫ No capacity for pervasive variability
▫ No probabilities or knowledge of sensitivities
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The Traditional Method
• The method has been simply to ask for “the
number” in hundreds of areas
▫ No vetting of the estimate or review of methodology
▫ Pure guesses
• The results (the target or output) is then used as
the “planning factor” for all downstream
requirements
▫ Number of patients, deaths, ICU beds, ventilators,
other system capacities, etc.
• Once established, “the number” becomes sacred
▫ Again, any knowledge of the inherent variability is
lost or glossed over
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New Method Proposed
• Tap into a variety of data sources to construct
parameters for the major variables:
▫ Hospital clinical patterns and flows
▫ Attack and mortality rates
▫ Critical infrastructure availability, including workforce resources
• Incorporate variability and simulation into all major
areas
▫ Go beyond three-point estimates (minimum, most likely, and
maximum)
▫ Probable outcomes and sensitivity analyses
• Incorporate outputs in formats easily presentable to
decision-makers
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Google Knows All
• Google claims that
certain search terms are
good indicators of flu
activity.
• Google Flu Trends uses
aggregated Google
search data to estimate
flu activity in your state
up to two weeks faster
than traditional flu
surveillance systems.
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Proposed Solution
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Construct a logic model
Build a model structure
Incorporate estimates for key variables
Give end-users a tool that can incorporate
uncertainty
Base Model: Panalysis® developed by Mark Abromovich,
Interdisciplinary Solutions, LLC, 2008
Visit www.panalysismodel.com
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Panalysis® Design Considerations
• Model inputs consist of:
▫ Disease-specific inputs, which define the characteristics of
the epidemic in the hospital’s community, and
▫ Hospital-specific inputs, which describe the capacity and
resources of the hospital
• Includes seven epidemiologic curves
▫ Some based on historical data such as the 1918 pandemic
• Ranges of attack rates and severity
▫ Consistent with historical pandemics or government
estimates
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Mortality
Mortality
Core Panalysis® Logic Model
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Major Components
• Hospital characteristics
▫ Bed types and various
rates (occupancy, ALOS)
▫ Elective surgery data
▫ Triage assessment
capacity
▫ Manpower availability
▫ Ventilator data
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Major Components
• Disease characteristics
▫ Attack rates
▫ Age distributions
▫ Clinical severity factors
▫ Fatality assumptions and
rates
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Major Components
• Staff Augmentation
▫ Person-power
Availability
Augmentation
▫ Cross Utilization
▫ Workweek Increase
• Bed Capacity and
Equipment Augmentation
▫ Bed-related surge
strategies
▫ Ventilator Augmentation
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Results Window
• Settings for scenario to be modeled
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Results Window
• Hospital Capacity Results
28 Capacity Results
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Results Window
• Hospital
Capacity
Results
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Results Window
• Staffing, Ventilator
and Fatality
Forecasts
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Results Window
• 32 summary
indicators
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@Risk Model Elements
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Use of @Risk
• Generally used triangular distributions
▫ “Safe” starting point
• Distribution characteristics
▫ Parameters for the disease-specific distributions
based on past pandemics
▫ Parameters for the hospital-specific distributions
based on hospital experience data
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Sensitivities
• Ability to present key influencing variables to
senior management and clinical staff
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Benefits of @Risk
• Simulation benefits obvious
• Capability to produce output easily understood
by senior leadership
• Ability to run complex models very quickly
▫ This model took over 1 hour to run using an older
version of @Risk. The current version (5.0.1) takes
less than 3 minutes.
x Output options are much more flexible and intuitive
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What’s Next?
• Educating senior leadership on the value of
simulation modeling (they may already be sold!)
• Extension of the basic Panalysis® model
• Extending input and output capabilities
• Incorporate downstream supply forecasts
• Implications for other areas of panflu planning
▫ Developing data sources and a data management
strategy
▫ Developing basic models for other plan components:
x Point of Distribution (POD) operations
x Vaccine distribution
x Fatality management operations
Questions?