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Uncertainty Analysis of Pandemic Influenza Impacts on National
Infrastructure

Supplement to the National Population and Infrastructure Impacts of
Pandemic Influenza Report

15 October 2007

**Prepared by**

**National Infrastructure Simulation & Analysis Center**

**Infrastructure Analysis and Strategy Division**

**Office of Infrastructure Protection**

**Department of Homeland Security**

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# Table of Contents {#table-of-contents .unnumbered}

[1. Introduction 1](#introduction)

[2. Model Overview 3](#model-overview)

[2.1 Infectious Disease Model 4](#infectious-disease-model)

[2.1.1 Disease Stages 4](#disease-stages)

[2.1.2 Demographic Groupings 4](#demographic-groupings)

[2.1.3 Vaccination, Antiviral Prophylaxis and Treatment, Quarantine, and
Isolation
5](#vaccination-antiviral-prophylaxis-and-treatment-quarantine-and-isolation)

[2.2 Regional Population Model 6](#regional-population-model)

[2.3 Public Health Model 7](#public-health-model)

[2.3.1 Normal Care 7](#normal-care)

[2.3.2 Treatment of Pandemic Flu Patients
9](#treatment-of-pandemic-flu-patients)

[2.3.3 Worried Well 10](#worried-well)

[2.4 Economic Impacts Model 11](#economic-impacts-model)

[3. Sensitivity and Uncertainty Analysis
13](#sensitivity-and-uncertainty-analysis)

[3.1 Approach 13](#approach)

[3.1.1 Experimental Design 13](#experimental-design)

[3.2 Mitigation Scenarios 14](#mitigation-scenarios)

[3.2.1 Contact Tracing 14](#contact-tracing)

[3.2.2 Social Distancing (Self-Quarantine)
15](#social-distancing-self-quarantine)

[3.2.3 Targeted Versus Mass Vaccination
16](#targeted-versus-mass-vaccination)

[3.2.4 Antiviral Treatment 17](#antiviral-treatment)

[3.3 Variable Ranges 17](#variable-ranges)

[3.3.1 Selection of Uncertain Parameters
17](#selection-of-uncertain-parameters)

[3.4 Biological Uncertainty Factors 20](#biological-uncertainty-factors)

[3.4.1 Reproductive Number 20](#reproductive-number)

[3.4.2 Nominal Fatality Rate 21](#nominal-fatality-rate)

[3.4.3 Asymptomatic Infection 22](#asymptomatic-infection)

[3.5 Sociological Uncertainty Factors
23](#sociological-uncertainty-factors)

[3.5.1 Nonpharmaceutical Interventions
23](#nonpharmaceutical-interventions)

[3.6 Public Health Uncertainty Factors
24](#public-health-uncertainty-factors)

[3.6.1 Base Fraction of Afflicted Seeking Healthcare
24](#base-fraction-of-afflicted-seeking-healthcare)

[3.6.2 Maximum Mortality Enhancement Multiplier
25](#maximum-mortality-enhancement-multiplier)

[3.6.3 Health Threat Reaction Time 25](#health-threat-reaction-time)

[3.7 Parameter Distributions 26](#parameter-distributions)

[3.7.1 Pre-symptomatic and Asymptomatic Spread
27](#pre-symptomatic-and-asymptomatic-spread)

[3.7.2 Effective Reproductive Number 31](#effective-reproductive-number)

[4. Results and Analysis 35](#results-and-analysis)

[4.1 Initial Sensitivity Analysis 35](#initial-sensitivity-analysis)

[4.2 Final Analysis 39](#final-analysis)

[4.2.1 Mitigation Strategies Compared
39](#mitigation-strategies-compared)

[4.3 Economic Impact 46](#economic-impact)

[4.3.2 Case Fatality Rate 52](#case-fatality-rate)

[4.4 Final Sensitivity Analysis 53](#final-sensitivity-analysis)

[4.4.1 Pandemic Model Comparison 56](#pandemic-model-comparison)

[4.5 Decision Model 72](#decision-model)

[4.5.1 Methodology 72](#methodology)

[4.5.2 Aid to Decision Making 74](#aid-to-decision-making)

[4.5.3 Decision Model Results 75](#decision-model-results)

[4.5.4 Satisfaction and Regret Analysis
80](#satisfaction-and-regret-analysis)

[4.5.5 Highlights and Comparison to EpiSimS Results
86](#highlights-and-comparison-to-episims-results)

[5. Summary and Conclusions 89](#summary-and-conclusions)

[Appendix A: Summary of Intervention Strategy Frequency Histograms
A-1](#appendix-a-summary-of-intervention-strategy-frequency-histograms)

[Appendix B: Summary of Intervention Strategy Consequence Statistics
B-1](#appendix-b-summary-of-intervention-strategy-consequence-statistics)

[Appendix C: Outlines for Nonpharmaceutical Mitigation Measures for
Pandemic Influenza
C-1](#appendix-c-outlines-for-nonpharmaceutical-mitigation-measures-for-pandemic-influenza)

[Appendix D: Summary of Cost Estimates
D-1](#appendix-d-summary-of-cost-estimates)

# List of Figures {#list-of-figures .unnumbered}

[Figure 2-1: Major elements of the Critical Infrastructure Protection
Decision Support System (CIPDSS) models used in the pandemic influenza
impacts analysis 3](#__RefHeading___Toc179875372)

[Figure 2-2: Representation of the multi-stage
susceptible-exposed-infected-recovered (SEIR)\
general infectious disease model in the Critical Infrastructure
Protection Decision Support System (CIPDSS)
4](#__RefHeading___Toc179875374)

[Figure 2-3: Division of the U.S. into 10 Federal Emergency Management
Agency (FEMA)\
regions 6](#__RefHeading___Toc179875375)

[Figure 2-4: Flow diagram for normal emergency care in the public health
model 9](#_Ref163955301)

[Figure 2-5: Flow diagram for pandemic flu patients in the public health
model 10](#_Ref163955345)

[Figure 2-6: Flow diagram for worried well patients in the public health
model 11](#__RefHeading___Toc179875378)

[Figure 3-1: The beta (5,20) distribution for the fraction of
transmission that occurs prior to\
clear symptoms 29](#__RefHeading___Toc179875380)

[Figure 3-2: The beta (6,8) distribution for the relative contagiousness
of asymptomatic\
infected people 29](#__RefHeading___Toc179875382)

[Figure 3-3: The beta (4,8) distribution for relative fraction of
asymptomatic infections\
compared to symptomatic infections 30](#__RefHeading___Toc179875384)

[Figure 3-4: The relationship of fraction of contagious population
isolated per day to the\
reduction in overall transmission of the disease
31](#__RefHeading___Toc179875386)

[Figure 3-5: Cumulative distributions of sampled *R~0~* and effective
*R* (all 512 realizations) 32](#__RefHeading___Toc179875388)

[Figure 3-6: Distributions of *R*~0~ and effective *R* (243 realizations
with *R* 1) 33](#__RefHeading___Toc179875389)

[Figure 4-1: Cumulative number of illnesses for baseline scenario runs
with no mitigations 35](#__RefHeading___Toc179875391)

[Figure 4-2: Scenario averages for different *R~0~* groupings
38](#__RefHeading___Toc179875392)

[Figure 4-3: Scenario Averages for total deaths due to the pandemic
41](#_Ref169404436)

[Figure 4-4: Scenario averages for flu illnesses
41](#__RefHeading___Toc179875396)

[Figure 4-5: Scenario ranges for flu illnesses 42](#_Ref169342768)

[Figure 4-6: Scenario ranges for flu deaths
43](#__RefHeading___Toc179875399)

[Figure 4-7: Patients treated in hospital versus effective reproductive
number (baseline) 44](#__RefHeading___Toc179875400)

[Figure 4-8: Scenario averages for patients hospitalized in temporary
care facilities 45](#__RefHeading___Toc179875402)

[Figure 4-9: Scenario ranges for patients hospitalized in temporary care
facilities 45](#__RefHeading___Toc179875404)

[Figure 4-10: Scenario averages for lost gross domestic product
46](#_Ref171131849)

[Figure 4-11: Scenario averages for peak fraction of workers unavailable
47](#__RefHeading___Toc179875408)

[Figure 4-12: Scenario ranges for lost gross domestic product
48](#_Ref164835702)

[Figure 4-13: Scenario ranges for peak fraction of workers unavailable
48](#__RefHeading___Toc179875411)

[Figure 4-14: Effect of social distancing on the no intervention
scenario 50](#__RefHeading___Toc179875413)

[Figure 4-15: Effect of social distancing on the targeted vaccination no
antivirals scenario 51](#__RefHeading___Toc179875414)

[Figure 4-16: Influenza deaths for the baseline scenarios by case
fatality rate 53](#__RefHeading___Toc179875416)

[Figure 4-17: Scatter plot of the overall attack rate against *R~0~* for
the baseline (no interventions; noSDnoAV, vaccination strategy 1);
feasibility assessment for\
vaccines and antivirals 60](#__RefHeading___Toc179875417)

[Figure 4-18: Relative preference of intervention strategies over the
likelihood of a\
pandemic (risk-neutral profile \[excludes social distancing scenarios\])
77](#__RefHeading___Toc179875418)

[Figure 4-19: Relative preference of intervention strategies including
social distancing over\
the likelihood of a pandemic, risk-neutral profile
79](#__RefHeading___Toc179875420)

[Figure 4-20: Satisfaction/regret analyses for selected pairs of
intervention strategies not\
involving quarantine 81](#__RefHeading___Toc179875421)

[Table 4-12: Satisfaction/regret statistics for selected pairs of
intervention strategies not\
involving quarantine 82](#__RefHeading___Toc179875422)

[Figure 4-21: Satisfaction/regret analysis for selected pairs of
pandemic influenza intervention strategies involving quarantine and
targeted vaccination 83](#__RefHeading___Toc179875423)

[Figure 4-22: Satisfaction/regret analysis for the choice of mass
vaccination over targeted\
vaccination strategies with partially-effective pre-pandemic vaccine
85](#__RefHeading___Toc179875424)

# List of Tables {#list-of-tables .unnumbered}

[Table 2-1: Summary of medical care in 2003
8](#__RefHeading___Toc178751238)

[Table 3-1: The 24 scenarios simulated in the sensitivity/uncertainty
analysis 14](#__RefHeading___Toc178751239)

[Table 3-2: Input variables with ranges considered in sensitivity
analysis 18](#_Ref163955682)

[Table 3-3: Output variables of interest 19](#_Ref163955864)

[Table 3-4: Probability distributions for uncertainty analysis
26](#__RefHeading___Toc178751242)

[Table 3-5: Input variables that affect transmission
28](#__RefHeading___Toc178751243)

[Table 4-1: Comparison of scenario averages
40](#__RefHeading___Toc178751244)

[Table 4-2: EpiSimS phase 2 and published simulation studies for
pandemic influenza 57](#__RefHeading___Toc178751245)

[Table 4-3: Conversion factors from trivalent seasonal flu vaccine to
monovalent pandemic\
flu vaccine capacity 62](#__RefHeading___Toc178751248)

[Table 4-4: Conversion factors from seasonal to pandemic vaccine,
assuming a 2-dose\
regimen 63](#__RefHeading___Toc178751249)

[Table 4-5: The Health and Human Services pandemic medical
countermeasure goals for\
vaccines 63](#__RefHeading___Toc178751250)

[Table 4-6: Influenza vaccine prices 67](#__RefHeading___Toc178751251)

[Table 4-7: Vaccination campaign costs 70](#__RefHeading___Toc178751252)

[Table 4-8: Antiviral drug distribution campaign costs
72](#__RefHeading___Toc178751253)

[Table 4-9: Value tradeoffs (indifference equivalencies) among decision
metrics 73](#__RefHeading___Toc178751254)

[Table 4-10: Relative preference and equivalent measures for pandemic
influenza\
intervention strategies 76](#__RefHeading___Toc178751255)

[Table 4-11: Incremental additional equivalent facilities for
vaccination strategies,\
antiviral and no antiviral 80](#__RefHeading___Toc178751256)

[Table 4-12: Satisfaction/regret statistics for selected pairs of
intervention strategies\
not involving quarantine 82](#__RefHeading___Toc178751257)

[Table 4-13: Satisfaction/regret statistics for selected pairs of
intervention strategies\
involving quarantine and targeted vaccination
84](#__RefHeading___Toc178751258)

[Table 4-14: Satisfaction/regret statistics for the choice of mass
vaccination over targeted\
vaccination strategies with partially-effective pre-pandemic vaccine
86](#__RefHeading___Toc178751259)

# List of Acronyms and Abbreviations {#list-of-acronyms-and-abbreviations .unnumbered}

  --------------- -------------------------------------------------------
  ANL             Argonne National laboratory

  ANOVA           analysis of variance

  AV              antivirals

  BEA             Bureau of Economic Analysis

  CDC             Centers for Disease Control and Prevention

  CIDRAP          Center for Infectious Disease Research and Policy

  CIPDSS          Critical Infrastructure Protection Decision Support
                  System

  CT              contact tracing

  DHS             Department of Homeland Security

  EMS             emergency medical services

  EpiSimS         Epidemiological Simulation System

  ER              emergency room

  EV              early vaccine

  FEMA            Federal Emergency Management Association

  GDP             gross domestic product

  GID             General Infectious Disease model

  GSP             gross state product

  H               hemagglutinin

  IASD            Infrastructure Analysis and Strategy Division

  LANL            Los Alamos National Laboratory

  LHS             Latin hypercube sampling

  MV              mass vaccination

  N               neuraminidase

  NISAC           National Infrastructure Simulation and Analysis Center

  NPI             nonpharmaceutical intervention

  OA              orthogonal array

  SNL             Sandia National Laboratories

  SD              social distancing

  SEIR            susceptible-exposed-infected-recovered

  SNS             Strategic National Stockpile

  TV              Targeted Vaccination

  U.S.            United States

  WHO             World Health Organization
  --------------- -------------------------------------------------------

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# Intro**d**uction

The U.S. Department of Homeland Security (DHS) tasked the National
Infrastructure Simulation and Analysis Center (NISAC)[^1] and the
Critical Infrastructure Protection Decision Support System (CIPDSS)[^2]
project[^3] to assess the potential impacts of pandemic influenza on our
nation's infrastructures and the potential effectiveness of a range of
intervention strategies. This report describes sensitivity and
uncertainty analyses conducted by the CIPDSS project in support of the
analysis.

A simulation-based uncertainty analysis uses repeated evaluations of a
model, using different combinations of key model parameters, to estimate
not only the range of potential outcomes but the probability
distribution of those outcomes. Key model parameters are identified
through a sensitivity analysis, which determines how the outcomes vary
with changes in the values of model parameters; parameters that cause
the most variation in the outcome are used in the uncertainty analysis.
Information about the distribution of likely outcomes, which needs to
account for likely outcomes as well as exposure to low-probability,
high-consequence events, is important in a risk-informed decision-making
environment.

The objectives of the analysis described in this report are to

-   Assess the potential range of consequences of pandemic influenza
    given the uncertainty about its disease characteristics (What if it
    isn't like the 1918 pandemic?)

-   Investigate multiple mitigation strategies

-   Assess the importance of vaccines, antivirals, and social distancing
    measures

-   Assess the feasibility of mitigation strategies where the
    implementation cost is difficult to quantify, such as school
    closures

-   Assess the impacts of a pandemic on the healthcare infrastructure

-   Identify high utility and robust strategies

There are many sources of uncertainty in studying the potential effects
of an influenza pandemic. These can be grouped into 4 categories:
biological, policy, sociological response, and infrastructure response.

-   Biological Uncertainty: Rapidly changing infectious diseases such as
    influenza have strong elements of uncertainty due to evolution of
    the virus as well as individual, population, and regional
    differences in how the host and viral pathogen interact in a
    pandemic. During a pandemic, the influenza virus continues to
    evolve, altering its contagiousness and virulence as it adapts to
    new hosts. The diversity of human populations with regard to immune
    function, health status, and gene function for responses to
    infectious diseases represents another element of uncertainty. Even
    as targeted vaccines are developed for a specific instance of a
    viral disease, the rapidity of viral evolution and the length of
    time to develop and deploy a vaccine imply uncertainty in vaccine
    efficacy. These aspects combine to define biological variation that
    leads to uncertainty in an analysis of a pandemic.

-   Policy Uncertainty: There are numerous intervention strategies for
    fighting infectious diseases. These include vaccinations,
    antivirals, social distancing, quarantining infectious and
    susceptible persons, and respiratory protection. Each of these
    strategies decreases either the transmission or the virulence of the
    diseases. The selection of particular combinations of interventions,
    timing of implementation, and resources available (such as vaccine
    and antiviral drug stockpiles) to implement the strategies are all
    elements of policy uncertainty. Because state health departments are
    autonomous of federal control, it is likely that their approaches to
    containing a pandemic will vary.[^4] Differences in state and local
    responses to a pandemic, such as school closures, are likely to
    exist.

-   Sociological Uncertainty: It is rarely certain what fraction of the
    affected people will actually comply with policy guidance in an
    emergency situation. Within the context of an influenza pandemic,
    individual values, religious beliefs, perception of risk, confidence
    in authorized agencies, and a myriad of other characteristics
    influence an individual's decision of whether to comply with
    recommended policy. Sociological responses to a pandemic today may
    be very different than those of almost a century ago, minimizing the
    benefit that historical examples may bring to analyzing the
    uncertainty of human behavioral responses to a pandemic. The
    uncertainties addressed in this analysis include the wide variation
    of nonpolicy actions undertaken by individuals intending to lower
    personal risk; for example, self-isolation, refusing vaccination,
    reliance on over-the-counter drugs, and so forth.

-   Infrastructure Response Uncertainty: The resilience of
    infrastructure operations in a pandemic is related to the
    availability of the workforce, flexibility in labor management and
    usage (for example, overtime), and shifts in demand for
    infrastructure services. Labor availability is dependent on the
    disease severity (illnesses) and the sociological response. This
    analysis focuses on the response of the healthcare infrastructure,
    in particular as an infrastructure facing labor shortages and
    significantly increased demand.

The analysis described in this report identifies key uncertain variables
in each of these 4 categories and assesses the variability in outcomes
due to uncertainty in the inputs. Feasibility and costs for identified
intervention strategies are also assessed. Outcomes and costs are
combined in a decision model to assess the relative utility of selected
mitigation strategies as a function of event likelihood.

Section 2 discusses the CIPDSS model and its component disease
propagation and public health models. Section 3 describes the approach
to sensitivity and uncertainty analysis. Section 4 presents the
simulation results and analysis. Section 5 presents the summary and
conclusions of the study. Appendix A contains intervention strategy
frequency histograms. Appendix B contains intervention strategy
consequence statistics. Appendix C is an outline of nonpharmaceutical
mitigation measures for pandemic influenza. Appendix D contains a
summary of cost estimates.

# Model Overview

The model used to perform this analysis is a subset of the suite of
CIPDSS models described elsewhere.[^5] The set of models used represents
infectious disease spread and intervention, population, travel, labor,
and infrastructure operations as shown in Figure 2-1. The model is on a
national scale with the country partitioned into 10 multi-state regions.
Important submodels include a general infectious disease (GID) model, a
population model used to track inter-region travel and labor
availability, the public health model, and an economic impact estimation
model.

The population and infectious disease models interact to introduce the
pandemic flu strain into the population and spread the disease across
the nation. The population developing symptoms and needing treatment
place a demand on the public health sector. The use of various
intervention strategies, such as vaccination, affects the spread of the
disease and alters the impacts on the public health system and on the
overall population. The population model also tracks which members of
the ill population are also members of the workforce and estimates
absenteeism due to illness, care for ill family members, or other
reasons. The economic impact model tracks the impact of absenteeism and
mortality on the economy.

![](media/image2.wmf){width="5.5in" height="3.6791666666666667in"}

Note: The more critical models are highlighted and described in the
following paragraphs.

[]{#__RefHeading___Toc179875372 .anchor}Figure 2-1: Major elements of
the Critical Infrastructure Protection Decision Support System (CIPDSS)
models used in the pandemic influenza impacts analysis

## Infectious Disease Model

The infectious disease model is a modified
susceptible-exposed-infected-recovered (SEIR) model[^6] using an
extended set of disease stages; demographic groupings; an integrated
model for vaccination, antiviral prophylaxis and treatment, quarantine,
and isolation; and demographic and stage-dependent behavior. As a
variant on the SEIR model paradigm, this implementation represents the
populations as homogeneous and well mixed, with exponentially
distributed residence times in each stage. However, the use of
additional stages and demographic groupings adds heterogeneity where it
is useful in capturing key differences between subpopulations for
disease spread and response.

### Disease Stages

The disease stages are represented generically so that the model can be
used for a number of infectious agents by adjusting the input parameters
appropriately. This is illustrated in Figure 2-2.

[]{#__RefHeading___Toc179875374 .anchor}Figure 2-2: Representation of
the multi-stage susceptible-exposed-infected-recovered (SEIR) general
infectious disease model in the Critical Infrastructure Protection
Decision Support System (CIPDSS)

### Demographic Groupings

The demographic groups used in the model are as follows:

-   Infants (ages 0--4)

-   Youth (ages 5--18)

-   Young adults (ages 19--44)

-   Older adults (ages 45--64)

-   Elderly (ages 65+)

-   Responders

Responders are health and emergency-services workers, and they are
assumed to be taken from the two adult groups (young adults and older
adults). The populations of young adults and older adults are reduced by
the number of responders in each group. Responders are treated
separately in the model to allow modeling of different levels of disease
exposure compared to the general population and to model alternate
policies regarding access to vaccines, antivirals, and other
prophylactic measures.

The basic reproductive number (*R~0~*) is the average number of people
infected by a typically infectious individual in an otherwise
susceptible population. If *R~0\ ~*is greater than 1, the disease has
the potential to spread. If it is less than 1, the disease will die out
after only a few generations. The parameters that affect *R~0~* include
the ease of transmission of a disease and the contact rates among the
populations. The CIPDSS infectious disease model can either use *R~0~*
as an input into the model or it can calculate it as an output of the
model based on input contact rates by demographic group. For this study,
*R~0~* was used as an input to the model.

### Vaccination, Antiviral Prophylaxis and Treatment, Quarantine, and Isolation

Government response in the model in the form of quarantine and
vaccination programs is initiated after recognition of the first cases
in the public health system or by direct detection if available. Once
the disease is detected, appropriate mitigation measures are employed.
The model represents the mitigation strategies under a variety of policy
assumptions. Mitigation options include vaccines (delivered via targeted
vaccination, mass vaccination, or a combination), antivirals (as
prophylaxis prior to infection or treatment after infection), and
isolation and quarantine. Vaccination can be biased toward particular
subpopulations to model priority vaccinations of children or healthcare
personnel. Allowances can be made for segments of the population who
either refuse vaccination or cannot tolerate vaccination. The model can
handle 2 separate vaccines during a simulation if desired: a
pre-pandemic strain that is available early but is assumed less
effective because it wasn't designed for the particular strain being
dealt with and a subsequent targeted (specifically designed for the
disease strain), more effective, vaccine available after a delay for
production and distribution. This is a particularly relevant feature for
a potential pandemic influenza outbreak that, in order to be human
transmissible, will be different from the infectious agent for which
current vaccines are designed.

Schools are not included in the generic infectious disease model, but
school closing can be modeled by including age-group dependence for
*R*~0~ or contact rates, thus allowing age-dependent control of the
transmission and infection of school-age children. The model also
responds to investments in better hospital care, isolation, and
antiviral treatments, which can affect fatality and recovery rates in
the population. The model keeps track of the state of the population in
terms of immunity, health status, unavailability (sick and/or in
quarantine), and fatalities. Unavailability and fatalities are passed to
the population and infrastructure models, where their effects can then
feed back into the infection model. Examples of this behavior include
sickness and fatalities leading to reductions in healthcare staff, which
in turn can raise fatality rates in the infection model due to poorer
and less timely care.

## Regional Population Model

The regional population model is arrayed to represent the spread of
disease in each of 10 regions. For this analysis, the regions are chosen
to be the 10 Federal Emergency Management Agency (FEMA) regions (Figure
2-3). The population model keeps track of the number of people in
different health statuses for each region. It drives the visitation
rates for the public health model in 3 categories: normal afflictions,
pandemic influenza afflictions, and "worried well" afflictions. (Worried
well people are those who think they might have the pandemic flu, but
who do not.) The population model also outputs the fractional labor
availability for each infrastructure category. In addition to normal,
pandemic influenza, and worried well people, it also takes into account
the number of quarantined and self-isolating people. Parents of infected
children that stay home from work to care for them can be tracked by the
model.

A submodel tracks emergency responders separately, so the labor
availability for emergency services can be different than for other
infrastructures. All other infrastructures have essentially the same
level and time history for labor availability.

![](media/image3.jpeg){width="4.416666666666667in"
height="2.9298611111111112in"}

Note: Guam, Puerto Rico, and the Virgin Islands are not included in this
analysis.

[]{#__RefHeading___Toc179875375 .anchor}Figure 2-3: Division of the U.S.
into 10 Federal\
Emergency Management Agency (FEMA) regions

The regional population model tracks how many contagious people are
currently visiting another region. The fraction of each region's
contagious people who are currently traveling in another region is input
into the epidemiological submodel, which spreads the disease across
regions. The pandemic influenza simulations did not have a single
outbreak point, but the contagious travel submodel ensures that the
pandemic continues to be spread across regions as a national outbreak,
rather than having 10 separately modeled regional epidemics. Travel
restrictions that lower travel rates can also be implemented with this
model.

## Public Health Model 

The public health model represents treatment of patients by physicians'
offices and clinics, emergency medical services (EMS), emergency rooms
(ERs), and hospitals. Regional variations are represented by dividing
the U.S. into large regions. As with the population model, this model
uses the 10 FEMA regions for the regional breakdown. Within each region,
average values are used for patient-treatment capacities, number of
hospital beds, and so forth. The 3 types of patients tracked in the
model are "normal" patients (numbers based on historical data), patients
who have pandemic flu (denoted "special" within the model), and worried
well patients (people who think they might have the pandemic flu, but
who do not). If the numbers of patients increase substantially over
normal conditions, backlogs and long waits result, causing a reduction
in the quality of care. Also, if significant numbers of healthcare
workers are sick or in isolation, the capacity to treat patients is
reduced, further exacerbating the overloading of the healthcare system.
In a situation like this, it is possible that additional healthcare
workers would be brought in (for example, healthcare workers from other
locations and the armed services, retirees, and volunteers) to relieve
the overloading, but the ability to add healthcare workers is not
included in the model at present. It is worth noting, however, that the
usual methods of bringing in additional healthcare workers might not
work well in a pandemic because the entire country is affected.

### Normal Care

The ongoing rates of medical treatment under normal conditions are based
on data for year 2003 from the "National Hospital Ambulatory Medical
Care Survey: 2003 Emergency Department Summary";[^7] *Health, United
States, 2005*;[^8] and the "National Hospital Discharge Survey: 2003
Annual Summary."[^9] Key data are summarized in Table 2-1. Visit rates
are given per year in the table, but the model uses units of hours.
Weekdays and weekends are not differentiated in the model, so annual
totals were divided by the number of hours in a year to get the hourly
values used by the model. The numbers listed in the table are all
national averages. The last 2 numbers (hospital beds and hospital
occupancy rate) are available for each state in *Health, United States,
2005*, and were averaged over FEMA regions for input to the model. The
hospital occupancy rate is not a model input parameter, being calculated
within the model, but appropriate occupancy rates for the FEMA regions
were obtained by specifying the number of staffed beds per person and
the average length of stay for each FEMA region.

[]{#__RefHeading___Toc178751238 .anchor}Table 2-1: Summary of medical
care in 2003

  --------------------------------------------- -------------------------
  **Quantity**                                  **Value**

  Rate of visits to physicians' offices and     3.5 visits per year per
  hospital outpatient departments               person

  Rate of visits to hospital emergency          0.40 visits per year per
  departments                                   person

  Rate of admissions to community hospitals     0.12 visits per year per
                                                person

  Fraction of emergency department patients     14%
  arriving by ambulance                         

  Fraction of emergency department patients     14%
  requiring hospital admission                  

  Fraction of emergency department visits dead  0.3%
  on arrival or dying in the emergency          
  department                                    

  Fraction of hospital inpatients dying         2.3%

  Average time spent in emergency department    3.2 hours

  Average hospital length of stay               5.7 days (rounded to 135
                                                hours)

  Number of staffed community hospital beds     0.0028 beds per person

  Occupancy rate for community hospital beds    66%
  --------------------------------------------- -------------------------

A flow diagram illustrating the major patient flows for "normal"
emergency care in the model is shown in Figure 2-4. Several of the
fractions are unknown, but they have been chosen to be consistent with
the data in Table 2-1. For example, a 75-percent/25-percent split
between emergency patients going directly to an ER versus being treated
in the field by EMS, coupled with the assumption that half of the
patients treated by EMS are then taken to ER by ambulance, results in
approximately 14 percent of patients arriving at the ER by ambulance, as
shown in Table 2-1. A total mortality rate of 0.3 percent is derived by
combining the mortality rates of the EMS and ER patients.

[]{#_Ref163955301 .anchor}Notes: EMS = emergency medical services; ER =
emergency room

![](media/image4.wmf){width="6.325694444444444in"
height="2.2090277777777776in"}Figure 2-4: Flow diagram for normal
emergency care in the public health model

The demand for "normal" care continues even during an influenza
pandemic; therefore, the CIPDSS team made a number of adjustments in the
model:

-   When there is a long waiting time for visits, up to 20 percent of
    the patients who would have gone to a physician's office are assumed
    to go instead to a hospital ER because of an urgent problem; up to
    40 percent of the patients who would have gone to a physician's
    office are assumed to cancel if there is a long waiting time for
    visits

-   If the waiting time for EMS response gets too long, more people will
    go directly to the ER rather than wait

-   When hospital beds are full, nonemergency patients are not admitted
    to hospitals, but the patients who would have been admitted wait and
    are admitted later when the hospital crowding subsides

-   When demand exceeds normal capacity for treatment, the time it takes
    to treat a patient goes up and quality of care goes down, which can
    cause an increase in the mortality rates

### Treatment of Pandemic Flu Patients

People becoming ill with the pandemic flu are added to the normal load
on the healthcare system. The rates at which people get sick and recover
or die from the pandemic flu are calculated by the epidemiological
model, which was discussed above. Figure 2-5 illustrates the entry of
pandemic flu patients into the healthcare system. The fractional splits
in the model are variable, depending on the average case fatality rate
and the degree of crowding in the healthcare system. A higher case
fatality rate indicates a more severe variant of the disease and so the
fractions of patients seeking healthcare, requiring emergency care, and
requiring admission to hospital are higher.

Overcrowding in the healthcare system can have several effects,
including more people self-treating because waiting times are so long,
more people going directly to an ER rather than waiting for EMS, and if
wait time for hospital admission is long enough, people starting to
recover before getting into the hospital. Further, as mentioned above,
overcrowding can cause quality of care to decline and mortality rates to
increase. In Figure 2-5, hospital includes temporary alternative-care
facilities that are assumed to be set up when hospitals are full. The
calculated healthcare cost includes a reduced cost per patient-day. For
these simulations, CIPDSS assumed that there are sufficient alternative
beds (for example, in gyms, convention centers, or elsewhere). CIPDSS
also assumed that some ![](media/image5.wmf){width="6.325694444444444in"
height="2.1277777777777778in"}patients are sent home for care because
there is no room in hospitals or temporary facilities.

[]{#_Ref163955345 .anchor}Notes: EMS = emergency medical services; ER =
emergency room

Figure 2-5: Flow diagram for pandemic flu patients in the public health
model

CIPDSS assumed the time spent in an ER would be the same for pandemic
patients as for normal patients, but the time in hospital would be
longer. CIPDSS also assumed the average hospital stay for pandemic flu
patients would be the same as the flu recovery time (the amount of time
spent in the last disease stage in Figure 2-2).

### Worried Well

The number of worried well patients is highly uncertain, but it could be
large enough to cause a significant additional load on physicians'
offices and ERs. For the analysis discussed here, CIPDSS assumed the
number of worried well patients would be 20 percent larger than the
number of people who have pandemic flu symptoms (that is, the number of
worried well patients would be 1.2 times the number of pandemic flu
patients). The worried well rate has been much higher for many past
incidents; for example, there were 5 times as many worried well patients
as actual patients afflicted after the Sarin attack in Tokyo.[^10]
However, because a large fraction of the population is assumed to become
ill in the influenza pandemic if there is no mitigation, the worried
well multiplier could not be too much higher. If mitigation measures
successfully reduce the fraction of population becoming ill, then there
would be less reason for the fear response that leads to people becoming
worried well. Furthermore, while worried well patients can have an
important impact on crowding in parts of the healthcare system, the
uncertainty in the number of worried well patients has less impact than
many other uncertainties on the quantities of greatest concern (such as
number of deaths and economic impact). Therefore, for this analysis, the
worried well multiplier was fixed at 1.2.

The flow of worried well patients through the healthcare system is
simpler than for normal or pandemic-flu patients because, by definition,
worried well patients are not seriously ill, but are only afraid they
might be. Therefore, CIPDSS assumed that not one person is treated by an
EMS, admitted to the hospital, or dies. This simplified patient flow is
illustrated in Figure 2-6.

![](media/image6.wmf){width="6.488194444444445in"
height="1.6513888888888888in"}The split of patients seeking care at a
physician's office versus an ER is variable, depending on the average
case fatality rate. CIPDSS assumed that if the disease is more severe
(as indicated by a higher case fatality rate), then worried well
patients would be more likely to seek emergency care instead of going to
a physician's office. Also, if the waiting time for an appointment at a
physician's office is too long, some patients may end up self-treating
and bypassing the healthcare system as they start to recover from their
flu-like symptoms.

Note: ER = emergency room

[]{#__RefHeading___Toc179875378 .anchor}Figure 2-6: Flow diagram for
worried well patients in the public health model

## Economic Impacts Model 

This model calculates a dollar value for economic impacts to estimate
the magnitude of a possible pandemic influenza. The main number in this
estimate is the lost gross domestic product (GDP). This number is an
estimate of direct GDP losses. "The National Infrastructure Impacts of
Pandemic Influenza: Phase 1 Summary Report"[^11] included a more
extensive estimate of direct and indirect losses.

Lost GDP is a measure of lost output.[^12] The input values are directly
derived from the 2002 GDP data for state and industry, which are
available from the Bureau of Economic Analysis (BEA).[^13] Specifically,
the model calculates the lost GDP by multiplying the total value of the
GDP per day, by state and industry, by the fraction of workers in that
state and industry not working on any given day. The model derives the
fraction of unavailable workers from population estimates of sick, dead,
and self-isolation workers. This unavailable-worker fraction directly
determines the derived economic losses.

The summed GDP for all states is equal to the total GDP for the nation.
For this reason, the lost-GDP calculation can be considered to be an
estimate for the losses to the U.S. economy over the time studied, in
this case, a year. However, the following caveats apply:

-   Although the events may cause structural changes to the economy,
    those changes are not evaluated in the economics model or in the
    rest of the CIPDSS infrastructure models (most of these changes will
    take more than a year to emerge)

-   The ability to change business processes and, for example,
    substitute inputs, is limited during the scenario and generally is
    not modeled; this ability may decrease the actual GDP losses

This model estimates the economic impacts from reductions in output due
to reduced workforce participation attributable to the pandemic. One
calculation not included in these results is the agricultural costs due
to losses in stocks of poultry from an avian form of the influenza. This
study did not calculate potential losses in this or other areas,
although they are high, because the human costs of a pandemic influenza
would far outweigh agricultural losses.

# Sensitivity and Uncertainty Analysis

Uncertainty analysis is estimating the distributions of outcome metrics
of interest so that probability estimates can be made on the
consequences of the simulated pandemic. In this context, sensitivity
analysis is identifying which input variables cause the most variation
in the outputs. The relative sensitivity of an output to an input
variable is often given as a ratio of variance measures, referred to as
the R^2^ metric later in this section.

## Approach

The analysis team selected an initial set of uncertain input variables
to study, including biological variation, policy options, and
sociological response differences, based on the team's experience. An
initial sensitivity analysis identified key input variables that are
important in determining the variation in the outcomes. The team then
used the resultant variables as the basis for a subsequent uncertainty
analysis to characterize the uncertainty in the outcomes.

### Experimental Design

In the design and modeling of complex systems, designed experiments are
frequently the only practical approach to obtaining a solution.
Typically, a simulation model of system performance is constructed based
on knowledge of how the system operates, and sampling via experiments
may be employed to estimate the number of possible outcomes. If the
simulation model is computationally expensive, then the optimization of
the possible outcomes may instead rely on a sampling methodology to
adequately cover all the possible ranges of inputs that contain
uncertainty or variability.

This uncertainty analysis employed an experimental design approach to
pandemic scenario simulations to obtain information for statistical
estimates and correlation in the most efficient manner possible. The
goal of the experiment design was to improve the understanding of the
relationship between important factors that drive a pandemic and the
response to a pandemic. The team based the design on a fractional
factorial experiment, which allows an experiment to be conducted with
only a fraction of all the possible experimental combinations of
parameter values. The team uses orthogonal arrays to aid in the design
of an experiment. The orthogonal array specifies the combinations of
inputs for each run of the simulation experiment. This approach to
designing and conducting an experiment to determine the effect of design
factors (parameters) and noise factors give this statistical strength
for both the uncertainty analysis and sensitivity analysis.

The statistical method of Latin hypercube sampling (LHS) was developed
to generate a distribution of plausible collections of parameter values
from a multidimensional distribution. The sampling method is often
applied in uncertainty analysis. The LHS technique was first described
by McKay[^14] and it was further elaborated by Ronald Iman and
others.[^15] In the context of statistical sampling, a square grid
containing sample positions is a "[Latin
square](http://en.wikipedia.org/wiki/wiki/Latin_square)" only if there
is only 1 sample in each row and each column. A Latin hypercube is the
generalization of this concept to an arbitrary number of dimensions,
whereby each sample is the only one in each axis-aligned hyperplane
containing that sample.

The model experimental design applied an orthogonal array (OA)-based LHS
plan (strength 5), using 512 runs and varying 20 input variables based
on their input distributions, to 24 distinct scenarios (see Table 3-1)
for a total of 12,288 simulation runs. Each scenario involved a unique
combination of a vaccination strategy, social distancing, contact
tracing, and antiviral prophylaxis.

[]{#__RefHeading___Toc178751239 .anchor}Table 3-1: The 24 scenarios
simulated in the sensitivity/uncertainty analysis

+-----------------------------------------------------------------------+
| **Vaccination Strategies:**                                           |
|                                                                       |
| 1.  No vaccination or contact tracing                                 |
|                                                                       |
| 2.  Contact tracing without vaccination                               |
|                                                                       |
| 3.  Mass vaccination only without early vaccine                       |
|                                                                       |
| 4.  Mass vaccination and early vaccine                                |
|                                                                       |
| 5.  Targeted vaccination followed by mass and early vaccination       |
|                                                                       |
| 6.  Targeted vaccination and early vaccination                        |
|                                                                       |
| **Scenario Set for each Vaccination Strategy:**                       |
|                                                                       |
| 1.  No social distancing, no antivirals                               |
|                                                                       |
| 2.  No social distancing, antivirals                                  |
|                                                                       |
| 3.  Social distancing, antivirals                                     |
|                                                                       |
| 4.  Social distancing, no antivirals                                  |
+-----------------------------------------------------------------------+

## Mitigation Scenarios

CIPDSS developed mitigation responses based on potential interventions
for pandemic influenza. The team developed 24 distinct mitigation
scenarios to evaluate, based upon combinations of 6 vaccination
strategies and 4 scenario sets of social distancing and antiviral usage
(Table 3-1). These included 2 baseline scenarios with minimal mitigation
measures. The vaccination strategies included mass and targeted
vaccination, contact tracing, and the availability of a
partially-effective and immediately available vaccine (early vaccine).
The following sections include discussions on the characteristics of the
vaccination scenarios and scenario sets.

### Contact Tracing

Contact tracing is identifying and diagnosing persons who may have come
into contact with an infected person. Public health workers identify
symptomatics, interview them for their contacts, and quarantine them and
their contacts for the time they are infectious so they can\'t infect
others. The fraction of actual contacts identified and traced varied in
the study, ranging from 0.025 to 0.5. These lower values are appropriate
because the disease is assumed to be contagious prior to the appearance
of symptoms. Reviewers[^16] of this work have advised the authors that
the upper limit is far too high and suggested that an upper bound of 0.1
is more reasonable, with average effectiveness less than 0.05. The
distribution governing contact tracing effectiveness was changed to have
a range of 0.0 to 0.1 with a mean of 0.333 and the simulation output was
updated. The number of contacts per afflicted case varied between 5 and
40 people.

### Social Distancing (Self-Quarantine) 

There are many ways to achieve social distancing including measures to
limit person-to-person interactions such as canceling events involving
large gatherings and closing buildings or schools. These restrictions
are sometimes called "focused measures to increase social distance" and
decrease the rate of contact between individuals, thereby slowing the
spread of the disease. Closing office buildings, stores, schools, and
public transportation systems is a feasible community containment
measure that could be employed during a pandemic. Because CIPDSS does
not have the resolution to closely model specific social distancing
measures, CIPDSS approximated social distancing by varying the
self-quarantine behavior in the models. People self-quarantine by
voluntarily limiting their contacts with others, thus achieving social
distancing. Self-quarantined individuals may remain home for a variety
of reasons, including school closures, child or afflicted persons care,
fear, telecommuting, or extended leave policy, but the reason for their
self-quarantine is not explicitly represented in CIPDSS. This differs
from analyses such as those from the epidemiological simulation system
(EpiSimS) where some of these different methods for achieving social
distance are explicitly represented.

The CIPDSS model for social distancing begins by estimating what is
called the nominal rate of self-quarantine, which is the rate if this
quarantine behavior was unaffected by competing model behaviors such as
getting sick or being contact traced. This nominal rate is based on a
multiplier of the rate that people are entering the prodromal stage.
This multiplier can be varied by demographic group and is delayed from
the prodromal rate by a user-defined reaction time delay. Thus, the
self-quarantine behavior is linked to the observed progression of the
disease; that is, as more people get sick, the tendency for self
quarantine increases. Time reaction delay allows strengthening or
weakening of coupling between the disease progression and social
distancing.

This nominal rate of self-quarantine won't be the actual rate, because
the model has to account for the fact that people would be getting sick
and not self-quarantining. CIPDSS also wants to allow for some user
control to cap the self-quarantine behavior based on data or judgment.
Therefore, the model calculates an "otherwise occupied rate" that
accounts for vaccinations, contact tracing (with quarantine), and people
entering prodromal. The model uses an "otherwise-occupied" rate to
calculate a fraction of the population that is otherwise occupied. Only
the population not otherwise occupied is available to be
self-quarantined. Thus, the model estimates the actual self-quarantine
rate as the nominal self-quarantine rate (based on prodromal rate) times
a power law damping factor that accounts for the fraction of the
population available for self-quarantine and the user-defined maximum
fraction self-quarantined. CIPDSS incorporated this maximum relative
tendency of the different demographic groups to self-isolate during a
crisis to represent demographic differences in quarantine behavior. For
example, infants and the elderly will be much more likely to
self-isolate than working-age people who need to continue work-related
tasks. In addition, school-age children may stay home at a higher rate
due to school closures. So as the nominal rate approaches either the
fraction available for self-quarantine or the desired maximum
self-quarantine rate, it is damped down smoothly so that it never
exceeds these values.

The infectious disease model applies this self-quarantine rate to the
unexposed population. A user-defined multiplier determines the rate
people leave self-quarantine on the characteristic time of the disease
(the time from infection to recovery or death). The result is that early
in the disease progression, the self-quarantine rate looks like a
straight multiplier on the sick rate; however, as things progress, it
becomes altered below this nominal rate. In many cases, the
self-quarantine rate gets damped down much lower than this nominal rate
as the population gets depleted and the maximum behavior is reached.

The reaction delay is an important parameter that regulates how closely
tied the population's self-quarantine behavior is to the progression of
the disease. The smaller the delay, the closer it is tied to disease
progression and the more oscillations it creates in the self-quarantine
and disease progression behavior. People lower their self-quarantine
behavior as soon as the disease starts to let up, allowing for the
disease to grow again, and so on. The longer the delay, the more these
behaviors are decoupled and the oscillations are damped out.

The model calculates a number of metrics that provide useful
representations of the overall social distancing behavior in a scenario,
including the cumulative number of persons self-quarantined, the
cumulative number of person-days spent in self-quarantine, a
population-weighted overall fraction self-quarantined (taking into
account the differing behaviors of the demographic groups), the peak
self-quarantine fraction, and the time during the scenario when the peak
self-quarantine behavior is achieved.

### Targeted Versus Mass Vaccination

A targeted vaccination campaign is limited to contacts of infected
people while a mass vaccination program vaccinates all susceptible
people until vaccine stockpiles are used or the program catches up to
the vaccine production. A fraction of the population may not be able to
receive vaccines due to allergies, immunodeficiency diseases, or other
reasons. Targeted vaccination includes contact tracing and quarantine as
described above.

#### Partially Effective Vaccine

CIPDSS considered the use of a partially effective pre-pandemic vaccine
that is stockpiled and available early in the pandemic as one of the
vaccination strategy options. Accordingly, this mitigation uses
stockpiled vaccine that may be designed based on a close relative strain
of the pandemic influenza strain. The relative efficacy of the partially
effective vaccine varied in the study between 10 percent and 70 percent.
The size of the stockpile varied between 20 million and 120 million
doses.

#### Fully Effective Vaccine

Vaccine providers typically take several months to develop a
strain-specific vaccine. In this analysis, time to production of the
vaccine varied from 1.5 to 6 months. After initial introduction, the
team assumed that providers would continue producing and distributing
the vaccine, providing a stable production rate of approximately 13
million doses per day for mass vaccination.

### Antiviral Treatment

CIPDSS assumed that antivirals would be available in the following 2
scenario sets: social distancing, antivirals; and no social distancing,
antivirals. The stockpile of available courses varied between the
current 21 million and the projected 80 million courses. An optional
capability to produce additional antivirals with a ramp-up to a
designated production rate is also available. One course for treatment
is 5 days, or 10 days for prophylactic measures. 

## Variable Ranges

### Selection of Uncertain Parameters

For this study, CIPDSS identified 20 input variables as likely
influential variables on the estimated consequences. The team reviewed
these selected variables and associated reasonable ranges with each
input (Table 3-2) based on the literature and the ranges of similar
values defined in mitigations used by EpiSimS. Each input varied within
the specified ranges for every simulation run. Table 3-3 lists the
output variables of interest. Input variables that are together in a box
were varied together (that is, with 100-percent correlation). The team
describes the significance of some of the inputs in a subsequent section
of this report.

[]{#_Ref163955682 .anchor}Table 3-2: Input variables with ranges
considered in sensitivity analysis

  ------------------------------------- ---------- ---------- ---------------
  **Variable**                          **Low      **High     **Units**
                                        Range**    Range**    

  **Antivirals/Vaccines**                                     

  Initial antiviral availability        21M        80M        Courses

  Antiviral standard production rate    0          120M       Courses/year

  Fraction antivirals applied to        0.10       0.90       Fraction
  prophylaxis                                                 

  Death rate reduction from antivirals  0.20       0.70       Fraction

  Initial vaccine stockpile (partially  21M        121M       Doses
  effective)                                                  

  Vaccine effectiveness (partially      0.1        0.7        Fraction
  effective)                                                  

  Vaccine effectiveness (fully          0.4        0.8        Fraction
  effective)                                                  

  Time before vaccine gives immunity    168        1152       Hours
  (both types)                                                

  **Quarantine and Isolation**                                

  Self quarantine sick rate modifier    2          10         Dimensionless

  **Contact Tracing**                                         

  Fraction contact effectiveness        0.025      0.5        Fraction

  **Transmission**                                            

  Average contacts per case             5          40         People

  Reproductive number (all ages)        1.1        4.5        Dimensionless

  Fraction of transmission prior to     0.1        0.5        Fraction
  clear symptoms                                              

  Relative contagion of asymptomatic    0.1        0.8        Fraction

  Additional fraction of asymptomatic   0.05       0.8        Fraction

  **Case Fatality Rates**                                     

  Nominal fatality rate (all population 0.005      0.15       Fraction
  groups)                                                     

  **Disease Stage Time Periods**                              

  Time to incubate                      12         96         Hours

  Duration of prodromal                 12         96         Hours
  (infectious/asymptomatic)                                   

  Duration of early symptoms            24         120        Hours

  Time to recover                       120        552        Hours

  **Public Health Factors**                                   

  Maximum mortality enhancement         1          2          Factor
  multiplier                                                  

  Base fraction of afflicted seeking    0.4        0.9        Fraction
  healthcare                                                  

  Detection threshold                   1          512        People
  ------------------------------------- ---------- ---------- ---------------

[]{#_Ref163955864 .anchor}Note: M = million

Table 3-3: Output variables of interest

  -----------------------------------------------------------------------
  **Output Response Variable** **Description**
  ---------------------------- ------------------------------------------
  Total cumulative deaths      Cumulative deaths caused by event

  Total event healthcare cost  Total cost in the healthcare sector

  Total fraction workers       This is the fraction (weighted average) of
  unavailable                  workers unavailable across all regions

  Total scenario affliction    Total rate at which people are becoming
  rate                         afflicted by the scenario (persons per
                               hour)

  Maximum outbreak duration    The maximum over all regions of the
                               outbreak duration; based on a threshold of
                               25 people remaining in the exposed stage
                               that determines when the outbreak is
                               considered over

  Overall total cases          Number of pandemic cases, irrespective of
                               treatment

  Total attack rate            The total attack rate (ratio of cumulative
                               cases to population) in percentage terms

  Total cumulative deaths      Total fatalities

  Total cumulative symptomatic Total symptomatic cases

  Total number hospitalized    Hospitalizations

  Total patients leaving       Number of patients who are supposed to be
  without being admitted       admitted to the hospital, but who end up
                               leaving because waiting times are too long

  Total patients treated in    Number of flu patients who need to be
  alternative beds             hospitalized, but who are sent to
                               temporary facilities because of
                               insufficient room in hospitals

  National lost gross domestic The dollar amount of GDP activity lost
  product (GDP)                during the study period

  National baseline GDP        The dollar amount of GDP activity that
                               would have occurred during the time period
                               with no pandemic

  Cumulative cases             Cases by age groups
  demographically              

  Cumulative deaths            Deaths by age groups
  demographically              

  Maximum peak fraction        The maximum over all regions of the peak
  self-quarantined             fraction self-quarantined in each
                               demographic group

  All regions cumulative       Total days for self-quarantined population
  self-quarantine person-days  

  Overall peak fraction        The weighted average over population of
  self-quarantined             the peak fraction self-quarantined

  Total cumulative             The cumulative number of person-days that
  self-quarantine person-days  people have been self-quarantined, summed
                               over all regions and all demographic
                               groups

  Total vaccination cost       The total cost of administering
                               vaccinations

  Total antiviral cost         The total cost of treating afflicted with
                               antivirals
  -----------------------------------------------------------------------

##  Biological Uncertainty Factors

### Reproductive Number 

Generating reliable estimates of the reproductive number (*R~0~*) for
influenza is difficult from analyses of past outbreaks because of
confounding influences of etiological properties of the infectious agent
and population-level effects. An example of the former is the profound
antigenic diversity of influenza and the consequent impact of population
immunity on transmission. Population age structure, contacts, and
density are factors that need to be incorporated. The doubling time for
a pandemic curve is a function of the reproductive number. The magnitude
of *R~0~* also determines the intensity of control measures needed to
bring the pandemic to a halt.

Estimates of *R~0~* for pandemic influenza in the literature range from
1.68 to 20.[^17] The lower bounds typically correlate with longer serial
intervals, or generation times. The study by Murray[^18] uses a simple
pandemic model to fit an outbreak in a boarding school for boys. This
study implied an *R~0~* value of 3.8, with an implied infectious period
of approximately 2.3 days, which is low for influenza. Behavioral
factors could be the origin here, as the boys were sent to the infirmary
when they showed symptoms, corresponding to a large reduction of contact
rate with susceptible people and leading to a low estimate of the
infectious period. Spicer[^19] fitted a daily time-series model to
weekly deaths from influenza and influenza pneumonia in England and
Wales for the 1958--1973 pandemic seasons. Multiplying his result, which
was estimated on a per-day basis, by the length of the infectious period
leads to a large value for *R~0~* in the range of 11.5--22.7. Mills and
others fitted the pneumonia and influenza death pandemic curves from 45
U.S. cities during the 1918 pandemic to a deterministic SEIR, where an
*R~0~* between 2 and 3 was estimated.[^20] Mills and others estimated
the median value for these data to be 2, with an interquartile[^21]
range of 1.7--2.3 and an absolute maximum of 6.3. Over the interquartile
range, Mills and others did not significantly correlate *R~0~* with
latitude, longitude, population size, population density, age
distribution, or sex distribution. The extreme value, *R~0~* = 6,
correlated weakly with population density (*p*=0.001). If the Mills and
others analysis is accurate, it indicates that the 1918 pandemic A/H1N1
strain did not have an inherently higher transmission rate than other
influenza subtypes that cause yearly flu epidemics. By comparison, the
range of *R~0~* used in this study, 1.1 to 4.5, is fairly conservative.

### Nominal Fatality Rate

Nearly 50 percent of the influenza-related deaths during the 1918--1919
pandemic occurred among young (20 to 40 years old) and previously
healthy adults,[^22] which is 10 times higher than the influenza
pandemics of 1957 and 1968. This led to more than 500,000 excess deaths
in the U.S. and 20 million excess deaths worldwide.[^23] During the
1957--1958 A/H2N2 pandemic, 36 percent of all excess influenza-related
deaths occurred in people under 65 years old. During the 1968--1969
A/H3N2 pandemic, the excess mortality rate for people under 65 years old
was 48 percent. By way of comparison, between 1972 and 1992, annual
influenza epidemics caused a mortality of 21,000 excess deaths per year.
In the most recent seasons, 80 to 90 percent of these deaths occurred in
the people over 65 years old.[^24]

While the accepted case mortality rate for the U.S. in the 1918 pandemic
is generally agreed to be\
2--2.5 percent, the global rate ranged as high as 33 percent. The Board
of Global Health reported that for the 1918 pandemic:[^25]

> The case mortality rate varied widely. An overall figure is impossible
> to obtain, or even estimate reliably, because no solid information
> about total cases exists. In U.S. Army camps where reasonably reliable
> statistics were kept, case mortality often exceeded 5 percent, and in
> some circumstances exceeded 10 percent. In the British Army in India,
> case mortality for white troops was 9.6 percent, for Indian troops
> 21.9 percent. In isolated human populations, the virus killed at even
> higher rates. In the Fiji islands, it killed 14 percent of the entire
> population in 16 days. In Labrador and Alaska, it killed at least
> one-third of the entire native population (Jordan, 1927; Rice, 1988).

To include this wide range of possible case mortality rates, this study
distributed the nominal fatality rate according to a beta (2,38)
distribution, which has 99 percent of its values in the interval \[0.00,
0.16\] and has a mean value of 0.05, obviously higher than the 1918
pandemic. This distribution is meant to characterize severe pandemics.
The Phase I study concluded that a 1918-like pandemic would not cause
significant disruption of any infrastructure other than public health.
CIPDSS used this distribution to explore cases that would be more severe
than the 1918 pandemic and gauge the consequent effects on
infrastructures

The model combined the nominal fatality rates for the other demographic
groups with the same range, but independent of the value for infants and
the elderly. This range allows for a very high influenza case mortality
rate in comparison to the 1918 pandemic, but far lower than the observed
H5N1 case mortality rate. The model mapped simulation results to the
pandemic severity indices recommended by the U.S. Centers for Disease
Control and Prevention's (CDC's) "Pre-Pandemic Planning Guidance."[^26]

### Asymptomatic Infection

There are 2 main aspects of asymptomatic infections in a population with
circulating influenza: the total fraction of asymptomatic people and how
transmissible the infection is to other persons from an asymptomatic
person. Considerable variation exists in these two aspects of
asymptomatic infections for both seasonal and pandemic influenza.
Indeed, since 1997, the clinical spectrum of H5N1 infection ranges from
asymptomatic infection to fatal,[^27] and authors are currently debating
on the real numbers of cases of H5N1 in humans that have been
asymptomatic.[^28] In a report to the World Bank, Zelicoff argues that
data show that hundreds of millions of people have antibodies to H5,
strongly implying infection with an H5-containing strain of influenza.
Thus, the overwhelming majority of these infected humans did not develop
disease or even mild symptoms. The implication of this argument would be
a much reduced case fatality rate for H5N1. Nonetheless, studies such as
Zelicoff's point to the fact that even more commonly accepted
assumptions regarding influenza can be major uncertainties in the
scientific community.

In one study by Frank and others,[^29] at least 8 percent of illnesses
in children in a seasonal outbreak were associated with pre-symptomatic
shedding, which was noted up to 6 days before illness; 5 percent
continued to shed at the end of the second week. Peak rates of shedding
correlated with symptoms. In an influenza type A study of children by
Hall and others,[^30] symptoms and the highest viral shedding occurred
early in illness, and 30 percent of patients still shed at day 7.
Several studies have also shown that asymptomatic shedding at a low
concentration may be common during a seasonal outbreak.[^31] Monto and
others[^32] documented that approximately 60 percent of seasonal
infections were asymptomatic. However, this fraction included persons
with partial immunity due to infection with the same subtype as in
previous years. The prevailing idea for H5N1, with the majority of the
world being immunologically naïve, is that the percentage of
asymptomatic infections would be lower in a pandemic. Viral shedding in
asymptomatic persons is likely to be quite low and viral shedding is
correlated with symptoms.[^33] To stay within the bounds of what is
known about asymptomatic infections of influenza and to capture the
uncertainty inherent in asymptomatic infections, CIPDSS used lower
transmission values for the fraction of transmission (10 to 50 percent)
occurring prior to the onset of clear symptoms. The total fraction of
asymptomatic infections could then be varied from 5 to 80 percent, and
all parameters for asymptomatic infections were co-varied.

## Sociological Uncertainty Factors

### Nonpharmaceutical Interventions

Ultimately, the successful control and containment of a pandemic will
depend on using a variety of mitigation measures that include both
pharmaceutical and nonpharmaceutical interventions (NPIs). In February
2007, the CDC released a guidance document for the use of NPIs during an
influenza pandemic.[^34] These guidelines document the objectives of
NPIs during a pandemic to include

-   Delaying the exponential growth in illnesses and shifting the
    pandemic curve to the right to "buy time" for production and
    distribution of a well-matched pandemic strain vaccine

-   Decreasing the pandemic peak

-   Reducing the total number of incident cases and reducing community
    morbidity and mortality

While NPIs have been shown to be effective in controlling a pandemic in
other modeling simulation studies,[^35] they can also be the most costly
in terms of lost wages and workers available.

#### Isolation and Quarantine

Isolation refers to separating persons who have a specific infectious
illness from those who are healthy and restricting their movement to
stop the spread of that illness. Quarantine refers to separating and
restricting movement of persons who, while not yet ill, have been
exposed to an infectious agent and therefore may become infectious. Both
isolation and quarantine are public health strategies that have proven
effective in stopping the spread of infectious diseases. Quarantine of a
person exposed to pandemic influenza would last for 1 incubation period.
For annual influenza, the incubation period usually is as long as 4
days. However, this period could be either potentially shorter or longer
for a new type of influenza virus. To capture this uncertainty, this
study used a uniform range of 12 to 96 hours.

The simulation scenarios for pandemic influenza used a general
quarantine factor that varied by age class and by public health
responders that correspond to the 4 types of social distancing suggested
by the CDC guidelines.[^36] These included, but were not limited to,

-   Voluntary isolation of the sick at home or in a hospital

-   Voluntary home quarantine of potentially exposed family members of
    the sick

-   Child social distancing, including dismissing students from schools,
    closing childcare programs, and reducing out-of-school social
    contacts and community mixing

-   Adult social distancing, including canceling large public gatherings
    and altering work environments and schedules

##### Self Quarantine

The fraction of people who self-quarantine is dependent on age group,
correlated to the rate that people become ill and restricted to those
who are not already affected by the outbreak (such as the sick or
vaccinated). Individuals may self-quarantine either as a result of their
own preferences (for example, fear reaction to media coverage) or in
response to guidance from government officials. There is a delay in the
model between changes in the progress of the outbreak and changes in
self-quarantining behavior. This delay decouples the disease progression
and self-quarantine behavior to some degree, with delays of a week or
more producing reasonable results.

Due to demographic differences in quarantine behavior, the study
incorporated a maximum relative tendency of the different demographic
groups to isolate themselves during a crisis. For example, infants and
the elderly will be much more likely to self-isolate than working-age
people who need to continue work-related tasks. In addition, school-age
children may stay home at a higher rate due to school closures.

The self-quarantine sick-rate modifier is a multiplier of the rate that
people are becoming ill, which is used as the initial estimate of the
rate at which people self-quarantine. The team assumed that the people
that self-quarantine will be a multiple of the infected people. They
then modified the rate to reflect limitations imposed by the
availability of people to self-quarantine and the maximum fraction of
the population subset that engages in the self-quarantine activity
(self-quarantine tendency is 0.95 for infants and elderly, 0.9 for
school-age children, 0.4 for adults, and 0.25 for responders). A range
of 2 to 10 was used for the sick-rate modifier, loosely based on survey
results from the SARS outbreak in Hong Kong. The team believes that the
resulting range of self-quarantine behavior in the simulations is
reasonable.* *

## Public Health Uncertainty Factors

### Base Fraction of Afflicted Seeking Healthcare

This parameter is the fraction of people who would be symptomatic with
the pandemic flu and who would seek healthcare of some kind (at a
physician's office, clinic, or ER or by calling for EMS) if the case
fatality rate was 0.02 (the 1918 pandemic case mortality rate). CIPDSS
used a case fatality rate of 0.02 for the point-case analyses earlier in
this study and assumed the fraction of afflicted seeking healthcare to
be 0.6, with the other 40 percent of people with pandemic flu
self-treated. The team based this choice for the earlier part of this
study on the planning assumptions in the *HHS* \[U.S. Department of
Health and Human Services\] *Pandemic Influenza Plan*,[^37] which were
that 50 percent of ill people would seek outpatient medical care and 11
percent would be hospitalized in a severe (1918-like) pandemic. This
study took the fraction seeking medical care to be 60 percent rather
than 50 percent, because the HHS plan is unclear about whether the
fraction hospitalized is a subset of those patients seeking outpatient
medical care or a separate fraction.

In setting an appropriate range of values for the uncertainty analysis,
CIPDSS considered it unlikely that 100 percent of flu victims would seek
medical care, so the analysts set the upper limit to 90 percent. The
team set the lower limit to 40 percent because, in preliminary
simulations, unrealistic results would sometimes occur if the fraction
was lower. For example, if over 60 percent of flu cases were mild enough
that the people would simply self-treat, there might not be enough
severe cases to account for the assumed death rate. The model is
currently structured so that anyone sick enough to be at risk of dying
is assumed to seek healthcare. In reality, some people might die without
having sought care, but the current model neglects that possibility.

CIPDSS incorporated a number of adjustments to this fraction in the
model. Most notably, for case fatality rates higher than 0.02, CIPDSS
assumed the fraction of afflicted seeking healthcare to be higher than
the base fraction that is sampled. Thus, at the upper end of the case
fatality rates considered (that is, near 0.15), the model assumes that
almost all flu victims would seek care even if the sampled value of base
fraction of afflicted seeking healthcare is well below 1. In addition,
the model allows for the possibility that some people will seek
healthcare and then cancel their request for care if the healthcare
system is heavily overloaded and the waiting time is so long that their
health starts to improve.

### Maximum Mortality Enhancement Multiplier 

This parameter takes into account a decline in the quality of treatment
if the healthcare system is overloaded. If waiting times for emergency
care or to gain admission to hospital are too long, the mortality rate
in the model will increase, up to the maximum mortality enhancement
multiplier. In the earlier analyses, this effect was not included, so
the mortality enhancement multiplier was left at 1. For the uncertainty
analyses, inclusion of this effect was desired, but the amount of
increase in mortality that could occur is not really known. There have
been a number of studies (usually relating to particular illnesses) that
have found increases in mortality when care is delayed, but how the
increase in mortality might average out over all patients in an
emergency room or a hospital is not known. Thus, the range of 1 to 2 for
the maximum effect is somewhat speculative although it does have some
support in the literature.

### Health Threat Reaction Time

This parameter determines how quickly an outbreak of pandemic influenza
is recognized. It is the number of cases of flu that occur before the
outbreak is recognized, and it is determined independently for each
region. After the outbreak is recognized, various mitigation policies
can be activated (such as targeted or mass vaccination and contact
tracing with quarantine), depending on what policies are assumed for a
given scenario. At the lower end, the outbreak is recognized with the
very first case, which is not realistic but is included to see how much
it would help if the pandemic could be recognized immediately. There is
no specific rationale for the upper limit of 512 cases, but the team
assumed in the model that the pandemic would be recognized by the time
there are that many cases.

## Parameter Distributions

The model initially assigned the uniform probability distribution over
the expected range of the variable for the 20 parameters selected for
uncertainty analysis in this study. The uniform distribution assumes
that any value in the variable range is equally likely and is often used
to represent a lack of knowledge about the relative frequency of values
for a variable. While less useful for uncertainty analysis, it is
appropriate for sensitivity analysis. Distributions other than the
uniform are more appropriate for several of the parameters. For example,
the input variable for time of incubation in days can be described by a
beta distribution and *R~0~* by a lognormal distribution. By assigning
appropriate probability distributions to the input variables,
distributions on the output consequences can be represented and average
outcomes can be determined with greater confidence.

CIPDSS completed the sensitivity analysis using the uniform distribution
for all of the variables in order to obtain equal and appropriate
samples of each possible scenario. Table 3-4 defines the distributions
used in the uncertainty analysis.

[]{#__RefHeading___Toc178751242 .anchor}Table 3-4: Probabil**i**ty
distributions for uncertainty analysis

  -------------------------- ------------------- ---------- ---------- ------------ ------------ ----------- ----------
  **Variable Name**          **Distribu-tion**   **Mean**   **Std      **Param1**   **Param2**   **Scaling   **Shift
                                                            Dev**                                Factor**    Factor**

  Initial antiviral          uniform             5.10E+07   1.73E+07   2.10E+07     8.10E+07     1           0
  availability                                                                                               

  Fraction antivirals        uniform             0.50       0.2309     0.1          0.9          1           0
  applied to prophylaxis                                                                                     

  Death rate reduction from  uniform             0.45       0.1443     0.2          0.7          1           0
  antivirals                                                                                                 

  Initial vaccine            uniform             7.00E+07   2.89E+07   2.00E+07     1.20E+08     1           0
  stockpile\[partially                                                                                       
  effective\]                                                                                                

  Vaccine                    uniform             0.40       0.1732     0.1          0.7          1           0
  effectiveness\[partially                                                                                   
  effective\]                                                                                                

  Vaccine                    uniform             0.60       0.1155     0.4          0.8          1           0
  effectiveness\[fully                                                                                       
  effective\]                                                                                                

  Time before vaccine gives  gamma               432.0000   83.1384    27           16           1           0
  immunity \[partially                                                                                       
  effective\]                                                                                                

  Time before vaccine gives  gamma               432.0000   83.1384    27           16           1           0
  immunity\[fully                                                                                            
  effective\]                                                                                                

  Fraction contact           beta                0.033      0.0163     4            116          1           0
  effectiveness                                                                                              

  Standard production rate   beta                2.00E+07   1.13E+03   2            10           1.20E+08    0

  Self quarantine sick rate  uniform             6.0        2.3094     2            10           1           0
  modifier                                                                                                   

  Average contacts per case  normal              20.0       5.0000     20           5            1           0

  Reproductive               lognormal           2.33       1.1802     0            0.76         1           1
  number\[infant\]                                                                                           

  Reproductive               lognormal           2.33       1.1802     0            0.76         1           1
  number\[youth\]                                                                                            

  Reproductive number\[young lognormal           2.33       1.1802     0            0.76         1           1
  adult\]                                                                                                    

  Reproductive number\[old   lognormal           2.33       1.1802     0            0.76         1           1
  adult\]                                                                                                    

  Reproductive               lognormal           2.33       1.1802     0            0.76         1           1
  number\[elderly\]                                                                                          

  Reproductive               lognormal           2.33       1.1802     0            0.76         1           1
  number\[responders\]                                                                                       

  Fraction of transmission   beta                0.20       0.0784     5            20           1           0
  prior to clear symptoms                                                                                    

  Relative contagion of      beta                0.43       0.1278     6            8            1           0
  asymptomatics                                                                                              
  -------------------------- ------------------- ---------- ---------- ------------ ------------ ----------- ----------

Table 3-4: Probabil**i**ty distributions for uncertainty analysis
(continued)

  ------------------------ ------------------- ---------- ---------- ------------ ------------ ----------- ----------
  **Variable Name**        **Distribu-tion**   **Mean**   **Std      **Param1**   **Param2**   **Scaling   **Shift
                                                          Dev**                                Factor**    Factor**

  Additional fraction      beta                0.33       0.1307     4            8            1           0
  asymptomatic                                                                                             

  Nominal fatality         beta                0.05       0.0340     2            38           1           0
  rate\[infant\]                                                                                           

  Nominal fatality         beta                0.05       0.0340     2            38           1           0
  rate\[elderly\]                                                                                          

  Nominal fatality         beta                0.05       0.0340     2            38           1           0
  rate\[youth\]                                                                                            

  Nominal fatality         beta                0.05       0.0340     2            38           1           0
  rate\[young adult\]                                                                                      

  Nominal fatality         beta                0.05       0.0340     2            38           1           0
  rate\[old adult\]                                                                                        

  Nominal fatality         beta                0.05       0.0340     2            38           1           0
  rate\[responders\]                                                                                       

  Time to incubate         gamma               42.00      24.2487    3            14           1           0

  Duration of prodromal    gamma               42.00      24.2487    3            14           1           0

  Duration of early        gamma               63.48      29.5977    4.6          13.8         1           0
  symptoms                                                                                                 

  Time to recover          gamma               288.00     83.1384    12           24           1           0

  Maximum mortality        uniform             1.50       0.2887     1            2            1           0
  enhancement multiplier                                                                                   

  Base fraction of         uniform             0.65       0.1443     0.4          0.9          1           0
  afflicted seeking                                                                                        
  healthcare                                                                                               

  Normal health threat     gamma               180.00     103.9230   3            60           1           0
  reaction time                                                                                            
  ------------------------ ------------------- ---------- ---------- ------------ ------------ ----------- ----------

### Pre-symptomatic and Asymptomatic Spread

The evidence supporting the transmission of influenza prior to symptoms
includes data from 2 sources. First, experimental and observational
studies of viral excretion usually find that infected people start
excreting influenza viruses at low levels from their respiratory tracts
a short while before they develop symptoms.[^38] Second, serological
studies of levels of population immunity to influenza often find people
who show antibodies from prior infection but have no recollection of
symptoms.[^39] Other surveys suggest that many more people have been
infected during annual pandemics than can be explained by the number of
cases observed.[^40] However, as there are few field reports of
infections from asymptomatic or pre-symptomatic persons, the public
health importance of such transmission remains unclear. One systematic
review found very few convincing reports of transmission from a
pre-symptomatic patient.[^41] Reports of asymptomatic or pre-symptomatic
patients excreting high levels of virus are rare and it is possible that
any infections resulting from such transmissions are mild or
asymptomatic, although they could be important in maintaining chains of
transmission.[^42]

Because the spread of influenza by infected but asymptomatic people is
one of the largest biological uncertainties, it has been described as
the top research priority.[^43] Knowledge gaps for influenza include the
following questions:

-   What is the length of the asymptomatic incubation period?

-   Is there viremia excreted during the asymptomatic stage?

-   What is the infectivity of blood during the asymptomatic stage?

-   What are the contributions to influenza transmission and disease
    from pre-symptomatic and asymptomatic spread?

-   Is there significant pre-symptomatic (transmission from people who
    will become sick with influenza before they develop symptoms) and/or
    asymptomatic spread (transmission from people who are infected but
    never develop symptoms)?

To capture the uncertainty of the role of transmission of influenza
prior to symptoms or in asymptomatic individuals, the model uses 3 input
variables that affect transmission, as listed in Table 3-5.

[]{#__RefHeading___Toc178751243 .anchor}Table 3-5: Input variables that
affect transmission

  -------------------------------------- ---------- ---------- -----------
  **Variable**                           **Low      **High     **Units**
                                         Value**    Value**    

  Fraction of Transmission Prior to      0.1        0.5        Fraction
  Clear Symptoms                                               

  Relative Contagion of Asymptomatic     0.1        0.8        Fraction

  Additional Fraction of Asymptomatic    0.05       0.8        Fraction
  -------------------------------------- ---------- ---------- -----------

The 3 variables in the model were varied while using a beta distribution
for each.

#### Fraction of Transmission Prior to Clear Symptoms

This parameter (equal to theta in Fraser et al., 2004)[^44] is the
average fraction of transmissions of an infected individual before
symptoms appear. The larger the value, the harder it is to suppress a
pandemic using a limited quarantine based on recognition of symptoms
alone (that is, not doing aggressive contact tracing). Fraser et al.
(2004)[^45] indicate that the range for this parameter is from 0 up to
0.20 (Figure 3-1). For this study, the team selected 6.6 percent in
consultation with EpiSimS*.*

![](media/image7.wmf){width="4.058333333333334in"
height="2.395138888888889in"}

[]{#__RefHeading___Toc179875380 .anchor}Figure 3-1: The beta (5,20)
distribution for the fraction of\
transmission that occurs prior to clear symptoms

#### Relative Contagion of Asymptomatics

The contagiousness of asymptomatics is expressed as a fraction of the
contagiousness of the symptomatics (Figure 3-2).

![](media/image8.wmf){width="4.058333333333334in"
height="2.395138888888889in"}

[]{#__RefHeading___Toc179875382 .anchor}Figure 3-2: The beta (6,8)
distribution for the relative contagiousness of asymptomatic infected
people

#### Additional Fraction of Asymptomatics 

The number of persons who are asymptomatic is expressed as a fraction of
those showing symptoms (Figure 3-3).

![](media/image9.wmf){width="4.0625in" height="2.3854166666666665in"}

[]{#__RefHeading___Toc179875384 .anchor}Figure 3-3: The beta (4,8)
distribution for relative fraction of asymptomatic\
infections compared to symptomatic infections

#### Reduction in Early Symptom Contagion Due to Limited Quarantine 

This parameter captures the effect of the assumption that 50 percent of
remaining cases are isolated per day after symptoms become apparent; for
example, 15 percent of cases slip through due to delayed recognition.
This study assumed this fraction of persons isolating themselves to be
0.82 based on the idea of isolating 50 percent of the remaining
symptomatics each day for 3 days. The assumption would work as follows:
If the duration of the early (contagious) stage is 5 days, 20 percent of
the possible transmissions (without any isolation) would occur each day.
If 50 percent of those people were isolated at the beginning of the
first day (with the initial onset of symptoms), only 10 percent of the
possible transmissions would occur. If 50 percent of the
remaining people were isolated at the beginning of the second day, only
5 percent of the possible transmissions would occur that day. Again,
isolating 50 percent of the remaining symptomatics at the beginning of
the third day would result in 2.5 percent of the possible transmissions
occurring. Assuming everyone else is isolated by the beginning of the
fourth day, the total transmission would be 0.1+0.05+0.025=0.175 of the
possible transmissions and the reduction multiplier would be 0.825.
Figure 3-4 gives the relationship of transmission reduction to fraction
of people being isolated. The model set this variable at 0.82 for all
runs. This value may be optimistic for pandemic influenza.

![](media/image10.png){width="3.6666666666666665in" height="2.96875in"}

[]{#__RefHeading___Toc179875386 .anchor}Figure 3-4: The relationship of
fraction of contagious population isolated per day to the reduction in
overall transmission of the disease

### Effective Reproductive Number 

The effective reproductive number, *R*, for disease transmission is
defined as the average number of secondary infectious cases produced in
a population where a fraction *X* is susceptible over the lifetime of
one infectious individual.[^46] The effective reproductive number, *R*,
is related to the basic reproductive number, *R~0~*, by the equation

*R* = *R~0~X* Equation 1

The basic and effective reproductive numbers are good indicators of the
severity of a pandemic and the effectiveness of control.[^47] In
general, estimation of these parameters from actual disease outbreak
data is limited because the process of infection is not observed, data
are often incomplete, and the rate of infection is non-linear.[^48] An
effective reproductive number is calculated to integrate the impacts of
asymptomatic transmission and effects of individuals who isolate
themselves early in the disease progression due to being ill.
Self-isolation of infected and symptomatic persons greatly affects the
transmission of the disease to new individuals. In terms of the
parameters used in the CIPDSS infectious disease model, the effective
reproductive number is given by the following formula:

> $R = R_{0}\left\{ f_{t} + (1 - f_{t})(1 - f_{i}) + f_{a}C_{a} \right\}$
> Equation 2

Where

> *R*~0~ is the basic reproductive number
>
> *f~t~* is the fraction of transmission prior to clear symptoms
>
> *f~a~* is the additional fraction asymptomatic
>
> *C~a~* is the relative contagion of asymptomatic
>
> *f~i~* is the reduction in early symptom contagion due to limited
> quarantine

The ranges and probability distributions of the above parameters were
described previously. Figure 3-5 gives the resulting distribution of the
effective reproductive number, *R*, and shows the cumulative
distribution function (CDF) obtained from the 512 sampled values. Figure
3-5 also shows the CDF of the basic reproductive number, *R~o~,* for
comparison. Approximately half of the sampled realizations had an
effective reproductive number less than 1, which means that a pandemic
would not develop in those realizations. To retain the focus of this
study on the potential for a severe pandemic, the realizations with *R*
\< 1 were dropped, and the 243 realizations with *R* 1 were the focus of
discussion. Figure 3-6 shows the CDFs of *R* and *R*~0~ over the subset
of realizations with\
*R* 1.

![](media/image11.wmf){width="5.302083333333333in"
height="3.6131944444444444in"}

[]{#__RefHeading___Toc179875388 .anchor}Figure 3-5: Cumulative
distributions of sampled *R~[0]{.underline}~* and effective *R*\
(all 512 realizations)

![](media/image12.wmf){width="5.354166666666667in"
height="3.6486111111111112in"}

[]{#__RefHeading___Toc179875389 .anchor}

Figure 3-6: Distributions of *R*~0~ and effective *R* (243 realizations
with *R* 1)

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# Results and Analysis

## Initial Sensitivity Analysis

Acknowledging the high degree of uncertainty with the possible variable
ranges and outcomes, the analysts conducted a preliminary sensitivity
analysis to reduce the number of variables included, the acceptable
ranges used, and the number of co-varied outcomes. Results show that the
overall uncertainty in the parameters of the infectious disease model
can be remarkably large. Figure 4-1 shows some of the variability that
may be expected for multiple scenario runs that cover the range of
biological, sociological, and mitigation options for affecting the
cumulative number of illnesses.

![](media/image13.wmf){width="4.25in" height="3.6798611111111112in"}

[]{#__RefHeading___Toc179875391 .anchor}Figure 4-1: Cumulative number of
illnesses for baseline scenario\
runs with no mitigations

R^2^ is a standard goodness of fit measure for evaluating fit of a model
to a response. For the preliminary sensitivity, the model used R^2^ as a
tool to rank inputs with respect to relative importance. The R^2^ was
based on fitting a single input, x, to a response modeled as

> y = mx + b Equation 3

If for a certain response, the R^2^ for a model based on x~1~ is larger
than R^2^ for a model based on x~2~, then x~1~ is judged more important
or influential than x~2~ for that response. The model applied an
approximate test of significance for R^2^: if R^2^ for a particular
response (x~i~) is below a statistically determined threshold, then x~i~
is judged unimportant. The model then computed the R^2^ values for 20
inputs and 47 responses. The results served as a filter to eliminate
unimportant variables for the subsequent uncertainty analysis.

The CIPDSS team completed a coarse sensitivity analysis on 14 mitigation
scenarios (minus the baseline scenarios) for a total of 64 runs per case
and a total number of 896 simulation runs.

Using the R^2^ criterion in the preliminary sensitivity analysis for
each of the outputs listed below, the highest ranking variables that
affect that pandemic scenario outputs are given.

-   Total attack rate and overall total illnesses

```{=html}
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```
-   Fraction transmission prior to clear symptoms (infectious
    asymptomatic)

-   Reproductive number

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```
-   Total cumulative deaths

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```
-   Fraction transmission prior to clear symptoms (infectious
    asymptomatic)

-   Nominal fatality rate (infant, elderly)

-   Nominal fatality rate (youth, adults, responders)

-   Reproductive number

```{=html}
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```
-   Total cumulative event deaths by cause (normal)

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```
-   Maximum mortality enhancement multiplier

-   Self-quarantine tendency maximum (adults, responders)

-   Disease stage duration (all stages)

```{=html}
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```
-   Maximum total scenario affliction rate

```{=html}
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```
-   Fraction transmission prior to clear symptoms (infectious
    asymptomatic)

-   Reproductive number

-   Disease stage duration (all stages)

```{=html}
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```
-   Maximum total workers unavailable

```{=html}
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```
-   Self-quarantine tendency maximum

```{=html}
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```
-   Total lost GDP

```{=html}
<!-- -->
```
-   Disease stage duration (all stages)

```{=html}
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```
-   Total event healthcare cost

```{=html}
<!-- -->
```
-   Fraction transmission prior to clear symptoms (infectious
    asymptomatic)

-   Nominal fatality rate (infant, elderly)

-   Nominal fatality rate (youth, adults, first responders)

-   Reproductive number

-   Base fraction of afflicted seeking healthcare

```{=html}
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```
-   Total number of hospitalized cases (normal)

```{=html}
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```
-   Average contacts per case

-   Self quarantine tendency maximum

-   Reproductive number

-   Disease stage duration (all stages)

-   Base fraction of special patients requiring admission

-   Fraction of special patients treated in field by EMS

-   Additional fraction asymptomatic

-   Nominal fatality rate (infant, elderly)

```{=html}
<!-- -->
```
-   Total number of hospitalized cases (special)

```{=html}
<!-- -->
```
-   Fraction of transmission prior to clear symptoms (infectious
    asymptomatic)

-   Reproductive number

-   Disease stage duration (all stages)

The results showed several variables that were ***not*** significant
according to the R^2^ criterion. Therefore, these variables would not
have a strong impact on the outcome of the pandemic in the number of
people affected, deaths, or outbreak duration. The following variables
also failed the R^2^ criterion and were deemed unimportant for
subsequent analysis in this study (in no particular order):

-   Normal health threat reaction time

-   Base fraction of patients seeking care at physicians offices and
    clinics (special)

-   Base fraction of patients seeking care at physicians offices and
    clinics (worried well)

-   Base fraction of special patients taken to hospital by EMS

-   Delta nominal fatality rate

-   Detection threshold

-   Amount of time to create vaccine (fully effective)

-   Initial vaccine stockpile (partially effective)

-   Time before vaccine gives immunity (partially and fully effective)

-   Vaccine effectiveness (partially and fully effective)

-   Initial antiviral availability (given that the sample range was
    20--80 million)

-   Fraction of physicians office and clinic visits that are urgent

-   Self quarantine tendency maximum (youth, infant, elderly)

-   Fraction antivirals applied to prophylaxis

-   Death rate reduction from antivirals

Figure 4-2 shows the averages for scenario deaths for the preliminary
analysis using the uniform distributions for the input variables that
are binned by reproductive number. Mitigations show an impact at all
values of reproductive number.

![](media/image14.wmf){width="3.75in" height="2.59375in"}

![](media/image16.wmf){width="3.625in" height="2.5104166666666665in"}

![](media/image17.wmf){width="3.6243055555555554in"
height="2.4590277777777776in"}

Notes: AV = antiviral; SD = social distancing

Vaccination Strategy:

1 No vaccination or contact tracing 5 Mass vaccination ***and*** early
vaccination

2 Contact tracing without vaccination 6 Targeted followed by mass
vaccination ***and*** early vaccination

3 Mass vaccination only with no early vaccine 7 Targeted vaccination
***and*** early vaccination

4 Targeted vaccination only with no early vaccine

[]{#__RefHeading___Toc179875392 .anchor}Figure 4-2: Scenario averages
for different *R~0~* groupings

## Final Analysis

### Mitigation Strategies Compared

Table 4-1 summarizes the scenarios analyzed in terms of the averages of
some key measures of impact. Figures 4-3 and 4-4 present the averages
for flu deaths and illnesses. The results show that all of the
interventions considered provide some reduction in the number of
illnesses and deaths caused by the pandemic, but social distancing (in
all social distancing scenarios; labeled SD in the figures) shows the
best results in this analysis. Mass vaccination provides little
reduction in deaths if it only occurs after a fully effective vaccine is
developed (vaccination strategy 3), because the pandemic has largely run
its course by then. Even mass vaccination with a stockpile of partially
effective vaccine (vaccination strategy 4) does not perform as well as
social distancing because of the limited quantity of vaccine available
in the initial stockpile. The results show only a small reduction in
deaths from contact tracing and quarantine, with or without vaccination
(vaccination strategies 2, 5, and 6). The scenarios that include
antivirals (labeled AV in the figures) have lower deaths than the
corresponding scenarios without antivirals, but the reduction in deaths
is relatively modest. However, social distancing provides substantial
reduction in deaths, even without any contact tracing or vaccination. On
average, antivirals provide a reduction in deaths of about 8 percent,
social distancing provides a reduction in deaths of about 62 percent,
and the combination of social distancing plus antivirals provides a
reduction in deaths of about 66 percent. (There is some variation in
these numbers for the various vaccination strategies.)

The computed numbers of flu deaths shown in Table 4-1 and illustrated in
Figure 4-3 include an increased death rate when the healthcare system is
overloaded, as discussed in the model section. Under such conditions,
the model also includes an increased death rate from normal (non-flu)
afflictions. The number of non-flu deaths attributable to the pandemic
averages about 35,000 in the simulations and is as high as about 400,000
in the most extreme realizations, but the computed number of non-flu
deaths is insignificant compared to the number of direct influenza
deaths. If the average number of deaths shown in Table 4-1 is divided by
the average number of illnesses, the result is an average case fatality
rate of approximately 6 percent. The average of the distribution used
for the nominal fatality rate is about 5 percent. The increase from 5
percent to 6 percent indicates an average 20-percent increase in deaths
because of healthcare overloading. However, there is great uncertainty
about this effect so the average 20-percent increase in deaths should
not be considered representative, but only indicative of the potential
magnitude of the effect.

[]{#__RefHeading___Toc178751244 .anchor}Table 4-1: Comparison of
scenario averages

  ------------- ---------------- -------------- ------------------- -------------- ---------- -------------- ----------------
  **Scenario    **Vaccina-tion   **Flu          **Flu               **Flu Deaths   **1-Year   **Peak         **Health-care
  Set**         Strategy**       Illnesses      Hospitali-zations   (millions)**   Lost GDP   Fraction       Cost
                                 (millions)**   (millions)**                       (%)**      Workers        (\$billions)**
                                                                                              Unavail-able   
                                                                                              (%)**          

  No social     1                100            22.7                6.6            2.9        13             220
  distancing,                                                                                                
  no antivirals                                                                                              

                2                72             16.2                4.6            2.7        14             170

                3                92             21.0                6.1            2.8        13             200

                4                59             13.2                3.8            1.8        8              130

                5                63             14.2                4.1            2.3        13             150

                6                68             15.4                4.4            2.5        14             160

  No social     1                93             21.1                6.1            2.7        12             210
  distancing,                                                                                                
  antivirals                                                                                                 

                2                68             15.3                4.4            2.6        13             170

                3                85             19.3                5.6            2.5        12             190

                4                53             11.9                3.4            1.6        7              120

                5                58             13.0                3.7            2.1        12             140

                6                63             14.3                4.1            2.3        13             160

  Social        1                41             8.9                 2.4            6.0        22             100
  distancing,                                                                                                
  no antivirals                                                                                              

                2                35             7.6                 2.0            6.0        21             90

                3                32             7.1                 1.9            4.3        21             80

                4                22             4.9                 1.3            2.8        14             50

                5                26             5.6                 1.5            4.0        20             70

                6                32             7.0                 1.9            5.5        20             80

  Social        1                37             8.1                 2.1            5.6        20             90
  distancing,                                                                                                
  antivirals                                                                                                 

                2                33             7.1                 1.9            5.6        19             90

                3                29             6.3                 1.7            4.0        20             70

                4                19             4.2                 1.1            2.6        13             50

                5                23             5.0                 1.4            3.7        19             70

                6                29             6.4                 1.7            5.1        19             80
  ------------- ---------------- -------------- ------------------- -------------- ---------- -------------- ----------------

> Notes: GDP = gross domestic product
>
> Vaccination Strategy
>
> 1 No vaccination or contact tracing 4 Mass vaccination with early
> vaccine
>
> 2 Contact tracing without vaccination 5 Targeted followed by mass
> vaccination with early vaccine
>
> 3 Mass vaccination only with no early vaccine 6 Targeted vaccination
> with early vaccine

![](media/image18.wmf){width="5.020833333333333in"
height="3.423611111111111in"}

[]{#_Ref169404436 .anchor}

Notes: AV = antiviral; SD = social distancing

Figure 4-3: Scenario Averages for total deaths due to the pandemic

![](media/image19.wmf){width="5.166666666666667in"
height="3.5215277777777776in"}

Notes: AV = antiviral; SD = social distancing

[]{#__RefHeading___Toc179875396 .anchor}Figure 4-4: Scenario averages
for flu illnesses

The amount of variation in the number of illnesses and deaths about the
averages is presented in box plots in Figures 4-5 and 4-6. In each box
plot, the range of values is shown for each of the 24 scenarios. For
each scenario, the box has its bottom at the 25^th^ percentile of the
distribution and its top at the 75^th^ percentile. A line within the box
indicates the 50^th^ percentile (median value) and a circled plus sign
indicates the average value. (The averages are the values listed and
plotted in Table 4-1, Figure 4-3, and Figure 4-4). A whisker extends
above each box either to the highest value or to a length of 1.5 times
the box length, whichever is less. Any additional values are considered
outliers and are indicated individually above the whiskers.

![](media/image20.wmf){width="6.022916666666666in"
height="4.011805555555555in"}The ranges are quite large for each
scenario, ranging from near zero to much higher than the average. The
average is sometimes significantly higher than the median. The highest
values in these figures would represent extremely severe consequences,
far more severe than any pandemic in recent history. The wide ranges of
the consequence measures result, of course, from the large amount of
uncertainty in the sampled input parameters. The previous discussion
based on the average values would still be generally valid if maximum
values were used instead, but the differences among the maxima are not
as strong as the differences among the averages.

[]{#_Ref169342768 .anchor}Notes: AV = antiviral; SD = social distancing

Figure 4-5: Scenario ranges for flu illnesses

![](media/image21.wmf){width="6.0in" height="4.0in"}

Notes: AV = antiviral; SD = social distancing

[]{#__RefHeading___Toc179875399 .anchor}Figure 4-6: Scenario ranges for
flu deaths

#### Public Health Infrastructure Impacts

Following are a few results relating to the public health
infrastructure, which was the only infrastructure included in the model
simulations.[^49] As with the consequence measures already discussed,
there is a very large range of stresses on the healthcare system in the
simulations. If the average number of hospitalizations is divided by the
average number of illnesses, the result is that approximately 22 percent
of the people becoming sick with the pandemic flu are sick enough to
require hospitalization. This is much higher than the 11 percent assumed
for a severe pandemic in the *HHS Pandemic Influenza Plan*,[^50] but
that is because the range of severity considered in this analysis goes
well beyond the HHS severe pandemic (for example, a case fatality rate
of 2 percent is assumed for a severe pandemic in the HHS plan, as
compared with the average of 5 percent for the assumptions of this
analysis). The number of flu patients cared for in hospitals averages
2--5 million in the various scenarios, with a maximum on the order of 13
million. As with other variables, there is also a large range of
pandemic durations represented in the results, and the number of
patients treated in a hospital with a limited number of beds can be
greater for a longer pandemic. This is illustrated in Figure 4-7, noting
that the numbers treated in hospital are greatest for relatively low
values of the effective *R~0~*, for which the pandemic develops more
slowly. Of greater importance is that in approximately 40 percent of the
realizations there are not enough hospital beds available to admit all
the patients who need treatment. In that case, the model assumed that
temporary facilities would be set up to care for the excess flu
patients. Figure 4-8 shows the average number of patients treated in
temporary facilities for the various scenarios. Figure 4-9 shows that
the scenario-to-scenario variation is similar to that for illnesses and
deaths. Figure 4-9 also shows the amount of variation within each
scenario. The high end of the range is extraordinary; that is, nearly
half of the entire population. It seems unlikely that temporary
facilities could be set up to care for that many people, and many of
them would likely have to go home for self-care or care by family or
friends. It would even be a challenge to set up enough temporary
facilities for the average number in the higher scenarios (almost 20
million patients). This underscores the importance of providing
interventions (vaccinations with an initial stockpile of partially
effective vaccine, antiviral treatments, contact tracing with
quarantine, and social distancing) that can reduce the numbers to a
manageable level. It also highlights the need for hospitals to have
viable plans to engage alternate treatment facilities, including home
care, temporary facilities, and use of workforce plans for highly
stressed situations.

[]{#__RefHeading___Toc179875400
.anchor}![](media/image22.wmf){width="6.5in"
height="4.430555555555555in"}Figure 4-7: Patients treated in hospital
versus effective\
reproductive number (baseline)

![](media/image23.wmf){width="4.877083333333333in" height="3.325in"}

Notes: AV = antiviral; SD = social distancing

[]{#__RefHeading___Toc179875402 .anchor}Figure 4-8: Scenario averages
for patients hospitalized in temporary care facilities

![](media/image24.wmf){width="5.791666666666667in"
height="3.857638888888889in"}

Notes: AV = antiviral; SD = social distancing

[]{#__RefHeading___Toc179875404 .anchor}Figure 4-9: Scenario ranges for
patients hospitalized in temporary care facilities

## Economic Impact 

The scenario averages for the calculated lost GDP are shown in Table 4-1
and Figure 4-10. The results show greater economic impact (lost GDP) in
scenarios that include social distancing because the assumptions for
social distancing include people staying away from work, and the
calculation of lost GDP is based on the number of worker-days lost.
Thus, while the scenarios with social distancing have the lowest number
of deaths, they also have the highest economic impact. The lowest
average economic impact is indicated to be in the scenarios with mass
vaccination and a stockpile of partially effective vaccine (vaccination
strategy 4, without social distancing). Another measure of economic
impact is the peak fraction of workers who are unavailable either
because of sickness or social distancing, shown in Table 4-1 and Figure
4-11. In comparing social distancing and no social distancing with no
antivirals, the result is a reduction of 60 million illnesses and 4
million deaths and an increase of the peak worker unavailability from 13
percent to 22 percent. If large numbers of employees are out of work, it
could be difficult for an infrastructure or industry to operate. Such
effects were discussed in some detail in the earlier report and will not
be discussed further here. The computed lost GDP is strongly correlated
with the peak fraction of workers who are unavailable to work, but they
are not completely correlated because there is variation in the amount
of time workers are out in addition to the fraction of them who are out.

![](media/image25.wmf){width="5.635416666666667in"
height="3.8333333333333335in"}

[]{#_Ref171131849 .anchor}

Notes: AV = antiviral; SD = social distancing

Figure 4-10: Scenario averages for lost gross domestic product

![](media/image26.wmf){width="5.5465277777777775in"
height="3.7909722222222224in"}

Notes: AV = antiviral; SD = social distancing

[]{#__RefHeading___Toc179875408 .anchor}Figure 4-11: Scenario averages
for peak fraction of workers unavailable

Box plots for lost GDP and peak fraction of workers unavailable because
of sickness or social distancing are shown in Figures 4-12 and 4-13.
Once again, the range for each scenario is very large, and the highest
values represent extremely severe consequences (although at low
probability). Few, if any, organizations could function with an absentee
rate of 80 percent. The most-cited potential peak for worker absenteeism
is 40 percent, which originated as a worst case planning assumption for
the pandemic effect on the workforce. The average absentee rates in this
study are much lower than that (Table 4-1 and Figure 4-8), but the full
range of results in individual realizations includes absentee rates
higher than 40 percent (Figure 4-13). Perhaps even more importantly,
Ehrenstein and others[^51] found that a modest majority of healthcare
professionals and only a minority of hospital administrators recognize
the obligation to treat patients in a pandemic situation despite the
potential risks. It will be a priority to maintain essential services
during the pandemic's peak and ensure continuity of businesses
nationwide. No special vaccinations or prophylactic treatments were
given to medical personnel in this study.

![](media/image27.wmf){width="5.4375in" height="3.6284722222222223in"}

[]{#_Ref164835702 .anchor}

Notes: AV = antiviral; SD = social distancing

![](media/image28.wmf){width="5.4319444444444445in"
height="3.6215277777777777in"}Figure 4-12: Scenario ranges for lost
gross domestic product

Notes: AV = antiviral; SD = social distancing

[]{#__RefHeading___Toc179875411 .anchor}Figure 4-13: Scenario ranges for
peak fraction of workers unavailable

#### Antiviral Use

If the scenario averages with antivirals are compared to those without
antivirals (no social distancing in both cases), the antivirals provide
an average reduction in deaths of 6 percent to 10 percent. The benefit
from antivirals will, of course, depend on the amount of antivirals
available. With the assumptions used in this analysis, the reduction in
deaths due to antivirals depends more on the antiviral production rate
than on the size of the initial antiviral stockpile. The results show a
large spread in the effectiveness of antivirals, with some increase in
the effectiveness at greater production rates.

Because antiviral agents can be stored (Tamiflu and Relenza have
shelf-lives of 3 and 5 years, respectively), developing a stockpile has
advantages as part of a strategy for a flu pandemic. By the end of 2006,
more than 20 million courses of antivirals were in the U.S. Strategic
National Stockpile (SNS). By 2008 it is estimated that there will be 80
million courses. Because this amount would be enough to cover only about
5 percent of the population for an 8-week period, which is far short of
WHO's recommendation that governments have enough antiviral treatments
on hand for at least 25 percent of their population, this study
considered an antiviral stockpile of up to 120 million courses. The
effectiveness of antivirals is dependent upon the infectiousness and
virulence of the disease and the amount in the SNS.

#### Effectiveness of Social Distancing

A primary social response to a contagious disease is to voluntarily
modify one's behavior, by any number of means, to reduce the rate of
contact with other potentially infectious individuals. While it is
reasonable to assume that some degree of social distancing occurs in a
pandemic, in this study social distancing is modeled as "on" or "off."
When social distancing is on, the degree of social withdrawal is assumed
to be proportional to the rate of infection. The rate of social
distancing is mildly decoupled from the rate of infection by introducing
a delay to account for individual perception of the risk of infection
and any preparation needed for entering a social distancing behavior.

The effect of social distancing is two-fold. First, it tends to reduce
morbidity and mortality by an average of 30 million cases and 10 million
deaths, respectively, over all vaccination strategies. Second, it tends
to increase the fraction of workers unavailable and, hence, increases
the fraction of lost GDP, by 0.05 (morbidity) and 0.01 (mortality),
averaged over all vaccination strategies. These results rely on
assumptions about gross social distancing behavior, as it is very
difficult to get reliable data upon which to base the model. It points
to a need for additional research in sociological response to pandemics.

The model calculates and reports the total number of self-isolation
person-days during the course of the disease based on input assumptions
about the strength of this behavioral response in the population. This
information is converted to an average reduction in the nominal contact
rate of the susceptible population by dividing the total self-isolation
person-days by the product of the duration of the disease in the
population and the average number of susceptible in the population:

Average Nominal Contact Rate Reduction of the Susceptible Population =
Equation 4\
Total Self-Isolation Person-Days/Disease Duration \* (293.6 Million -
Cases)/2)

Because the input variables are characterized with uncertainty
distributions and these affect the level of self-isolation adopted in
each model run, the value of the average nominal contact rate reduction
also varies across the runs. Furthermore, because of nonlinear
interdependencies in the model, there is not a simple relationship
between the level of contact rate reduction and the reduced number or
fraction of cases across the various vaccination strategies
investigated. However, in general, relatively small reductions in
contact rates (increases in social distancing), especially if
implemented early in the disease progression, can significantly
attenuate disease progression when compared to like scenarios without
social distancing implemented.

Figure 4-14 shows the effect of social distancing on the baseline (no
intervention) scenario by comparing the number on influenza cases with
the social distancing switch turned off (noSDnoAV1) and with social
distancing turned on (SDnoAV1). The comparison is presented in the form
of a scatter diagram that plots the number of cases with social
distancing (y-axis) against the number of cases without social
distancing (x-axis) from each of the 243 corresponding runs of the
SDnoAV1 and noSDnoAV1 scenarios. The vertical distance that each point
lies below the diagonal line drawn on the chart is the reduction in
number of influenza cases resulting from social distancing, with all
other input assumptions being the same. The average of all 243 runs is
noted by the vertical arrow extending down from the diagonal line and
the horizontal arrow extending from the left y-axis. When social
distancing assumptions are applied to the baseline (no intervention)
scenario runs, the average number of influenza cases decreases to 38.6
million from 98.2 million.

![](media/image29.wmf){width="6.270833333333333in"
height="4.302083333333333in"}

[]{#__RefHeading___Toc179875413 .anchor}Figure 4-14: Effect of social
distancing on the no intervention scenario

Figure 4-14 also shows the average reduction in nominal contact rate for
each social distancing scenario (SDnoAV1) run from the corresponding no
social distancing (noSDnoAV1) run for all 243 runs. The average contact
rate reduction ranged from 0.2 percent to 57.8 percent, with an average
value of 16 percent across all 243 runs. The largest reductions in cases
are not necessarily associated with higher contact rate reductions,
because many other factors in the runs influence the effect of contact
rate reduction, such as the gross attack rate or the rate of disease
progression through time.

![](media/image30.wmf){width="7.15625in" height="4.895833333333333in"}In
comparison, the effect of social distancing on the no intervention
scenario (Figure 4-15) shows a similar result based on the targeted
vaccination scenarios (noSDnoAV6 and SDnoAV6). Although the general
pattern of the results is similar, the reduction in contact rate is
generally smaller, averaging only 5 percent (range of 0.02 percent to
39.4 percent), and the average number of cases is reduced to 10 million
from 24.1 million. In both examples, the relative reduction in cases is
around 60 percent; examination of all the no social distancing and
social distancing scenarios results in average case reductions ranging
from 50 percent to nearly 70 percent.

[]{#__RefHeading___Toc179875414 .anchor}Figure 4-15: Effect of social
distancing on the targeted\
vaccination, no antivirals scenario

#### Vaccination

The primary defense against acquiring influenza is vaccination.
Vaccination for seasonal influenza has been shown to prevent disease,
reduce the rates of complications such as pneumonia, and reduce the
rates of hospitalizations and deaths. In the event of pandemic
influenza, vaccination will be important in controlling the pandemic.
However, because of the time required under current vaccine production
techniques, vaccines may not be available at the onset. Currently, the
time between identification of the candidate vaccine strain and
production of the vaccine for distribution to the population is
estimated to be approximately 6 months.[^52] As a result, considerable
effort is underway to develop better methods to produce vaccines, such
as developing cell-based or generic vaccines. Manufacturing capacity for
vaccine development is currently being improved to decrease the time to
distribution and increase the quantity of vaccines.

In addition, candidate pre-pandemic vaccines, based on the H5N1 strain,
are under development. The goal of pre-pandemic vaccines is to provide a
sufficient immune response using the smallest amount of antigen in a
single dose.[^53] Because the population is immunologically naïve,
pandemic vaccines currently under development require 2 doses.[^54]
Current vaccine protocols call for 2 doses (28 days apart) for
experimental pandemic vaccines. Pre-pandemic vaccines, as they are based
on strains other than the specific but unknown strain of the pandemic,
are less effective in providing immunity. Often, the system for
distributing vaccines, based mostly on private distributors, is
unpredictable and may deliver vaccines at various times throughout a
pandemic. In a press release dated 23 April 2007, the U.S. Food and Drug
Administration (FDA) announced the first vaccine with U.S. approval to
prevent people from becoming infected with the H5N1 influenza strain.
For this approved vaccine, 2 shots must be given 28 days apart.

Model results show that a pre-pandemic vaccine may be effective in
reducing the size and impact of an outbreak. However, there is much
variability surrounding the pattern for death rate reduction for an
early and partially effective vaccine. In the simulations, a single dose
was assumed to be given rather than 2, so the effective stockpile might
be considered to be half the size (if 2 doses are necessary). No pattern
for a threshold vaccine stockpile can be discerned at this time for the
range of pre-pandemic vaccine stockpile numbers considered. The next
step in investigating the relationship between potential responses and
the unknowns about the disease will be to differentiate the biological
uncertainty values in relationship to the variability in vaccine
stockpile and death-rate reduction. Using different transmission values,
EpiSimS may be able to add more detailed analyses to estimate vaccine
stockpile effectiveness.

### Case Fatality Rate

Case fatality is related to the overall deaths in a pandemic. In early
2007, the CDC released "Interim Pre-pandemic Planning Guidance:
Community Strategy for Pandemic Influenza Mitigation in the U.S. --
early targeted layered use of non-pharmaceutical interventions,[^55]"
which contained the pandemic severity index based on case fatality rate.
Figure 4-16 shows the relationship between the case fatality rate and
the total number of deaths, by the CDC severity index, in the baseline
scenario. As the case fatality rate increases, variability due to the
influence of the other disease characteristics increases.

![](media/image31.wmf){width="5.854330708661418in"
height="3.989763779527559in"}

[]{#__RefHeading___Toc179875416 .anchor}Figure 4-16: Influenza deaths
for the baseline scenarios by case fatality rate

## Final Sensitivity Analysis 

Using the R^2^ criterion in the final sensitivity analysis for each of
the outputs listed below, the highest ranking variables that affect that
pandemic scenario outputs are given.

There were 10 parameters that significantly contributed to the variance
of the output variables of interest:

-   Nominal fatality rate (infant, elderly)

-   Nominal fatality rate (youth, adults, responders)

-   Reproductive number

-   Time to recover (all stages)

-   Additional fraction asymptomatic

-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Maximum mortality enhancement multiplier

-   Fraction contact effectiveness (contact tracing)

-   Time to when vaccine gives immunity (both fully and partially
    effective)

-   Antiviral production rate

The following parameters were found to significantly affect the response
variables for all scenarios:   

-   Total attack rate and overall total illnesses

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-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Reproductive number

-   Time to recover (all stages)

-   Relative contagious asymptomatic

-   Additional fraction asymptomatic

-   Fraction contact effectiveness (contact tracing)

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-   Total cumulative deaths

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-   Fraction transmission prior to clear symptoms (infectious
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-   Nominal fatality rate (infant, elderly)

-   Nominal fatality rate (youth, adults, responders)

-   Reproductive number

-   Relative contagious asymptomatic

-   Additional fraction asymptomatic

-   Fraction contact effectiveness

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-   Maximum total scenario affliction rate

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-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Reproductive number

-   Disease stage duration (all stages)

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-   Maximum total workers unavailable

-   Reproductive number

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-   Relative contagious asymptomatic

-   Additional fraction asymptomatic

-   Fraction contact effectiveness (contact tracing)

-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Total lost GDP

-   Nominal fatality rate (youth, adults, first responders)

-   Reproductive number

-   Time to recover (all stages)

-   Relative contagious asymptomatic

-   Additional fraction asymptomatic

-   Fraction contact effectiveness (contact tracing)

-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Social distancing sick rate modifier

-   Total event healthcare cost

-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Nominal fatality rate (infant, elderly)

-   Nominal fatality rate (youth, adults, first responders)

-   Reproductive number

-   Base fraction of afflicted seeking healthcare

-   Time to recover (all stages)

-   Relative contagious asymptomatic

-   Additional fraction asymptomatic

-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Fraction contact effectiveness (contact tracing)

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-   Total number of hospitalized cases (normal)

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-   Average contacts per case

-   Self-quarantine tendency maximum

-   Reproductive number

-   Disease stage duration (all stages)

-   Base fraction of special patients requiring admission

-   Fraction of special patients treated in field by EMS

-   Additional fraction asymptomatic

-   Nominal fatality rate (infant, elderly)

-   Relative contagion of asymptomatics

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-   Total number of hospitalized cases (special)

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-   Fraction of transmission prior to clear symptoms (infectious
    asymptomatics)

-   Reproductive number

-   Disease stage duration (all stages)

In addition to looking at R^2^ within each scenario, the model can
identify other inferences on the relative importance of each parameter
by using the aggregate data for all 24 scenarios. IncR^2^ is the
increase in the R^2^ for a model with a single input, modeled linearly
for each scenario, above the R^2^ for a model fitting the average for
each of the 24 scenarios. The inference is similar to the primary
sensitivity analysis, with the 20 inputs being screened for variability
in the responses, which is explained by the scenarios being different
sets of data.

### Pandemic Model Comparison

A major limitation of models for pandemic influenza is the uncertainty
inherent in many of their assumptions. This uncertainty and the
differences in the assumptions used in each model make model comparison
difficult. However, one way of making sense of these uncertainties, in
addition to sensitivity analysis, is comparison of competing models.
Table 4-2 highlights several recent models and the EpiSimS Phase 2
analysis for pandemic influenza and the uncertainties included in each
study. Overall, the infectiousness of the influenza estimated by *R~0~*
was the most investigated uncertain parameter (ranging from 0.5 to 21).
Most models were in the range for *R~0~* of 2 to 3.5. In comparison,
this study investigated R~*0*~ up to 14.4 to examine where impacts begin
to degrade the critical infrastructure and public health. For runs where
R*~0~* is between 2 and 3.5, the average attack rate is 27.5 percent for
our baseline scenario (Figure 4-17), which compares favorably with other
published model attack rates for baseline scenarios. Three important
biological parameters can be identified from the results of all previous
pandemic influenza models: the *R~0~* that corresponds to the number of
secondary infections generated by primary infections in a susceptible
population, the disease generation or stage time that is the time
interval between one person and the people they infect, and the
proportion of transmission occurring prior to the onset of symptoms and
the related fraction of persons that are asymptomatic.[^56]

The most investigated mitigation strategies included vaccination,
antivirals, and social distancing (focusing on school closures).
Overall, the models are in agreement when using similar infectiousness
parameters. However, different mitigation results occur when the
biological parameters differ significantly. For example, school closure
was found to be effective by several studies, but was found to minimally
affect the overall attack rate by Ferguson and others.[^57]

[]{#__RefHeading___Toc178751245 .anchor}Table 4-2: EpiSimS phase 2 and
published simulation studies for\
pandemic influenza

+---------+-----------------+------------+-----------------------------+
| **      | **Mitigation    | **Uncer    | **Results**                 |
| Study** | Strategies**    | tainties** |                             |
+---------+-----------------+------------+-----------------------------+
| Carrat  | Vaccination;    | In         | Antiviral stockpile         |
| et al., | antiviral       | fectivity; | crucial; accelerated mass   |
| 20      | treatm          | susc       | vaccine development         |
| 06[^58] | ent/prophylaxis | eptibility | effective; school closures  |
|         |                 |            | effective                   |
|         | quarantine;     |            |                             |
|         | school closure  |            |                             |
+---------+-----------------+------------+-----------------------------+
| Cooper  | Reducing        | *R~0~* =   | Interventions to reduce     |
| et al., | transmission to | 0.5 to     | local transmission of       |
| 20      | simulate        | 3.5;       | influenza than air travel   |
| 06[^59] | mitigation      |            | restrictions                |
|         | impacts; air    | susce      |                             |
|         | travel          | ptibility; |                             |
|         | restrictions    | se         |                             |
|         |                 | asonality; |                             |
|         |                 | infectious |                             |
|         |                 | time       |                             |
+---------+-----------------+------------+-----------------------------+
| Stroud  | School          | Different  | Household quarantine        |
| et al., | closures;       | mitigation | effective; partially        |
| 20      | social          | strategies | effective pre-pandemic      |
| 07[^60] | distancing by   | and        | vaccine had minimal impact  |
|         | age;            | co         | at 20 million stockpile     |
|         | quarantine;     | mbinations | doses; antiviral stockpile  |
|         | antivirals and  |            | crucial; high-risk          |
|         | early and       |            | vaccination not more        |
|         | fully-effective |            | effective; fear-based       |
|         | vaccination     |            | isolation has great impact  |
|         |                 |            | on worker absenteeism       |
+---------+-----------------+------------+-----------------------------+
| F       | Targeted/mass   | *R~0~* = 1 | Socially targeted antiviral |
| erguson |                 | to 2; AV   | only effective for          |
| et al., | antiviral       | stockpile  | containment at low *R*~0~;  |
| 20      | prophylaxis;    |            | geographical prophylaxis    |
| 05[^61] | school closure; |            | required; combination of    |
|         | social          |            | social distancing and       |
|         | distancing      |            | antivirals required to      |
|         |                 |            | contain outbreak            |
+---------+-----------------+------------+-----------------------------+
| F       | Household       | *R~0~* =   | Travel restrictions only    |
| erguson | quarantine;     | 1.4 to     | delay pandemic; school      |
| et al., | travel          | 2.0;       | closure reduces peak attack |
| 20      | restrictions;   | incubation | rates by 40%, but little    |
| 06[^62] | school closure; | period     | impact overall; isolation   |
|         | antivirals;     |            | and quarantine effective;   |
|         | early vaccine   |            | mass antiviral effective;   |
|         |                 |            | early and partially         |
|         |                 |            | effective vaccine           |
|         |                 |            | significantly reduces       |
|         |                 |            | illnesses; multiple         |
|         |                 |            | combinations required       |
+---------+-----------------+------------+-----------------------------+

Table 4-2: EpiSimS phase 2 and published simulation\
studies for pandemic influenza (continued)

+----------+----------------+------------+----------------------------+
| *        | **Mitigation   | **Uncer    | **Results**                |
| *Study** | Strategies**   | tainties** |                            |
+----------+----------------+------------+----------------------------+
| Fraser   | Isolation of   | *R~0~* up  | Fraction transmission      |
| et al.,  | symptomatics;  | 21;        | prior to symptoms          |
| 2        | contact        | fraction   | important; in four-disease |
| 004[^63] | tracing and    | tr         | comparison, influenza      |
|          | quarantine     | ansmission | hardest to control using   |
|          |                | prior to   | isolation and quarantine   |
|          |                | symptoms;  | measures                   |
|          |                | isolation  |                            |
|          |                | parameters |                            |
+----------+----------------+------------+----------------------------+
| Gani et  | Antiviral      | Antiviral  | Antiviral stockpile for    |
| al.,     | treatment      | stockpile; | 20--25 percent of          |
| 2        |                | *R~0~* =   | population sufficient to   |
| 005[^64] |                | 1.28 to    | treat patients; for        |
|          |                | 2.0        | smaller antiviral          |
|          |                |            | stockpiles, targeting      |
|          |                |            | at-risk groups required    |
+----------+----------------+------------+----------------------------+
| Germann  | Targeted       | *R~0~* =   | Travel restrictions only   |
| et al.,  | antivirals;    | 1.6 to 2.4 | delay pandemic; early and  |
| 2        | early vaccine; |            | partially effective        |
| 006[^65] | school         |            | vaccine significantly      |
|          | closure;       |            | reduces illnesses;         |
|          | social         |            | antivirals effective at    |
|          |                |            | low *R*~0~; multi-strategy |
|          | distancing     |            | combinations required at   |
|          |                |            | high *R~0~*                |
+----------+----------------+------------+----------------------------+
| Glass et | Targeted       | *R~0~* =   | Targeted social distancing |
| al.,     | social         | 1.5 to 2.5 | can be effective without   |
| 2        | distancing;    |            | vaccination and            |
| 007[^66] | school         |            | antivirals. At low *R~0~*, |
|          | closure;       |            | school closure and child   |
|          |                |            | quarantine effective       |
+----------+----------------+------------+----------------------------+
| Haber et | School         | Contact    | School closing had little  |
| al.,     | closing;       | rate;      | impact on illnesses and    |
| 2        | isolation and  |            | deaths; isolation of ill   |
| 007[^67] | targeted       | Infec      | effective                  |
|          | quarantine;    | tiousness; |                            |
|          | long-term care |            |                            |
|          | facility       | withdrawal |                            |
|          | quarantine     | to home    |                            |
+----------+----------------+------------+----------------------------+
| Kli      | Contact        | Infectious | Iterative contacting       |
| nkenberg | tracing        | period     | tracing could be better    |
| et al.,  |                | length;    | for influenza              |
| 2        |                | time to    |                            |
| 006[^68] |                | detection  |                            |
+----------+----------------+------------+----------------------------+

Table 4-2: EpiSimS phase 2 and published simulation\
studies for pandemic influenza (continued)

  ----------- ----------------- ------------------- ----------------------------
  **Study**   **Mitigation      **Uncertainties**   **Results**
              Strategies**                          

  Longini et  Targeted and m    Time for detection  Vaccination of 80% of
  al.,        Table 4-2:                            children effective; targeted
  2004[^69]   EpiSimS phase 2                       antiviral use on a large
              and published                         scale effective (requiring
              simulation\                           1.9 billion antiviral doses)
              studies for                           
              pandemic                              
              influenza                             
              (continued)ass                        
              antiviral                             
              prophylaxis;                          
              vaccination                           

  Menon et    Antiviral         Attack rate;        25% attack rate exceeded
  al.,        treatment;        outbreak duration   United Kingdom present
  2005[^70]   critical care                         critical care capacity by
              services                              200 percent; antivirals
                                                    reduced hospitalization and
                                                    bacterial infections; mild
                                                    pandemics scenarios
                                                    overloaded critical care

  Wu et al.,  Quarantine;       Compliance          Combination of household
  2006[^71]   isolation;        household-          quarantine, isolation of
              antivirals;       interventions;      cases, and targeted
              contact tracing   *R~0~* 1.0 to 3.0;  antiviral prophylaxis most
                                transmission        effective; moderate-level
                                variables           quarantine compliance can be
                                                    effective.
  ----------- ----------------- ------------------- ----------------------------

Note: This figure is based on untransformed data.

[]{#__RefHeading___Toc179875417 .anchor}Figure 4-17: Scatter plot of the
overall attack rate against *R~0~* for the baseline (no interventions;
noSDnoAV, vaccination strategy 1); feasibility\
assessment for vaccines and antivirals

![](media/image32.wmf){width="6.0in" height="4.093055555555556in"}The
sensitivity analysis covers a broad range of stockpile sizes and of
doses distributed for vaccines and antivirals. In this section, the
feasibility of attaining the assumed ranges for vaccines and antivirals
is assessed by considering federal investments in pandemic readiness and
recent developments in vaccine and antiviral medicines and production
methods.

The full range of stockpile size and distribution for vaccines, assumed
in the sensitivity analysis, is feasible, though near the feasible
limit. This conclusion is subject to attaining the milestones in the
federal program. The conclusion for antivirals is similar, though less
dependent on dramatic developments and, again, subject to the timing of
a pandemic occurrence.

#### Vaccines

Each scenario includes distribution of a specific number of vaccine
doses. The cost can be calculated based on unit cost for production of
the pharmaceutical plus distribution cost. While cost may be a factor in
feasibility, this is primarily a budget issue. The focus of this study
is the practical limits to production and production capacity expansion.
Vaccine production is already a high-volume industry. The world's
largest manufacturer of vaccines, Sanofi Pasteur, has a worldwide annual
production of nearly 1 billion doses of vaccines, covering 20 diseases.
In 2006, global production capacity for seasonal influenza vaccine was
about 350 million doses per year.[^72] Recognizing this, it seems
feasible to develop a production capacity of up to 430 million
doses[^73] for a pandemic virus strain. This is especially true because
some seasonal flu vaccine production capacity might be reallocated to
production of pandemic influenza vaccine. However, the reality is more
complicated and introduces considerable risk; therefore, the capacity
needed for the U.S. program may not be realized.

A key difficulty in expecting to rely on this international production
capacity is that worldwide demand will exceed 6 billion courses.[^74]
Countries with production capacity are likely to commit to their own
needs first. Thus, the U.S. cannot rely on purchasing pandemic vaccines
from foreign sources[^75] and sufficient domestic capacity must be
built. Currently, only one vaccine production facility operates in the
U.S.,[^76] the Swiftwater, Pennsylvania, plant of Sanofi Pasteur.[^77] A
planned doubling of capacity at that plant will result in a capacity of
60 million doses of trivalent seasonal influenza vaccine (treats 3
separate strains).[^78] This suggests a current capacity of 30 million
doses of trivalent vaccine. The Center for Infectious Disease Research
and Policy (CIDRAP) reported that the estimated U.S. capacity for
influenza vaccine production was 50 million trivalent doses in
2004.[^79] As of this writing, a call to Sanofi Pasteur to resolve this
discrepancy has not yet been returned. The Center for Biosecurity of the
University of Pittsburgh also reports a U.S. domestic production
capacity of 50 to 60 million doses.[^80]

Japsen has reported that if the expanded capacity of 60 million doses
was used to produce a dedicated monovalent pandemic influenza strain, it
could produce 180 million doses (90 million courses) of a monovalent
pandemic flu vaccine.[^81] This estimate overlooks important health
policy and technical issues. From a health policy perspective, the need
for seasonal flu vaccine makes complete diversion of the production
capacity to pandemic flu vaccine very unlikely. Technically, differences
in production process, yield, and dosage make this simple calculation
significantly in error. The same error has been made by others in the
conversion of trivalent seasonal flu vaccine capacity to monovalent
pandemic flu vaccine capacity. Dr. Margaret Chan, Director-General of
the WHO, stated "For a trivalent pandemic vaccine, annual manufacturing
capacity is about 500 million doses. For a monovalent vaccine, this
figure increases to 1.5 billion doses."[^82]

More specifically, the antigen yield in egg cultures for the H5N1 virus
is very low[^83] and, at the same time, it appears that higher antigen
content is required for the pandemic vaccine than for the typical
seasonal flu vaccine. In addition, a 2-dose regimen is likely to be
required for successful immunization. To date, yields in egg cultures
have generally been about one-quarter to one-third of the yield obtained
with seasonal influenza strains with the best result of about
one-half.[^84] The correct multiplier for conversion of trivalent
seasonal flu vaccine capacity to monovalent pandemic flu vaccine
capacity is shown in Table 4-3. The relative yield is the ratio of
antigen yield for the pandemic vaccine to the antigen yield for the
seasonal vaccine. Trivalent seasonal vaccine doses currently contain 3
15-microgram (μg) doses of antigen, or 45 μg of antigen. Note that for
a\
2-dose regimen, these factors should be further divided by 2, as shown
in the subsequent table.

Table 4-3 shows multipliers for conversion from seasonal to pandemic
vaccine capacity assuming a single dose regimen.

[]{#__RefHeading___Toc178751248 .anchor}Table 4-3: Conversion factors
from trivalent seasonal flu vaccine to monovalent\
pandemic flu vaccine capacity

  -------------- ---------------- ----------- ----------- ----------- -----------
  **Relative     **Antigen Dose                                       
  Yield**        (micrograms)**                                       

                 **15**           **30**      **45**      **60**      **90**

  1.00           3.0000           1.5000      1.0000      0.7500      0.5000

  0.75           2.2500           1.1250      0.7500      0.5625      0.3750

  0.50           1.5000           0.7500      0.5000      0.3750      0.2500

  0.33           1.0000           0.5000      0.3333      0.2500      0.1667

  0.25           0.7500           0.3750      0.2500      0.1875      0.1250
  -------------- ---------------- ----------- ----------- ----------- -----------

> Source: Argonne National Laboratory

Table 4-4 shows multipliers for conversion from seasonal to pandemic
vaccine capacity assuming a 2-dose regimen.

[]{#__RefHeading___Toc178751249 .anchor}Table 4-4: Conversion factors
from seasonal to pandemic vaccine,\
assuming a 2-dose regimen

  -------------- -------------- ----------- ----------- ----------- -----------
  **Relative     **Antigen                                          
  Yield**        Dose,                                              
                 micrograms**                                       

                 **15**         **30**      **45**      **60**      **90**

  1.00           1.5000         0.7500      0.5000      0.3750      0.2500

  0.75           1.1250         0.5625      0.3750      0.2813      0.1875

  0.50           0.7500         0.3750      0.2500      0.1875      0.1250

  0.33           0.5000         0.2500      0.1667      0.1250      0.0833

  0.25           0.3750         0.1875      0.1250      0.0938      0.0625
  -------------- -------------- ----------- ----------- ----------- -----------

> Source: Argonne National Laboratory

On 17 April 2007, the FDA approved a Sanofi Pasteur H5N1 vaccine, the
first such vaccine to attain that approval. The manufacturer had already
contracted with HHS to manufacture and stockpile this vaccine. To
achieve the demonstrated effectiveness,[^85] it was necessary to provide
2 doses of 90-μg hemagglutinin (H) antigen. The doses were given 28 days
apart. Given that Sanofi Pasteur has reported an H yield of about 50
percent of that achieved for seasonal vaccines, the correct multiplier
for conversion from seasonal influenza vaccine capacity to pandemic
vaccine capacity is 0.125. Thus, if the current U.S. capacity of 60
million trivalent doses was converted to production of monovalent
pandemic vaccine, the capacity would be 7.5 million courses. If the same
technology was used to provide a 300-million-course pandemic influenza
vaccine in a 12-month production run, the required capacity would be 2.4
billion doses per year.

In response to the shortfall in domestic supply, the federal government
is supporting a series of efforts to expand production capacity and
improve vaccine performance. These include sponsoring the Swiftwater
plant expansion, refurbishing former vaccine manufacturing capacity,
enhancing egg production systems, and developing advanced manufacturing
techniques for vaccines. This federal plan is the key to feasibly
providing vaccine at the rates assumed in the mitigation scenarios.
Without federal funding, the required expansion in production capacity
would not take place and necessary technical improvements would be
delayed or not achieved at all. The capacity goals for the federal
program are listed in Table 4-5.

[]{#__RefHeading___Toc178751250 .anchor}Table 4-5: The Health and Human
Services pandemic\
medical countermeasure goals for vaccines[^86]

  --------------- -------------------------------------------------------
  Vaccine Goal #1 To establish and maintain a dynamic pre-pandemic
                  influenza vaccine stockpile for 20 million persons (at
                  2 doses per person) based on the H5N1 virus

  Vaccine Goal #2 To provide pandemic vaccine to all U.S. citizens within
                  6 months of a pandemic declaration (600 million doses
                  of pandemic vaccine)
  --------------- -------------------------------------------------------

If Vaccine Goal #1 is achieved with the current vaccine technology, the
equivalent seasonal vaccine capacity required would be 20 million/0.125
= 160 million doses per year based on the multiplier analysis. This is 3
times the current U.S. capacity and it must be available over and above
the existing capacity for seasonal vaccine. The sensitivity studies
include cases with stockpiles ranging up to 120 million doses, or 60
million courses; that is, 3 times the federal stockpile goal. The
feasibility of a stockpile exceeding the federal goal is very low, at
least in the near term. The federal goal is to stockpile 20 million
courses (40 million doses) of pre-pandemic vaccine by 2009.[^87] This
seems feasible because 14 million doses had been accumulated by January
2007.[^88] In summary, the feasible stockpile level currently is the
existing 14 million doses, and the feasible stockpile level by 2009 is
the federal goal of 40 million doses.

A higher level of pre-pandemic stockpile implies a change in the federal
goals and will only be achieved at a later date using new technologies,
some of which are currently under development. In the near term,
antigen-sparing techniques could be employed to reduce the amount of
antigen required per dose. There are both technical and strategic means
of antigen sparing under investigation. The technical means involve the
use of an adjuvant, which increases the level of immune response for a
given amount of antigen. The strategic approach involves priming the
population by giving doses of pre-pandemic vaccine as it becomes
available. There is some evidence that a primed population has a
stronger immune response to new, related influenza strains. Such a
primed population may only need a single dose from the pre-pandemic
vaccine stockpile when it is released at the onset of a pandemic. While
this does not increase the size of the stockpile, it may double the size
of the population that the stockpile can serve.

The use of an adjuvant can actually increase the number of doses in the
stockpile because the adjuvant reduces the amount of antigen required to
produce an immune response. Alum is used as an adjuvant in many
vaccines.[^89] In a trial with 51 volunteers, the addition of alum to a
Sanofi Pasteur H5N1 vaccine allowed a reduction of antigen from 90 μg to
30 μg with a somewhat improved immune response; 2 doses were still
required.[^90] Because antigen production is the limiting step in
vaccine production, this approach would triple the number of doses that
could be stockpiled from a given production capacity. Because the
federal plan is to provide a stockpile of 20 million courses (40 million
doses), based on current technology, the use of an adjuvant could extend
the stockpile to 120 million doses.[^91] Other trials have demonstrated
a strong immune response with antigen levels as low as 3.8 μg.[^92]
GlaxoSmithKline has achieved a 10-fold reduction in antigen requirements
using a proprietary adjuvant.[^93] While these results are from clinical
trials, there is consistency in results from several manufacturers.
Using the adjuvant technology, it is feasible that the intended federal
investment for a 40-million-dose stockpile could result in sufficient
production capacity to produce a 120-million-dose or greater stockpile
of pre-pandemic vaccine. Thus, the full range of stockpile sizes assumed
in the sensitivity studies is deemed feasible. However, a change in the
federal strategy that extends pre-pandemic vaccine production to a
broader population will be required. Also, additional investment in the
formulating, filling, and packaging of vaccines will be required to
increase capacity even if the adjuvant extends the effective capacity of
antigen production. Testing for optimal levels, type, and safety of
adjuvant may add some time to the creation of this larger stockpile,
perhaps moving the federal timetable from 2009 to 2010.

Because the creation of a pre-pandemic stockpile is a near-term goal,
vaccine production for the stockpile has emphasized the current
commercial egg-based antigen production technology. Antigen production
in cell cultures has been used for other vaccine and pharmaceutical
manufacturing, but has not been commercially established for flu
vaccine. Cell culture production is faster and more flexible than
egg-based production. Furthermore, it does not require the planned
availability of millions of specially prepared eggs. However,
historically, the yield of human influenza virus on tissue cultures has
been far lower than that in chicken embryos. Solving this problem was
already identified in 1994 as a key to pandemic readiness. At that time
in a short article in the *European Journal of Epidemiology*, Youri
Ghendon of the World Health Organization (WHO) called for the
identification of an "egg-independent" production technology.[^94]
Ghendon suggested that production on tissue culture could solve the
problems associated with a large increase in vaccine production. Between
fiscal years 1998 and 2005, the U.S. National Institute of Infectious
Diseases provided \$35 million in support of cell culture technology. In
2005, Sanofi Pasteur was awarded a \$97-million,\
5-year contract with HHS to develop cell-based technology for production
of an influenza vaccine with the intention of obtaining FDA approval. By
September 2006, Sanofi Pasteur had completed a clinical trial of such a
vaccine and demonstrated production-scale potential in a 20,000-liter
bioreactor. Chiron and Novartis have also completed successful clinical
trials of cell-based flu vaccines.[^95]

On 26 April 2007, the European Union (EU) Committee for Medicinal
Products for Human Use (CHMP) recommended that the cell-cultured
Novartis vaccine, Optaflu, a seasonal flu vaccine, be authorized by the
European commission. A decision normally follows within 3 months and
usually honors the CHMP recommendation. The Influvac cell-based flu
vaccine from Solvay Pharmaceuticals was approved for sale in The
Netherlands by the EU in 2001. However, the company has not yet
introduced it to the market.[^96] Novartis expects to apply for U.S.
licensing of its technology in 2008.[^97] Depending on the timing of FDA
approval, Novartis could have a cell-based production facility in full
production by 2012. In part, this facility would rely on an HHS grant of
\$220 million, which was committed in May 2006. Considering this status,
it seems that the long-held goal of producing a cell-cultured vaccine
for seasonal influenza is within reach. These developments will not have
an immediate impact on Vaccine Goal #1, the 20-million-course stockpile
of pre-pandemic vaccine. However, in the years following 2012, they may
be the technology of choice for maintaining that stockpile.

Vaccine Goal #2 is to provide pandemic vaccine to all U.S. citizens
within 6 months of a pandemic declaration. Nominally, this federal goal
is equivalent to 300 million courses (600 million doses).[^98] Because
this is the pandemic vaccine, it will not be immediately available upon
declaration of a pandemic. In fact, using current (egg-based) production
methods, 5 to 8 months is required for full-scale production once a
virus strain has been identified. This alone, seems to make the federal
goal infeasible in the near term. The simulations performed for this
analysis allow for such a delay, supplying vaccine on a more realistic
timetable. The federal plan is to develop cell-based culture technology,
which can produce commercial quantities of vaccine within 3 months of
the virus identification. Specifically, the federal plan is to rely on
egg-based production for 60 million courses and on cell-based production
for 240 million courses. However, it is unknown whether the cell-based
technology can be successfully scaled up and applied to a pandemic flu
vaccine. Current projections are that it should be demonstrated on a
commercial scale by 2009 and that the desired capacity will be on line
between 2011 and 2013. If this technology does not work or if the
pandemic occurs before adequate cell-based production capacity is in
place, only the egg-based capacity can be used. As demonstrated above,
an extraordinary base capacity of 240 million doses of seasonal vaccine
must be on hand to produce 60 million courses of the current H5N1
vaccine. Without technological advances, meeting the federal goals for
egg-based capacity is not feasible. However, the adjuvant technologies
and the cell-based technologies are progressing rapidly.

The goal of delivering this vaccine within 6 months of identification of
the pandemic is a significant challenge. In the low feasibility case,
only egg-based technology is available and it will take 6 months to
develop commercial quantities of the vaccine. In the high feasibility
case, 20 percent of the vaccine is still egg-based, and 80 percent of
the vaccine will not be available until at least the fourth month. This
means that distribution of 240 million courses would have to occur in 3
months, which would require an annual capacity of 920 million doses.
This is the case because there is no stockpile of pandemic vaccine. Any
stockpiled vaccine is a pre-pandemic vaccine that, at best, will be only
partially effective. There seems to be a divergence between the policy
goal and implementation limits. The simulation scenario assumptions
allow for a more realistic timetable for vaccine distribution.

The sensitivity runs include cases with a range of units dispensed
(doses given) up to 420 million in an 8-month period. This corresponds
to an annual capacity of 630 million units. The federal program goal is
to establish a cell-based production capacity of 475 million doses by
2013.[^99] This, in conjunction with egg-based capacity and
antigen-sparing technology, should make the 420-million-dose scenarios
feasible. A study by a University of Michigan team[^100] has found that
there is sufficient bioreactor capacity in the U.S. now to produce 600
million doses of pandemic flu vaccine in a 4-month period. They
recommend a training program for existing bioreactor production teams to
ready them for producing the vaccine.[^101] The authors believe this is
the best strategy to respond to a pandemic within the next 5 years. This
analysis suggests that both the federal Vaccine Goal #2 and the
sensitivity run range of assumptions are feasible near term. However,
the author gives the caveat that a government subsidy would be required
to cover training and down-time costs for the existing bioreactor
capacity. Furthermore, the profits from flu vaccine are likely to be low
relative to those from other products produced by the bioreactor
capacity. In addition, FDA approval has not yet been obtained for any
cell-based H5N1 vaccine and some allowance must be made for the other
pharmaceuticals normally produced in the reactor facilities. This
strategy would require a substantial revision of the federal program,
which seems unlikely to occur in advance of a pandemic.

##### Vaccine Costs

Until recent shortages in seasonal flu vaccines, the cost per dose was
roughly \$1.60.[^102] In October 2006, the WHO reported a cost range of
\$3 to \$7 per dose.[^103] Current costs in the U.S. are provided in the
following CDC table (Table 4-6). These prices include packaging, but
none of them include the cost of distribution, which is discussed below.

[]{#__RefHeading___Toc178751251 .anchor}Table 4-6: Influenza vaccine
prices

  ------------- ------------------- --------------- ------------- ------------- ------------ ------------------
  **Vaccine**   **Brand name/ Trade **Packaging**   **CDC\        **Private     **Contract   **Manufacturer**
                name**                              Cost/Dose**   Sector\       End Date**   
                                                                  Cost/Dose**                

  Influenza     Fluzone®            10-dose vials   \$10.153      \$11.198      2/28/08      Sanofi Pasteur
  ^a,b^\                                                                                     
  (Age 6 months                                                                              
  and older)                                                                                 

  Influenza     Fluzone®\           10-pack -\      \$12.77       \$13.613      2/28/08      Sanofi Pasteur
  ^b^\          Pediatric dose      1-dose syringes                                          
  (Age 6--35    Preservative-free                                                            
  months)                                                                                    

  Influenza     Fluzone®\           10-pack -\      \$13.751\     \$14.663\     2/28/08      Sanofi Pasteur
  ^b^\          No-preservative     1-dose          \             \                          
  (Age 36                           syringes\       \$13.751      \$14.663                   
  months and                        10-pack--\                                               
  older)                            1-dose vials                                             

  Influenza     Fluvirin®           10-dose vials   \$10.16       \$12.48       2/28/08      Chiron
  ^a,b^\                                                                                     
  (Age 4 years                                                                               
  and older)                                                                                 
  ------------- ------------------- --------------- ------------- ------------- ------------ ------------------

Table 4-6: Influenza vaccine prices (continued)

  ------------- ---------- --------------- ------------- ------------- ------------ ------------------
  **Vaccine**   **Brand    **Packaging**   **CDC\        **Private     **Contract   **Manufacturer**
                name/                      Cost/Dose**   Sector\       End Date**   
                Trade                                    Cost/Dose**                
                name**                                                              

  Influenza     Fluarix™   5-pack--\       \$12.00       \$13.25       2/28/08      GlaxoSmithKline
  ^b^\                     1 dose syringes                                          
  (Age 18 years                                                                     
  and older)                                                                        

  Influenza     FluMist®   10-pack--\      \$17.65       \$17.95       2/28/08      MedImmune
  ^b^\                     1-dose sprayers                                          
  Live,                                                                             
  Intranasal                                                                        
  (Age 5--49                                                                        
  years)                                                                            
  ------------- ---------- --------------- ------------- ------------- ------------ ------------------

Notes:

^a^Vaccines that contain Thimerosal as a preservative

^b^Vaccine cost includes \$0.75 per dose Federal Excise Tax

CDC = U.S. Centers for Disease Control and Prevention

These current and historical vaccine prices all apply to seasonal
influenza. It is naïve to assume that they would apply directly to
future costs of pre-pandemic and pandemic vaccines because of
differences in production technologies, as described above, and because
of the interplay of supply and demand along with government intervention
in the market. The price of a vaccine reflects research and development,
production, and regulatory compliance. Research and development costs
are normally embedded in the intellectual property value, which is
protected by patents and trade secrets. Patent protection and limited
licensing can certainly result in higher prices. Some vaccine examples
show a drop of more than 90 percent in prices following the defeat of a
patent through litigation or through new technology. Also, the agreement
on Trade-Related Aspects of Intellectual Property Rights (TRIPS) will
extend minimum universal intellectual property rights to developing
countries. This will strengthen the hand of property rights holders.
However, developing countries have a strong interest in maintaining
access and low prices. Also, exclusive licenses do not apply to many new
vaccines, which rely on technology in the public domain.[^104]
Furthermore, much of the research on pandemic vaccines is supported by
the federal government in the U.S. and other governmental agencies in
Europe and in Asia. As a condition of that support, public access to the
technology should be assured.

Production, the next element of costs, is likely to decline relative to
current vaccine production methods because of the large increase in
scale associated with pandemic preparedness and because of the presumed
shift to new, more efficient production technologies. The final element
of costs, regulatory compliance, principally involves clinical trials as
required for government licensing. These costs are likely to increase
for pandemic and pre-pandemic vaccines because population
characteristics worldwide will have to be taken into account through
broader trials. In addition, regulatory authorities in the U.S. and in
Europe are likely to require broader Phase III trials and may formalize
Phase IV studies of safety after marketing has begun. Clinical trials
were expected to account for 45 percent of drug development research and
development budgets in 2003.[^105]

In conclusion, the broad government support for pandemic and
pre-pandemic vaccines is likely to blunt the tendency of intellectual
property rights to increase prices. The large scale of this production
endeavor may also contribute to favorable economics, though demand will
be high. Most of the cost of clinical trials (regulatory compliance)
would be borne by the government. We, therefore, assume costs in the
range of \$3 to\$7, as currently reported by the WHO, for seasonal flu
vaccine.

##### Vaccine Distribution Costs

The cost of mass vaccination campaigns is not limited to the price of
the medication. Distribution will be equally, if not more, expensive on
a per-person-treated basis. Responsibility for mass vaccination
preparedness spans the levels of government from the federal to local
level. Most counties and large municipalities have begun the process of
updating plans based on potential terrorist events and combining them
with annual flu clinic plans, to be prepared for a pandemic disease.
Required resources include large-scale logistical support in materiel
and warehousing, general medical supplies, information technology, and
most importantly highly skilled and trained manpower in the form of
doctors, nurses, and emergency medical technicians.

The duration of a campaign is the variable that contributes most to mass
vaccination campaign costs (Table 4-7). Because the largest cost of
distribution is wages, the time employees will be on the books and how
that time is managed to deal with overtime is the single greatest factor
in calculating potential cost. The figures below are derived from
internal planning documents from the Chicago Metropolitan Area. Key
assumptions are

-   The majority of paid personnel are hourly, and a portion of the
    hours worked at mass clinics will be overtime

-   Mass clinics will be conducted in public school buildings with costs
    based on the most recent energy and maintenance cost (2005)

-   Transport of vaccine from a central point to the state based on
    overnight shipping costs for palletized freight, intrastate transit
    based on mileage driven per vehicle used

-   Well-run clinics are capable of completing a mass vaccination
    campaign in between 2 and 3 days (this represents 40 vaccinations
    per hour per vaccinator)

-   If clinic throughput is significantly less than what is required to
    meet a 3-day goal, the per-dose costs would increase.

[]{#__RefHeading___Toc178751252 .anchor}Table 4-7: Vaccination campaign
costs

  ------------------------- ------------- -------------- ----------- ---------------------
  Cost Factor                             **Population   **Cost per  **Total per Cost
                                          Size**         Vaccine**   Factor**

  **Campaign Cost**                                                  

  Personnel cost                          2,800,000      \$8.02      \$22,460,461.35

  Building cost                           2,800,000      \$0.13      \$366,092.77

  Transportation                          2,800,000      \$0.01      \$34,601.73

  **Total campaign cost**                 2,800,000      \$8.16      **\$22,861,155.84**

  **Preparation Cost**                                               

  Consumable supplies                     2,800,000      \$0.01      \$26,023.77

  Capital                                 2,800,000      \$0.07      \$187,443.18
  supplies/investment                                                

  Contractual                             2,800,000      \$0.04      \$110,910.00

  **Total preparation                     2,800,000      \$0.12      **\$324,376.95**
  cost**                                                             

  **Total cost**                          2,800,000      \$8.28      **\$23,185,532.79**
  ------------------------- ------------- -------------- ----------- ---------------------

Note: Costs are based on a single dose vaccination. Should a booster be
needed, the total cost of the campaign would double.

#### Antivirals

The sensitivity analysis evaluates cases with a very broad range of
assumptions for the production and distribution of antivirals. For some
cases, the annual production implied by the rate of distribution for an
8-month campaign exceeds 1.7 billion courses. These cases also
incorporate a range of antiviral stockpile sizes, though that range is
more narrowly prescribed and remains within the limits of current U.S.
pandemic flu plans; that is, up to 81 million courses. As in the case of
vaccines, the feasibility of attaining the stockpile sizes and
production rates assumed in the simulation is closely tied to the
federal program goals. The antiviral goals for that program are

-   To provide influenza antiviral drug stockpiles for pandemic
    treatment of 25 percent of the U.S. population; that is, 75 million
    treatment courses

-   To provide influenza antiviral drug stockpiles for strategic limited
    containment at the onset of a pandemic[^106]

By FY 2008 (October 2007), a 20-million-course stockpile should be in
place for the protection of healthcare workers and first responders, and
a 24-million-course stockpile should be in place for treatment of
pandemic flu. Also in 2008, a 6-million-course stockpile will be
established for containment of 1 or 2 local outbreaks. This is a total
of 50 million courses. The federal plan provides for a 24-percent
subsidy of state stockpiles, totaling 31 million courses. These
stockpiles also are to be completed in 2008. As of January 2007, 43
states had committed to a total purchase of 30.6 million courses, with
11 million already on order.[^107] On the basis of these considerations,
it certainly seems feasible to achieve the stockpile assumptions
implicit in the sensitivity runs, though not before 2008.

The conclusion on stockpile feasibility is bolstered by the fact that
Roche has increased its global production capacity to 400 million
courses[^108] with the recent addition of an 80-million-course
production capability in the U.S., which was expected to be fully
operational by the end of 2006. The current 400-million-course capacity
represents a 10-fold increase over the 2004 capacity.[^109] The federal
plan for antivirals does not include a specific goal for domestic
production capacity. The newly established capacity of 80 million
courses is more than sufficient to maintain the 81-million-course
stockpile, because the antivirals in the stockpile have a 5-year
shelf-life. That is, only 20 percent or 16 million courses will expire
per year.

The feasibility of a production capacity of over 1.7 billion courses
seems extremely unlikely, given a current domestic capacity of only 80
million. Given that Roche has increased capacity 10-fold since 2004 to
the current total of 400 million courses, it may be feasible to increase
global capacity to 1.7 billion courses over perhaps a decade. However,
in the absence of specific international government plans for a major
capacity expansion, this is unlikely. An alternative is to provide a
larger stockpile. The sensitivity runs examine the implications of
courses given over about an 8-month campaign. The necessary courses can
be obtained from a stockpile and from current production rather than
just from current production. The relative contribution from current
production declines as time permits the building of a larger stockpile.
If accumulation of the larger stockpile begins after completion of the
mandated 81-million-course stockpile and continues for 5 years, this
larger stockpile will be created during the years from 2009 through
2013. The maximum distribution for an 8-month campaign is about 1.2
billion courses. For distribution in the fifth year of production, 80
percent would be derived from the stockpile and 20 percent from current
production. This would require an average production capacity of about
240 million courses per year. This is less than the current global
capacity, 90 percent of which was developed from 2004 through 2006. On
this basis, it is surely feasible to develop domestic capacity
sufficient to stockpile antivirals for even the broadest distribution
anticipated in the sensitivity cases. However, this strategy would
require federal support and a change in the pandemic planning goals.

##### Antiviral Costs

The HHS has secured contract prices through the end of 2007 of \$19.32
for Tamiflu and \$21.72 for Relenza. Future prices are a matter of
speculation, because they will reflect both economies of scale
associated with rapidly increasing production and increases in demand as
developed and developing countries try to stockpile antivirals in
anticipation of a pandemic. Competition may also challenge current
prices if Peramivir, a new antiviral that is just entering clinical
trials, is successful. Given these uncertainties, it is assumed that the
current contract prices are representative of future antiviral prices.

##### Antiviral Distribution Costs

The process for distributing antivirals through mass dispensing centers
is not dissimilar to mass vaccinations. Due to there being less labor
involved in handing out orally consumed medication and the practice
completed in terrorism response exercises, the goal for completion of an
oral antiviral campaign is cut to 2 days (Table 4-8).

[]{#__RefHeading___Toc178751253 .anchor}Table 4-8: Antiviral drug
distribution campaign costs

  -------------------------------------- -------------- ------------- ---------------------
  Cost Factor                            **Population   **Cost per    **Total per Cost
                                         Size**         Antiviral**   Factor**

  **Campaign Cost**                                                   

  Personnel cost                         2,800,000      \$3.95        \$11,047,538.28

  Building cost                          2,800,000      \$0.06        \$179,444.40

  Transportation                         2,800,000      \$0.01        \$34,601.73

  **Total campaign cost**                2,800,000      **\$4.02**    **\$11,261,584.41**

  **Preparation Cost**                                                

  Consumable supplies                    2,800,000      \$0.01        \$26,023.77

  Capital supplies/investment            2,800,000      \$0.07        \$187,443.18

  Contractual                            2,800,000      \$0.04        \$110,910.00

  **Total preparation cost**             2,800,000      **\$0.12**    **\$324,376.95**

  **Total cost**                         2,800,000      **\$4.14**    **\$11,585,961.36**
  -------------------------------------- -------------- ------------- ---------------------

## Decision Model

### Methodology

The decision problem involves choosing, under conditions of great
uncertainty and risk, the preferred intervention strategy to protect
human health and the economy against the possibility of an influenza
pandemic with an optimum expenditure of public funds. In making choices
between alternative intervention strategies, decision makers are
concerned with minimizing the combined impact on human fatalities,
illnesses, and costs to the economy, including the cost of the
intervention strategy. An important benefit of the decision model is the
understanding of which intervention strategy, including no intervention,
is the preferred alternative to pursue, given information and judgment
about the likelihood of an influenza pandemic and the values placed on
statistical fatalities and illnesses relative to national economic
impacts. Because there is large uncertainty in the possible conditions
that may characterize an influenza pandemic and the mitigation of
available intervention strategies, it is also important to understand
that a lesser preferred alternative might actually result in lower
impacts than a more preferred alternative if a pandemic occurs. This is
a result of uncertainty in the possible outcomes of an event and, if the
uncertainty is judged to be too great, might suggest that further
research or analysis be undertaken to reduce that uncertainty.

Table 4-9 shows the value structure, or indifference equivalencies,
between the decision metrics. It is common to express these tradeoffs in
terms of an economic measure.[^110] That is, for this analysis, an
increase in economic costs of \$5 million has the same influence on the
preference calculation as 1 additional influenza-related fatality. One
additional case of illness due to pandemic influenza has the same
influence as (is equivalent to) an increase of \$10,000 in economic
costs or an additional \$10,000 to adopt and implement an intervention
strategy. The indifference equivalency ascribed to influenza-related
fatalities, \$5 million, is consistent with the value of human life used
in a majority of historical federal policy studies concerning safety
measures, environmental health impacts, and medical interventions, which
generally adopt values ranging from \$5 to \$6.5 million. Because value
of fatalities tends to dominate the choice of intervention strategy in
the decision model, choosing a value of life over a wide range around
\$5 million; that is, \$2 to \$10 million, has no effect on the order in
which the strategies would be preferred.

[]{#__RefHeading___Toc178751254 .anchor}Table 4-9: Value tradeoffs
(indifference equivalencies) among decision metrics

  ----------------------- ----------------------- -----------------------
  Decision Metric         Standard Units          Equivalent Economic
                                                  Cost

  Cost of intervention    \$1 M                   \$1 M

  Economic costs (lost    \$1 M                   \$1 M
  GDP)                                            

  Statistical fatalities  1 influenza-related     \$5 M
                          death                   

  Non-fatal illnesses     1 influenza illness     \$0.01 M
  ----------------------- ----------------------- -----------------------

> Notes: GDP = gross domestic product; M = million

The model derived the value ascribed to an influenza illness from the
fatality value assuming the average fatality shortened life by 35 to 40
years and the average length of an influenza illness from infection to
full recovery ranged from 25 to 30 days. The indifference equivalency
reflects the ratio of days lost to illness to those lost as a result of
influenza-related death.

In addition to the value tradeoffs between the metrics, attitude toward
risk can be an important consideration that can influence the choice of
a preferred intervention strategy. For situations that involve making
choices between alternatives that present only undesirable outcomes
under conditions of uncertainty, risk attitude describes the propensity
to tolerate or avoid the chance of incurring outcomes that are worse
than the expected value of all of the uncertain outcomes. A decision
maker's risk attitude is measured by having him or her consider
propositions having uncertain outcomes and specifying a "certain
equivalent" for a proposition that is as desirable as the uncertain
proposition.

For example, consider a decision maker who is faced with a proposition
that is equally likely to result in a \$200 million cost or a
\$100 million cost. The expected cost of the proposition is \$150
million, but the outcome is uncertain: it could be either \$100 million
or \$200 million. A decision maker who is willing to accept a certain
cost that is greater than the expected outcome to avoid the proposition
is said to be "risk averse." That is, a risk-averse decision maker would
seek to avoid the chance of incurring the \$200 million cost outcome by
accepting for certain the expected value and a "premium" over the
expected value of the outcomes. The maximum amount of "premium" the
decision maker is willing to accept to avoid the uncertainty is a
measure of the degree of risk aversion.

Alternatively, a decision maker who is willing to accept a maximum
certain cost that is equal to the expected cost is said to be "risk
neutral." In this summary, the results relate only to the risk-neutral
attitude. The influence of different risk attitudes, from highly risk
tolerant to highly risk averse, was explored in this study and found not
to change the order of preference for the alternative intervention
strategies. Attitude toward risk does have an influence on the relative
preference value over the range of likelihood of the pandemic occurring,
but the nature of uncertainty in the scenarios and other characteristics
of the scenarios are such that the order of preference remains
unchanged.

### Aid to Decision Making

The model used to compute the relative preference of alternative
intervention strategies is based on multi-attribute decision theory and
was developed specifically as an aid to making decisions under
conditions of uncertainty and risk. CIPDSS applied this model to help
decision makers integrate the large amount of quantitative
information[^111] and insights generated by the consequence simulations
into a single measure of relative merit that informs the decision
maker's comparison of available alternative intervention strategies. The
relative preference model is combined with an analysis model that
calculates the level of confidence in choosing any one intervention
strategy over another and quantifies maximum and expected levels of
satisfaction and regret, assuming the pandemic occurs.

The decision model features the following primary benefits:

-   Adaptability to the value structure of the decision maker, or
    decision-making organization, through the value tradeoffs or
    indifference equivalencies ascribed to the decision metrics (Table
    4-9 lists the value tradeoffs used in this study.)

-   Explicit accounting for and addressing the decision maker's or the
    decision-making organization's attitude toward risk by scaling the
    contribution of each consequence drawn from uncertainty
    distributions resulting from OA-based LHS simulation results in
    accordance with the decision maker's propensity for tolerating or
    avoiding risk

-   The relative preference of the alternative intervention strategies
    is based on an expected value that accounts for the cost of the
    strategy in lieu of the occurrence of a pandemic, the cost and
    mitigation value of the intervention strategies in the event that
    the pandemic does occur, and the likelihood of a pandemic occurring
    sometime in a definable future. For this analysis, CIPDSS set the
    time horizon to 5 years. The model plots relative preferences for
    the alternative intervention strategies as a function of the
    likelihood of a pandemic occurring anytime during this period.

-   Measures of confidence in selecting one intervention strategy over
    another and measures of expected satisfaction and regret can be
    computed for sets of alternative intervention strategies. These
    measures help to better understand and visualize the value of
    choosing one strategy over another and provide an additional
    dimension that is important to some decision makers, but that is not
    otherwise apparent in the utility theory model.

The choice of a 5-year horizon was somewhat arbitrary, but it only
influences the costs of stockpiling vaccines and antiviral medications
because of the limited shelf life of these items. The choice of a longer
time horizon would affect the analysis by marginally increasing the cost
of intervention strategies involving vaccine and/or antiviral stockpiles
by the additional stock replacement costs associated with the longer
time horizon. Because these costs are generally very small compared to
the consequence of a pandemic, the conclusions of the analysis do not
change if a longer time horizon is considered.

### Decision Model Results

#### Relative Preference of Alternative Intervention Strategies

The team determined the relative preference of each intervention
strategy from the uncertainty analysis runs, and the results of those
calculations are displayed in Table 4-10 for 2 endpoints. The first
endpoint assumes that all stockpiling of partially effective vaccines
and/or antivirals is completed, but that a pandemic has not occurred.
All intervention strategies have their highest relative preference value
at this endpoint because only the costs of stockpiles and other
preparatory costs are included. The relative preference values between
the intervention strategies at this endpoint reflect only the difference
in stockpile and other preparation costs, with the higher cost
strategies producing lower preference values.

[]{#__RefHeading___Toc178751255 .anchor}Table 4-10: Relative preference
and equivalent measures for pandemic\
influenza intervention strategies

![](media/image33.wmf){width="6.499305555555556in"
height="3.917361111111111in"}

Notes: AV = antiviral; SD = social distancing

The second endpoint is under the condition that a pandemic has occurred
following expenditure of all preparation and stockpiling costs.
Intervention strategies will have their lowest relative preference
values at this endpoint because the relative preference is based on the
full set of consequences resulting from the pandemic: illnesses,
fatalities, economic losses, and implementation costs associated with
the intervention strategy, in addition to the preparation costs. The
relative preference values between the intervention strategies at this
endpoint reflect all the consequences associated with a pandemic. Again,
higher overall consequence strategies produce lower preference values.

By using the indifference equivalencies from Table 4-9, analysts can
convert the relative preference values in Table 4-10 to equivalent
physical measures such as the equivalent costs and equivalent fatalities
as are presented in Table 4-10. It is often easier for decision makers
to think about the difference between alternative intervention
strategies in terms of these more familiar units than a dimensionless
relative preference value, which is arbitrarily set to a 0 to 1 scale.
Therefore, CIPDSS uses ***equivalent fatalities*** in the remainder of
this section to discuss the relative preference of the intervention
strategies.

Figure 4-18 shows the relative preference of all intervention strategies
without social distancing compared to the baseline, or no intervention,
scenario. The solid lines represent those intervention scenarios that
also exclude the use of antivirals, and the dashed lines represent the
corresponding vaccination strategies with the use of antivirals. Two
measures of preference are used on the y-axis. The left y-axis indicates
relative preference in a dimensionless "utility" value over the range of
1 to 0, where the best value (1) corresponds to no impacts and the worst
value (0) corresponds to a state that would have a total equivalent cost
of \$173 trillion,[^112] which is only slightly larger than the worst
simulation run. The right y-axis shows the equivalent scale in millions
of expected fatalities, where the best value (0) corresponds to no
fatalities and the worst value corresponds to a state that would have a
total of 34.6 million[^113] equivalent fatalities.

[]{#__RefHeading___Toc179875418 .anchor}Figure 4-18: Relative preference
of intervention strategies over the likelihood of a pandemic
(risk-neutral profile \[excludes social distancing scenarios\])

The relative preference for all of the intervention strategies is a
function of the likelihood that a pandemic will occur within 5 years. If
there were a zero probability of a pandemic occurring, then the only
cost would be the preparation costs associated with stockpiling vaccines
and antivirals (that would never be used). If a pandemic were to occur,
then the costs would also include the value of illnesses and lost lives,
pandemic-related medical costs, economic value lost due to worker
absenteeism, and additional intervention measure implementation costs.
This state is represented by the 100-percent likelihood on the x-axis.
In the region between no pandemic (0 percent) and a pandemic (100
percent), the measure of relative preference includes an increasing
fraction of costs associated with a pandemic (including the preparation
and stockpiling costs) and a decreasing fraction of the costs associated
with only preparation and stockpiling.[^114]

The 5-year period is defined by the period over which costs were
calculated for stockpiling vaccines and antivirals. That is, the
intervention strategy costs include 5 years worth of stockpile
maintenance and replacement of expired doses. Because these costs are
very small compared to economic losses and equivalent costs of illnesses
and fatalities, the results shown in Figure 4-18 are also indicative of
much longer time horizons.

Of the intervention strategies shown, the least preferred would be the
no intervention case (noSDnoAV1) because it has the lowest relative
preference (and highest equivalent fatality) measure over the likelihood
range. All of the other strategies would be preferred over the no
intervention strategy (noSDnoAV1) by the difference displayed between
that strategy and the no intervention strategy at a given likelihood.
Likewise, any strategy with a higher utility (or lower equivalent
fatality) value would be preferred over another strategy with a lower
utility (or higher equivalent fatality) value by the vertical difference
between them, which represents the expected benefit of choosing the more
preferred over the less preferred strategy.

For example, with no use of antivirals, if a pandemic strikes (100
percent on the x-axis), the expected benefit of choosing a mass
vaccination strategy with a partially effective vaccine (noSDnoAV4)
compared to a no intervention strategy (noSDnoAV1) is a reduction in
total impacts (costs, illnesses, and deaths) that is ***equivalent*** to
only about 2.8 million fatalities (6.7--3.9 million) out of an
impact-***equivalent*** 6.7 million fatalities. When antivirals are used
in addition to mass vaccination with a partially effective vaccine
\[noSDAV4\], the expected benefit is ***equivalent*** to nearly 3.2
million fatalities (6.7--3.5 million) or avoiding additional health and
economic impacts that are equivalent to another 400,000 lives.

Figure 4-19 shows the relative preference for the intervention
strategies that include social distancing both with and without the use
of antivirals. These are displayed on the same axis scales as Figure
4-18 for ease of comparison. In general, the social distancing
assumptions made in these runs show significant improvement in reducing
the impacts associated with an influenza pandemic. Overall, as a group,
these strategies show a 50- to 70-percent reduction in the impacts of a
pandemic. Unfortunately, it was not possible to model individual social
distancing policies as part of this uncertainty analysis, so little
additional commentary can be made with respect to the effectiveness of
specific social distancing policies. It is clear, however, that social
distancing, particularly in the early stages of a pandemic, can have a
significant impact on disease progression if it is practiced extensively
and effectively.

[]{#__RefHeading___Toc179875420 .anchor}Figure 4-19: Relative preference
of intervention strategies including social distancing over the
likelihood of a pandemic, risk-neutral profile

Figure 4-19 indicates a similar preference ordering of the alternative
vaccination strategies, with and without the use of antivirals, when
social distancing is included as when it was absent from the scenarios,
except that strategies 3 and 6 reverse order depending on whether or not
antivirals are used (Table 4-11). However, this reversal is not
significant because the difference in benefits between these scenarios
is very small, only 43,000 and 9,000 equivalent fatalities for
no-antivirals and antivirals, respectively.

[]{#__RefHeading___Toc178751256 .anchor}Table 4-11: Incremental
additional equivalent facilities for vaccination\
strategies, antiviral and no antiviral

+-------+-----------------------------------------+---------+---------+
| **    | **Vaccination Strategy Description**    | **Incr  |         |
| Strat |                                         | emental |         |
| egy** |                                         | Add     |         |
|       |                                         | itional |         |
|       |                                         | Equ     |         |
|       |                                         | ivalent |         |
|       |                                         | Fat     |         |
|       |                                         | alities |         |
|       |                                         | (thous  |         |
|       |                                         | ands)** |         |
+-------+-----------------------------------------+---------+---------+
|       |                                         | *       | **AV**  |
|       |                                         | *noAV** |         |
+-------+-----------------------------------------+---------+---------+
| 4     | Mass vaccination with early vaccine     | Best    | Best    |
+-------+-----------------------------------------+---------+---------+
| 5     | Targeted followed by mass vaccination   | 250     | 297     |
|       | with early vaccine                      |         |         |
+-------+-----------------------------------------+---------+---------+
| 6     | Targeted vaccination with early vaccine | 394     | 364     |
|       |                                         |         |         |
| 3     | Mass vaccination only with no early     |         |         |
| (AV)  | vaccine                                 |         |         |
+-------+-----------------------------------------+---------+---------+
| 3     | Mass vaccination only with no early     | 43      | 9       |
|       | vaccine                                 |         |         |
| 6     |                                         |         |         |
| (AV)  | Targeted vaccination with early vaccine |         |         |
+-------+-----------------------------------------+---------+---------+
| 2     | Contact tracing without vaccination     | 135     | 209     |
+-------+-----------------------------------------+---------+---------+
| 1     | No vaccination or contact tracing       | 354     | 259     |
+-------+-----------------------------------------+---------+---------+

> Note: AV = antiviral

### Satisfaction and Regret Analysis

Knowledge and insights derived from the consequence simulations combined
with the relative preference maps from the decision model are key tools
supporting the goal to better understand and structure intervention
strategy decisions that affect multiple criteria in different ways.
These tools can help decision makers to recognize the uncertainty and
risks and help to identify preferred choices in highly uncertain
situations based on a defined value structure and attitude toward
tolerating or avoiding the risks. However, because of uncertainty, any
choice of intervention strategy, even the optimally preferred choice,
can have some chance of resulting in greater losses than a less
preferred intervention strategy if a pandemic does occur.

The satisfaction and regret analysis performed by the decision model
calculates the confidence level with which any intervention strategy can
be selected over another, based on the results of the uncertainty
analysis simulations. The model also calculates several statistics
related to "satisfaction" and "regret" in the event a pandemic occurs.
Satisfaction is defined as the occurrence of a ***less undesirable***
consequence than what otherwise would have occurred due to the same
incident because of the choice of one intervention strategy over
another. Conversely, regret is defined as the occurrence of a ***more
undesirable*** consequence than what otherwise would have occurred due
the same incident because of the choice of one intervention strategy
over another.

Figure 4-20 depicts satisfaction and regret for selected vaccination
strategies, comparing the use of antivirals to the same strategies
without antivirals. Table 4-12 lists some important data related to
these curves. The standard CIPDSS convention is to describe undesirable
consequences, such as costs and deaths, as positive values; thus, the
regret region in Figure 4-20 is to the right of zero and the
satisfaction region is to the left. That is, negative costs are benefits
or gains.

![](media/image34.wmf){width="6.5in" height="4.430555555555555in"}

[]{#__RefHeading___Toc179875421 .anchor}Figure 4-20:
**Satisfaction/regret analyses for selected pairs of intervention\
strategies not involving quarantine**

Satisfaction and regret curves are calculated under the assumption that
an influenza pandemic does occur and they are useful in answering the
following questions:

-   If Intervention X is selected over Intervention Y, what level of
    confidence can the decision maker have that Intervention X will
    outperform Intervention Y in the event of a pandemic?

-   If Intervention X does outperform Intervention Y in the event of a
    pandemic, how much better off will society be than if Intervention Y
    were selected? What are the maximum, conditional average, and
    expected levels of satisfaction?

-   If Intervention X does not outperform Intervention Y in the event of
    a pandemic, how much worse off will society be than if Intervention
    Y were selected? What are the maximum, conditional average, and
    expected levels of regret?

[]{#__RefHeading___Toc178751257 .anchor}**Table 4-12:
Satisfaction/regret statistics for selected pairs of intervention\
strategies not involving quarantine**

+----------------------------+-------------+-------------+-------------+
| **Satisfaction/Regret**    | **noSDAV1   | **noSDAV3   | **noSDAV4   |
|                            | over**      | over**      | over**      |
|                            |             |             |             |
|                            | **          | **          | **          |
|                            | noSDnoAV1** | noSDnoAV3** | noSDnoAV4** |
+----------------------------+-------------+-------------+-------------+
| Likelihood of satisfaction | 100%        | 100%        | 84%         |
+----------------------------+-------------+-------------+-------------+
| Maximum possible           | 2,905       | 4,133       | 4,161       |
| satisfaction               |             |             |             |
+----------------------------+-------------+-------------+-------------+
| Average conditional        | 511         | 541         | 448         |
| satisfaction               |             |             |             |
+----------------------------+-------------+-------------+-------------+
| Expected satisfaction      | 511         | 541         | 378         |
+----------------------------+-------------+-------------+-------------+
| Likelihood of regret       | 0%          | 0%          | 16%         |
+----------------------------+-------------+-------------+-------------+
| Maximum possible regret    | 0           | 0           | 6           |
+----------------------------+-------------+-------------+-------------+
| Average conditional regret |             |             | 2           |
+----------------------------+-------------+-------------+-------------+
| Expected regret            |             |             | 0.3         |
+----------------------------+-------------+-------------+-------------+
| Expected overall payoff    | 511         | 541         | 377         |
+----------------------------+-------------+-------------+-------------+

Notes: Values expressed in thousands of equivalent fatalities unless
otherwise stated

AV = antiviral; SD = social distancing

The analysis of using antivirals in the manner modeled over no
intervention; that is, choosing strategy noSDAV1 over noSDnoAV1,
indicates that all 243 uncertainty runs of the model resulted in a lower
total consequence, measured in equivalent fatalities, or a 100-percent
likelihood of being satisfied with this choice if a pandemic occurs.
This was also the case when the strategy pairs included mass vaccination
when a fully-effective vaccine becomes available; that is, choosing
noSDAV3 over noSDnoAV3. This similarity should be expected because, in
many model runs, the majority of pandemic impacts occur prior to the
availability of a strain-specific vaccine. The same comparison based on
a mass vaccination with partially-effective vaccine; that is, choosing
noDSAV4 over noSDnoAV4, results in 16 percent of the model runs having
worse outcomes if a pandemic occurs. Although, the 16-percent chance may
seem great, the maximum possible level of regret is only 5,800
equivalent fatalities (barely detectable on the graph), compared to an
84-percent chance of satisfaction and a maximum satisfaction level of
over 4 million lives not lost to influenza.

CIPDSS performed a similar analysis on non-social-distancing scenarios
to compare contact tracing and quarantine alone (vaccination strategy
2), with targeted vaccination followed by mass vaccination with an
early, pre-pandemic vaccine (vaccination strategy 5), and only targeted
vaccination with an early, pre-pandemic vaccine (vaccination strategy
6). Both the targeted strategy and the targeted followed by mass
vaccination strategy, strategies 5 and 6, are predicated on contact
tracing and quarantine; that is, they are essentially extensions of the
contact tracing and quarantine alone strategy (strategy 2). In the
targeted scenario and the targeted followed by mass vaccination
scenario, contact tracing and quarantine alone accounted for roughly 75
to 80 percent of the reduction in illnesses and fatalities.

Figure 4-21 displays the results of this satisfaction/regret analysis
and Table 4-13 lists the supporting statistics. The results indicate
there is a high degree of assurance that combining targeted vaccination
or targeted vaccination followed by mass vaccination with a partially
effective, pre-pandemic vaccine and with contact tracing and quarantine
will result in fewer illnesses and deaths. The results indicate that if
these strategies are used over contact tracing and quarantine alone,
there is an 85- to 90-percent chance that the combined economic and
health impacts will be less by an expected value of 236,000 to 656,000
***equivalent fatalities***. There is a 10- to 15-percent chance that
the result will be otherwise, and that the combined impacts will be
greater that they otherwise would have been. If the combined impacts are
greater, however, the expected level of regret is only 50,000 to 62,000
![](media/image35.wmf){width="6.5in"
height="4.430555555555555in"}***equivalent fatalities***.

[]{#__RefHeading___Toc179875423 .anchor}**Figure 4-21:
Satisfaction/regret analysis for selected pairs of pandemic influenza\
intervention strategies involving quarantine and targeted vaccination**

[]{#__RefHeading___Toc178751258 .anchor}**Table 4-13:
Satisfaction/regret statistics for selected pairs of intervention
strategies\
involving quarantine and targeted vaccination**

+------------------------+----------+----------+----------+----------+
| *                      | **n      | **n      | *        | *        |
| *Satisfaction/Regret** | oSDnoAV5 | oSDnoAV6 | *noSDAV5 | *noSDAV6 |
|                        | over**   | over**   | over**   | over**   |
|                        |          |          |          |          |
|                        | **noS    | **noS    | **n      | **n      |
|                        | DnoAV2** | DnoAV2** | oSDAV2** | oSDAV2** |
+------------------------+----------+----------+----------+----------+
| Likelihood of          | 88%      | 86%      | 85%      | 90%      |
| satisfaction           |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Maximum possible       | 9,026    | 4,039    | 10,909   | 4,237    |
| satisfaction           |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Average conditional    | 703      | 348      | 836      | 397      |
| satisfaction           |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Expected satisfaction  | 622      | 298      | 708      | 358      |
+------------------------+----------+----------+----------+----------+
| Likelihood of regret   | 12%      | 14%      | 15%      | 10%      |
+------------------------+----------+----------+----------+----------+
| Maximum possible       | 3,863    | 3,862    | 3,570    | 3,584    |
| regret                 |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Average conditional    | 537      | 433      | 327      | 517      |
| regret                 |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Expected regret        | 62       | 62       | 50       | 51       |
+------------------------+----------+----------+----------+----------+
| Expected overall       | 560      | 236      | 659      | 307      |
| payoff                 |          |          |          |          |
+------------------------+----------+----------+----------+----------+

Notes: Values expressed in thousands of equivalent fatalities unless
otherwise stated

AV = antiviral; SD = social distancing

The analysis has demonstrated that if a partially-effective pre-pandemic
vaccine is available, the preferred strategy is mass vaccination over
either of the two targeted vaccination strategies modeled, whether or
not antivirals are also used. The mass vaccination strategies noSDnoAV4
and noSDAV4 are preferred because the ***expected***, or ***average***,
level of illnesses, fatalities, and other consequences, combined, are
less than if either of the corresponding targeted vaccination strategies
were used. However, in some situations, the targeted vaccination
strategies result in lower illnesses, fatalities, and other
consequences. Figure 4-22 and Table 4-14 show the results of a
satisfaction/regret analysis for the choice of the preferred mass
vaccination strategy (vaccination strategy 4) over the less-preferred
targeted vaccination strategies (vaccination strategies 5 and 6), with
and without the use of antivirals, but with no social distancing.

[]{#__RefHeading___Toc179875424 .anchor}**Figure 4-22:
Satisfaction/regret analysis for the choice of mass vaccination over
targeted vaccination strategies with partially-effective pre-pandemic
vaccine**

![](media/image36.wmf){width="6.5in" height="4.430555555555555in"}

[]{#__RefHeading___Toc178751259 .anchor}**Table 4-14:
Satisfaction/regret statistics for the choice of mass vaccination over
targeted vaccination strategies with partially-effective pre-pandemic
vaccine**

+------------------------+----------+----------+----------+----------+
| *                      | **n      | **n      | *        | *        |
| *Satisfaction/Regret** | oSDnoAV5 | oSDnoAV6 | *noSDAV5 | *noSDAV6 |
|                        | over**   | over**   | over**   | over**   |
|                        |          |          |          |          |
|                        | **noS    | **noS    | **n      | **n      |
|                        | DnoAV2** | DnoAV2** | oSDAV2** | oSDAV2** |
+------------------------+----------+----------+----------+----------+
| Likelihood of          | 68%      | 70%      | 72%      | 74%      |
| satisfaction           |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Maximum possible       | 11,687   | 16,674   | 14,352   | 17,612   |
| satisfaction           |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Average conditional    | 1,276    | 1,695    | 1,134    | 1,563    |
| satisfaction           |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Expected satisfaction  | 871      | 1,179    | 816      | 1,151    |
+------------------------+----------+----------+----------+----------+
| Likelihood of regret   | 32%      | 30%      | 28%      | 26%      |
+------------------------+----------+----------+----------+----------+
| Maximum possible       | 8,933    | 8,694    | 7,023    | 6,984    |
| regret                 |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Average conditional    | 1,722    | 1,736    | 1,662    | 1,702    |
| regret                 |          |          |          |          |
+------------------------+----------+----------+----------+----------+
| Expected regret        | 546      | 529      | 465      | 448      |
+------------------------+----------+----------+----------+----------+
| Expected overall       | 326      | 650      | 351      | 703      |
| payoff                 |          |          |          |          |
+------------------------+----------+----------+----------+----------+

Note: AV = antiviral; SD = social distancing

Because this analysis is based on the choice of mass vaccination
strategy 4 instead of vaccination strategies 5 and 6, the cumulative
probability traces to the left of zero in Figure 4-22 result from the
model uncertainty runs in which the choice of mass vaccination resulted
in lower consequences than if either of the targeted vaccination
strategies had been chosen. These runs result in "satisfaction" because
the chosen strategy produces the lowest level of consequences. However,
the traces in the regret region result from model uncertainty runs in
which the chosen mass vaccination strategy ***did not*** produce a lower
level of consequences. In the situations represented by the points along
the lines representing each choice of mass vaccination over targeted
vaccination, the targeted vaccination strategy produced lower
consequences, resulting in "regret" for having chosen the mass
vaccination strategy. The statistics in Table 4-14 indicate that the
likelihood of regret; that is, the likelihood that targeted vaccination
will result in lower consequences as measured in ***equivalent
fatalities***, is between 26 and 32 percent, depending on which targeted
vaccination strategy is compared and the use of antivirals. Thus,
sometimes targeted vaccination is better than mass vaccination. A
detailed multivariate analysis of the runs in both the satisfaction
region and the regret region is needed to determine if there are any
observable precursor conditions in the runs that uniquely or
statistically differentiate "satisfaction" runs from "regret" runs. If
so, decision makers could use observation of these variables during a
pandemic, when these variables are known, as a further aide to choosing
the best vaccination strategy, which might be targeted vaccination.

### **Highlights and Comparison to EpiSimS Result**s

CIPDSS model results in comparison with the EpiSimS sensitivity study
results are summarized here.

-   **The impact of the next pandemic is highly sensitive to how
    infectious the virus will be**. Both models predict that
    transmission or contagiousness of the influenza virus is critical
    for the propagation of the disease, the number infected, and the
    resulting impacts.

-   **Household-based interventions can significantly reduce pandemic
    spread**. EpiSimS showed that much of the expected disease
    transmission will occur at home, especially when the stay-home
    behavior of affected individuals is considered. While CIPDSS did not
    model household transmission, CIPDSS results show social-distancing
    interventions that reduce the number of daily interactions can
    reduce the severity of the pandemic (although at significant
    economic cost).

-   **Partially effective pre-pandemic vaccine at the current stockpile
    level will have minimal impact**. Both EpiSimS and CIPDSS examined
    the use of an influenza vaccine developed prior to the pandemic
    based on avian strains that may not be well matched to the pandemic
    strain. Such a partially effective vaccine with limited availability
    will have a minimal impact on reducing the number of cases. However,
    when using a higher availability of partially effective vaccines in
    the CIPDSS model, the potential impact on controlling the pandemic
    increased.

-   **Targeted vaccination can be more effective than mass
    vaccination.** It is unclear whether the elderly and children will
    be at highest risk during the next pandemic. Therefore, the CDC
    seasonal recommendation on prioritization groups might not be
    appropriate for a pandemic. CIPDSS results documented that a
    targeted vaccination strategy reduced the pandemic outcome
    significantly more than a mass vaccination strategy.

-   **Antiviral stockpile**. Antiviral usage may delay a pandemic.
    CIPDSS runs contained antivirals availability, which ranged from the
    current stockpile of 20 million to the projected 80 million and even
    up to 120 million courses. Antiviral effectiveness was also varied
    due to uncertainty in antiviral effectiveness per influenza strain
    and the potential for evolved resistance.

-   **Pharmaceutical interventions reduce worker absenteeism**. In
    agreement with EpiSimS, CIPDSS found that pharmaceutical
    interventions, such as antiviral medication and vaccine, reduce
    worker absenteeism and can help ensure continuity of critical
    services.

-   **Behavioral modifications may generate waves**. Individual and
    community-based behavioral modifications, such as school closures,
    fear-based home isolation, and social distancing, have a significant
    effect on slowing influenza spread and reducing morbidity and
    mortality. CIPDSS modeled a uniform self-quarantine
    (social-distancing) strategy that varied by age. Because CIPDSS does
    not model schools, it could not represent the infection waves caused
    by reopening. However, the social-distancing model can generate
    re-infection waves.

-   **Social distancing for any reason can have an impact on worker
    absenteeism**. Staying home for any reason can be effective in
    reducing morbidity and mortality and slowing transmission. However,
    CIPDSS also documented that the economy can be greatly affected if
    large numbers of people withdraw from their daily activities,
    causing disruption in social life.

-   **Impact of workforce absenteeism on critical infrastructure**.
    During a pandemic, significant absenteeism may occur. Absenteeism
    can be affected by variations in assumptions, and model parameters
    are affected in very different ways than illness and death. While
    using the entire range of possibilities, in extreme cases for both
    biological and sociological ranges and the mitigations such as
    self-quarantining, CIPDSS documented extreme values for unavailable
    workers. Minimizing the number of influenza-infected people and
    mortalities, while maximizing workers available, needs to be a focus
    for minimizing the overall impacts of the disease.

# Summary and Conclusions

National influenza pandemic preparedness plans currently focus on
reducing the impacts associated with a constant attack rate in addition
to reducing transmission. CIPDSS model results build on previous
modeling studies of pandemic influenza that have focused on the
possibility of containment through mitigation strategies. The model
results also complement the pandemic scenarios of EpiSimS by using
similar mitigation options, but under the additional conditions of
uncertainties in the influenza virus and the different ways people may
respond to a pandemic.

Understanding and predicting the effectiveness of different mitigation
strategies in controlling a pandemic will depend on the range of
virulence, transmissibility, age-effects, public response, and response
of the public health infrastructure. Mitigation strategies and
combinations of responses may vary with the reality of the pandemic
scenario presented, while maintaining a cost-effective and appropriate
response that does not affect critical infrastructure and does maintain
continuity of the nation's business operations and economy.

The most important variables related to the total number of afflicted
people in a pandemic are the fractions of the transmission that occur
prior to symptoms presenting themselves in a person (infectious
asymptomatics), the reproductive number, and the length of each disease
stage. The most important aspect of influenza transmission is the
limited window of time before symptoms appear when a person is able to
transmit the virus to other people. The reproductive number (cases that
an infected person subsequently infects) is directly related to the
attack rate of a pandemic.

The maximum total number of hospitalized cases was dependent on a wider
variety of both disease and response characteristics, including

-   Average contacts per case

-   Self-quarantine tendency maximum

-   Reproductive number

-   Disease stage duration (all stages)

-   Base fraction of special patients requiring admission

-   Fraction of special patients treated in field by EMS

-   Additional fraction asymptomatic

-   Nominal fatality rate (infant, elderly)

-   Relative contagion of asymptomatics

Public health preparedness will be dependent on the virulence of the
influenza virus, number of total cases, vaccine and antiviral
mitigations that are available, management of the medical professional
workforce, and sociology of the local population in the area.

The total impacts to the economy are predominately dependent on lost
worker days during the pandemic through illnesses, deaths, fear, school
closures, and other reasons for otherwise not being able to maintain
duties. Total workers unavailable are predominantly affected by the
fraction of workers that are self-quarantined or use social distancing
and the spread of the days that the specified fraction of workers is
self-quarantined.

The CIPDSS results are consistent with and supported by the published
literature. Most results are in congruence with the NISAC EpiSimS model
results. CIPDSS used a system dynamics model with the top mitigation
strategies represented that include rapid vaccine production, antiviral
usage, detection of the disease, fraction of social distancing by age
groups, and ability to effectively contact exposed persons. With the
ability to rapidly compute simulation runs, multiple mitigation
strategies were modeled for the various disease characteristics that are
possible.

The ability to effectively find and contact persons that may have been
exposed to an infected person and then have them be quarantined greatly
reduced the continued transmission of the influenza virus and thus,
reduced the total numbers of illnesses and deaths. Contact tracing alone
reduced the impact of the pandemic significantly. The best overall
vaccination strategy was to have an early and partially effective
vaccine that is available immediately, followed by a mass vaccination
specific to the strain 4 to 6 months later. Similar to EpiSimS, it was
found that a pre-pandemic vaccine greatly reduced the impacts of the
pandemic.

Immediate administration of antivirals may or may not have an impact
upon the pandemic, depending on the biological characteristics of the
influenza virus such as infectivity or virulence. Overall, antivirals
reduced the impacts of the pandemic, but there was also significant
variation in the responses due to the characteristics of the virus.
EpiSimS found that immediate administration of antivirals does not have
a large impact upon the pandemic with the current stockpile. This study
found that the current stockpile may or may not affect the pandemic,
depending upon the infectivity and virulence of the virus. Variability
of the responses for the availability of antivirals was considerable,
even when using the more unrealistic number of antiviral courses of 120
million. Impact of antivirals will depend on the severity of the disease
and stockpiled courses to be effective in curbing a pandemic.

The use of the NPI of social distancing reduced the pandemic outcomes
for all of the vaccination strategies. Social distancing with antivirals
reduced the pandemic on average by 63 percent. However, social
distancing also greatly affected the percent loss to the GDP for all
scenarios due to worker unavailability.

Similar to EpiSimS, when combining all mitigation strategies available,
it was found that the overall pandemic impact can be dramatically
reduced. The more infective, asymptomatic and infective, and virulent an
influenza virus is, the more it will limit the effectiveness of any or
even a combination of mitigation strategies. All scenarios within the
12,288 simulation runs contained a significant amount of variation.
However, even for *R~0~* of greater than 3, using the combination
strategy of a partially effective early vaccine followed by a mass
vaccination when the specific strain was available and social
distancing, can reduce the pandemic significantly. Containing a pandemic
that is highly infective and has a case fatality rate is possible with a
combination of strategies. However, the simulations here have shown that
although uncertainty and variability is large, coordinated efforts for
response and stockpiled pharmaceuticals can reduce the impacts felt by
potentially millions of people.

Overall, large variability was observed in the outcomes using all of the
possible biological, sociological, interventions, and policy
uncertainties. The top parameters that significantly contributed to the
variance of the output variables of interest, such as the number of
illnesses/deaths or unavailable workers, follow:

-   Fraction transmission prior to clear symptoms (infectious
    asymptomatics)

-   Reproductive number (*R~0~*)

-   Time to recover (all stages)

-   Relative contagious asymptomatic

-   Additional fraction asymptomatic

-   Fraction contact effectiveness (contact tracing)

-   Case fatality rate

-   Antiviral production

The impact of the next pandemic is highly sensitive to how infectious
the virus will be as well as how infectious the disease is prior to an
infected person showing symptoms. The majority of the variables listed
above deal with biological variability, with the exception of the
effectiveness of contact tracing and antiviral production rate.
Mitigation strategies for pandemic influenza that reduce the top disease
variables will be the most effective. The results show that all of the
interventions considered provide some reduction in the number of deaths
caused by the pandemic, but social distancing (in all social-distancing,
antiviral scenarios) and contact tracing with quarantine (in vaccination
strategies 2, 4, 6, and 7) show the best results in this analysis. Mass
vaccination provides little reduction in deaths if it only occurs after
a fully effective vaccine is developed (vaccination strategy 3), because
the pandemic has largely run its course by then.

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# Appendix A: Summary of Intervention Strategy Frequency Histograms {#appendix-a-summary-of-intervention-strategy-frequency-histograms .unnumbered}

![](media/image37.wmf){width="7.079166666666667in"
height="5.393055555555556in"}

![](media/image38.wmf){width="7.811805555555556in"
height="5.980555555555555in"}

![](media/image39.wmf){width="7.822916666666667in" height="5.90625in"}

![](media/image40.wmf){width="7.78125in" height="5.895833333333333in"}

![](media/image41.wmf){width="7.78125in" height="5.895833333333333in"}

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# Appendix B: Summary of Intervention Strategy Consequence Statistics {#appendix-b-summary-of-intervention-strategy-consequence-statistics .unnumbered}

Mean and extreme decision metric values alternative\
intervention strategy scenarios

No social distancing with no antivirals scenarios
![](media/image42.wmf){width="5.8597222222222225in"
height="6.397222222222222in"}

Mean and extreme decision metric values alternative\
intervention strategy scenarios (continued)

No social distancing with antivirals scenarios

![](media/image43.wmf){width="5.8597222222222225in"
height="6.397222222222222in"}

Mean and extreme decision metric values alternative\
intervention strategy scenarios (continued)

Social distancing with no antivirals scenarios

![](media/image44.wmf){width="5.8597222222222225in"
height="6.397222222222222in"}

Mean and extreme decision metric values alternative\
intervention strategy scenarios (continued)

Social distancing with antivirals scenarios

![](media/image45.wmf){width="5.8597222222222225in"
height="6.397222222222222in"}

# Appendix C: Outlines for Nonpharmaceutical Mitigation Measures for Pandemic Influenza  {#appendix-c-outlines-for-nonpharmaceutical-mitigation-measures-for-pandemic-influenza .unnumbered}

# Table of Contents {#table-of-contents-1 .unnumbered}

[Introduction C-3](#introduction-1)

[Public Information and Education
C-6](#public-information-and-education)

[School Closure C-12](#school-closure)

[Home Isolation and Quarantine C-17](#home-isolation-and-quarantine)

[Social Distancing Policies---Voluntary Social Withdrawal
C-23](#social-distancing-policiesvoluntary-social-withdrawal)

[Social Distancing Policies--Restrict Large Public Gatherings
C-25](#social-distancing-policiesrestrict-large-public-gatherings)

[Social Distancing Policies---Restrict Public Transportation
C-28](#social-distancing-policiesrestrict-public-transportation)

# List of Acronyms and Abbreviations {#list-of-acronyms-and-abbreviations-1 .unnumbered}

  --------------- -------------------------------------------------------
  ASTHO           Association of State and Territorial Health Officials

  CDC             The Centers for Disease Control and Prevention

  CIPDSS          Critical Infrastructure Protection Decision Support
                  System

  DEAS            Digital Emergency Alert System

  DHS             Department of Homeland Security

  HAN             Health Alert Network

  HHS             U.S. Department of Health and Human Services

  HOV             high occupancy vehicle

  JIC             Joint Information Center

  JIS             Joint Information System

  MIDAS           Models of Infectious Disease Agent Study

  NAACHO          National Association of County and City Health
                  Officials

  PSA             public service announcement

  RSS             really simply syndication

  SARS            Severe Acute Respiratory Syndrome
  --------------- -------------------------------------------------------

## Introduction {#introduction-1 .unnumbered}

The application of antiviral drugs and vaccines is expected to be the
key strategy for blunting the impact of pandemic influenza. However,
these pharmaceutical measures are subject to limited availability. This
is particularly true of the pandemic vaccine, which may not be available
for months after the start of a pandemic. Thus, nonpharmaceutical
measures will also play a critical role in limiting disease spread.
These measures reduce the spread of infection by reducing contact rates
between infectious and susceptible persons and by reducing transmission
of infectious agents through good hygiene. The intent of this appendix
is to provide a descriptive outline of the principal nonpharmaceutical
interventions and to broadly describe their feasibility, effectiveness,
and cost. The measures described are public information and education,
school closure, home isolation and quarantine, and social distancing
policies. The later include voluntary social withdrawal, restricting
large public gatherings, and restricting public transportation.

For each measure, the Critical Infrastructure Protection Decision
Support System (CIPDSS) has applied the following outline, though the
level of detail varies by mitigation measure according to the
availability of information. This outline is simply a consistent way to
organize descriptions of practical steps in implementing these measures
and of issues likely to be encountered. For some of the measures, CIPDSS
has identified cost benchmarks.

I.  Implementation of mitigation measure

    a.  Responsibility and authority

    b.  Major steps for implementation

    c.  Required resources

    d.  Problems and issues

II. Effectiveness

    a.  Percent of population affected by mitigation measure

    b.  Compliance rate

III. Duration

     a.  Feasible duration of intervention

     b.  Factors limiting duration

IV. Impacts and costs

    a.  Economic sectors affected

    b.  Type of impacts

The following table is a very brief summary of these outline elements
for each of the interventions.

Summary of nonpharmaceutical options

+-------------+-------------+-------------+------------+--------------+
| **Nonpha    | **Imple     | **Effe      | **         | **Impacts    |
| rmaceutical | mentation** | ctiveness** | Duration** | and Costs**  |
| I           |             |             |            |              |
| ntervention |             |             |            |              |
| Measure**   |             |             |            |              |
+-------------+-------------+-------------+------------+--------------+
| Public      | D           | Essential   | Ongoing in | Prepare the  |
| information | istribution | as          | pr         | public to    |
| and         | of          | preparation | e-pandemic | avoid fear   |
| education   | literature, | for the     | period     | and          |
|             | news        | application | with       | o            |
|             | an          | of any      | a          | verreaction; |
|             | nouncement, | other       | ccelerated | reduce       |
|             | training    | int         | program    | spread       |
|             | and         | erventions, | effort     | through good |
|             | awareness   | including   | during     | hygiene and  |
|             | programs    | pha         | pandemic   | reduced      |
|             |             | rmaceutical |            | contacts;    |
|             |             | in          |            | clarify      |
|             |             | terventions |            | treatment    |
|             |             |             |            | options and  |
|             |             |             |            | service      |
|             |             |             |            | options      |
|             |             |             |            |              |
|             |             |             |            | Costs: \$17  |
|             |             |             |            | million per  |
|             |             |             |            | year before  |
|             |             |             |            | pandemic,    |
|             |             |             |            | \$160        |
|             |             |             |            | million      |
|             |             |             |            | during       |
|             |             |             |            | pandemic^a^  |
+-------------+-------------+-------------+------------+--------------+
| School      | Official    | Uncertain   | Feasible   | Loss of      |
| closure     | closure of  | ef          | duration   | educational  |
|             | schools or  | fectiveness | is 90 to   | opportunity  |
|             | severe      | because of  | 300 days   | for          |
|             | r           | the need to | because    | students;    |
|             | estrictions | provide for | the        | lost teacher |
|             | on          | at-risk     | pandemic   | salaries;    |
|             | attendance  | children    | may come   | basically,   |
|             | to provide  | and the     | in waves;  | the full     |
|             | for at-risk | need for    | di         | school       |
|             | children;   | companion   | stribution | budget for   |
|             | must be     | r           | of         | the duration |
|             | implemented | estrictions | vaccines   | of the       |
|             | early in    | on other    | and        | closure will |
|             | pandemic    | forms of    | antivirals | be lost      |
|             |             | social      | may be a   | because      |
|             |             | interaction | factor     | continuity   |
|             |             | among       |            | must be      |
|             |             | students    |            | provided and |
|             |             |             |            | no product   |
|             |             |             |            | (education)  |
|             |             |             |            | will result; |
|             |             |             |            | \$10,600 to  |
|             |             |             |            | \$12,400 per |
|             |             |             |            | student per  |
|             |             |             |            | year lost    |
+-------------+-------------+-------------+------------+--------------+

Summary of nonpharmaceutical options (continued)

  --------------------- -------------------- ------------------- --------------- ---------------
  **Nonpharmaceutical   **Implementation**   **Effectiveness**   **Duration**    **Impacts and
  Intervention                                                                   Costs**
  Measure**                                                                      

  Home isolation and    Isolation of         Experience with     Program would   Isolated and
  quarantine            symptomatic          Severe Acute        be triggered by quarantined
                        individuals in their Respiratory         policy when     individuals
                        home and quarantine  Syndrome (SARS)     pandemic        will need
                        of close contacts    suggests that       reaches         medical
                        also at home;        public cooperation  predetermined   support, food,
                        program would be     can be expected;    threshold;      salary
                        triggered by policy  quarantine of       individuals     replacement,
                        when pandemic        contacts with       would be        and social
                        reaches              symptomatic         released after  support, with a
                        predetermined        isolates is likely  recovery;       6-week average
                        threshold            to spread the       program would   isolation and
                                             disease             continue until  quarantine
                                                                 pre-set         period for a
                                                                 threshold of    household and
                                                                 population is   25% of
                                                                 symptomatic     households
                                                                                 affected during
                                                                                 the course of a
                                                                                 pandemic; cost
                                                                                 could exceed
                                                                                 \$2.5
                                                                                 billion^a^

  Social distancing:    Self-isolation and   A compliance rate   Individual      Though this is
  voluntary social      quarantine of        of 10% to 40% is    household       a voluntary
  withdrawal            households; this may expected, though    participation   program, the
                        be in compliance     economic needs will likely to be    same levels of
                        with official        largely dictate     from 10 to 30   support as for
                        requests to reduce   compliance          days, depending isolation and
                        public contacts                          on number of    quarantine may
                                                                 members in the  be required;
                                                                 household and   however, for a
                                                                 economic        household of
                                                                 circumstances   well
                                                                                 individuals,
                                                                                 the costs would
                                                                                 be lower
                                                                                 because of
                                                                                 reduced medical
                                                                                 needs. Also, as
                                                                                 a voluntary
                                                                                 program, some
                                                                                 participants
                                                                                 will not
                                                                                 require salary
                                                                                 replacement
  --------------------- -------------------- ------------------- --------------- ---------------

Summary of nonpharmaceutical options (continued)

  --------------------- -------------------- ------------------- -------------- -----------------
  **Nonpharmaceutical   **Implementation**   **Effectiveness**   **Duration**   **Impacts and
  Intervention                                                                  Costs**
  Measure**                                                                     

  Social distancing:    Public gatherings,   Many public events  Duration of    Loss of revenue
  restrict large public including special    involve close       restrictions   to sports,
  gatherings            events and periodic  contact between     may be for the entertainment,
                        meetings such as     individuals;        full period of and religious
                        church and           therefore,          the pandemic   organizations;
                        government forums    restricting such    or for a       for a 12-week
                        would be suspended   events should       shorter period restriction
                        for the duration of  reduce contact      determined by  period, these
                        the pandemic.        rates and disease   health         losses may exceed
                                             transmission.       officials;     \$50,000,000^a^
                                                                 public         
                                                                 resistance may 
                                                                 be strong      
  --------------------- -------------------- ------------------- -------------- -----------------

^a^For a city population of 5 million

## Public Information and Education  {#public-information-and-education .unnumbered}

##### Intervention

Implement local public policies that reduce public anxiety and fear and
reduce risks of influenza exposure through person-to-person contacts by
educating the public about how influenza is transmitted and about social
distancing techniques.

##### Implementation

###### Responsibility and Authority

State and local government, as represented by public health departments,
schools, and other agencies, are generally responsible for developing
and disseminating public information on pandemic influenza. The federal
government, particularly the U.S. Department of Health and Human
Services (HHS) and the Centers for Disease Control and Prevention (CDC)
support this effort with technical assistance, prepared pamphlets, and
educational resources. In addition, healthcare-related businesses, such
as hospitals, clinics, pharmacies, and special care facilities, will be
responsible for the distribution of information to patients. During a
pandemic, all commercial news media will be relied upon to get out
critical influenza care and avoidance messages. Finally, volunteer
organizations, such as the Red Cross, may make important contributions
in particular communities by distributing information.

Major Implementation Steps

-   Develop key messages to educate the public about the risks
    associated with a pandemic influenza outbreak and about ways to
    minimize chances of exposure (including social distancing techniques
    and when and how to seek treatment)

-   Identify intended recipients for specific messages to optimize media
    choice and distribution channels. This may involve, for example,
    addressing the requirements of special needs populations, specific
    age groups, and non-English-speaking populations.

-   Coordinate messages between all levels of government and the private
    sector. The key to such coordination will be use of the Joint
    Information System (JIS), including creation and staffing of a
    national Joint Information Center (JIC) and creation and staffing of
    state and regional JICs.

-   Identify and establish competent "spokespersons" for the duration of
    the crisis

Another key organizational structure for pandemic communications is
being developed as the Health Alert Network (HAN). According to a CDC
web-site:[^115]

> The Health Alert Network (HAN) is a nationwide program to establish
> the communications, information, distance-learning, and organizational
> infrastructure for a new level of defense against health threats,
> including the possibility of bioterrorism. The HAN will link local
> health departments to one another and to other organizations critical
> for preparedness and response: community first-responders, hospital
> and private laboratories, state health departments, CDC, and other
> federal agencies. CDC is leading development of the HAN, in
> partnership with the National Association of County and City Health
> Officials (NACCHO), the Association of State and Territorial Health
> Officials (ASTHO), and other health organizations.

The HAN will participate in developing dissemination strategies for key
messages and in the production and dissemination of public education
materials. The types of materials that will be produced by the HAN and
other organizations include:

-   Written materials

```{=html}
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```
-   Brochures, pamphlets, flyers, and so forth

-   Newspaper inserts

```{=html}
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```
-   Video/Audio

```{=html}
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```
-   Public service announcements (PSAs) (television)

-   PSA (radio)

-   Podcasts

```{=html}
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```
-   Web/Net-based

```{=html}
<!-- -->
```
-   A standard-format National information site and state/local sites

-   Really Simple Syndication (RSS) feeds

-   Social networking sites

-   Mass text messaging (targeted by geographic region)

-   U.S. Department of Homeland Security (DHS) PrepNet programming

```{=html}
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```
-   Digital Emergency Alert System (DEAS) messages (targeted by
    geographic region)

-   News Media

```{=html}
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```
-   News releases

-   Interviews

One final responsibility of coordinating agencies is to monitor key
message dissemination for effectiveness or possible misinformation.

###### Required Resources

The resources needed to accomplish the intended communication goals are
funded (public and/or private) to identify and create message materials,
facilitate communication within the JIS, and house and equip JICs; and
determine staffing required for identification, development, and
dissemination of key messages, coordination of messages, and staffing of
JICs. A typical JIC organization is represented in the following figure.

![](media/image46.jpeg){width="5.739583333333333in"
height="4.304861111111111in"}

###### Problems and Issues

-   Given the cross-jurisdictional and overlapping nature of the
    information programs, CIPDSS anticipates that problems of the
    following types will need to be overcome:

```{=html}
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```
-   Political infighting and turf squabbles

-   Loss of public confidence in response due to mixed or confusing
    messages

-   Increase in public fear/anxiety from lack of information or
    mixed/confusing messages

##### Duration

The pre-pandemic information campaigns are expected to be ongoing, at
least while the threat posed by current avian influenza or other similar
threats exist. Such ongoing programs are at risk of losing public
support and interest if they are not renewed regularly with creative new
approaches to getting out the messages. This implies continuous funding
for staff, product development, and redistribution. During the pandemic,
information distribution, particularly as coordinated by the JICs, will
continue for the duration of the pandemic and during any subsequent
recovery period, during which normal social and economic activities are
restored.

###### Impacts

The main impacts of the public education and outreach program will be
benefits to the private sector, particularly to households. Assistance
to commercial enterprises and public schools will also be important.
These benefits include

-   Population not overreacting to events unfolding

-   Media coverage becoming more reasonable

-   Criticism of official actions lessening

-   Rumors countered more effectively

-   Fewer "worried well" to inundate medical facilities

-   Fewer disease transmissions due to exposure mitigation

-   Minimized financial impact if disease spread is lessened and safe
    behaviors are practiced

###### Pre-Outbreak Education and Awareness Program Costs

Costs are associated with development of new or adaptation of existing
materials to educate the public about the risks associated with a
pandemic influenza outbreak and how to minimize risk of exposure. All
materials must be produced in multilingual formats. Elements of cost for
a city of 5 million are provided in the following table.

Pre-outbreak education and awareness program costs

  -------------------------------------- -------------- -----------------
  Newspaper inserts, brochures,                         
  pamphlets, and flyers                                 

  replacement cycle, years               2              

  target audience fraction               0.7            

  number of items                        10             

                                         unit cost      extended cost

  layout and design                      \$2,000.00     \$10,000.00

  printing                               \$0.48         \$8,400,000.00

  distribution                           \$0.25         \$4,375,000.00

                                         total          \$12,785,000.00

  Video and Audio Public Service                        
  Announcements                                         

  replacement cycle, years               0.25           

  number of messages                     5              

  languages                              3              

  frequency, per year                    1000           

                                         unit cost      extended cost

  Design and production                  \$35,000.00    \$2,100,000.00

  broadcasting                           \$0.00         \$0.00

  Joint Information System Pandemic flu                 
  capability                                            

                                         staff is       
                                         in-place, no   
                                         additional     
                                         cost           

  Web-site                               staff-years    extended cost

  \$/staff year, computer specialists    \$150,000.00   

  Development and maintenance of a       10             \$1,500,000.00
  standard format central National                      
  information site and state/local sites                

  RSS feeds                              5              \$750,000.00

  Establishment and utilization of       0              \$0.00
  social networking sites                               

  Total Annual Cost, Pre-pandemic                       \$17,135,000.00
  -------------------------------------- -------------- -----------------

###### Outbreak Crisis Messaging

During an outbreak, the emphasis of messages will shift to the status of
disease spread, avoidance through good hygiene and social distancing,
and treatment. Information must be distributed on treatment centers,
isolation units, school closures, and restrictions on public gatherings
and transportation. Messages of all types will be updated and renewed
more frequently. Cooperation from the commercial media is expected. All
materials must be produced in multilingual formats. Staffing for the JIC
will be increased. Also, a reverse 911 program may be implemented.
Elements of cost for a city of 5 million are provided in the table on
the following page.

Outbreak Crisis Messaging

  --------------------------------- ----------------- --------------------
  Newspaper inserts, brochures,                       
  pamphlets, and flyers                               

  replacement cycle, years          0.25              

  target audience fraction          1                 

  number of items                   10                

                                    unit cost         extended cost

  layout and design                 \$2,000.00        \$80,000.00

  printing                          \$0.48            \$96,000,000.00

  distribution                      \$0.25            \$50,000,000.00

                                                      \$146,080,000.00

  Video and Audio Public Service                      
  Announcements                                       

  replacement cycle, years          0.02              

  number of messages                5                 

  languages                         3                 

  frequency, per year               1000              

                                    unit cost         extended cost

  Design and production             \$10,000.00       \$7,500,000.00

  broadcasting                      \$0.00            \$0.00

                                                      

  Use of U.S. Department of         In-place, no      
  Homeland Security PrepNet         specific pan-flu  
                                    costs             

  Reverse 911                       unit cost,        extended cost
                                    \$/call           

                                    0.2               \$500,000.00

  fraction called                   0.5               

  Web-based information                               

  servers to handle increased       1                 per 5,000 population
  demand for information                              

                                    Unit cost         extended cost
                                    \$/server         

                                    4000              \$4,000,000.00

  maintenance                       500               \$500,000.00

  Joint Information Center focused                    
  on Pan flu                                          

  staff per center                  16                

  Population per center             3000000           

  cost per staff                    75000             

  total operating cost for one year                   \$2,000,000.00

                                                      

  Total annual cost during pandemic                   \$160,580,000.00
  --------------------------------- ----------------- --------------------

## School Closure {#school-closure .unnumbered}

##### Intervention

Temporarily close public and private schools to reduce the attack rate
of the disease by avoiding contacts typical of those experienced in
schools. Of the 3 Models of Infectious Disease Agent Study (MIDAS)
researchers (Ferguson and Glass), 2 predict 30 percent of all infections
will occur in schools, a plurality of the infections. Glass predicts
that infants are twice as infectious as adults and teenagers are 1.5
times as infectious. School closure, especially in combination with
other nonpharmaceutical interventions could significantly reduce and
delay the attack rate of a pandemic influenza outbreak.[^116]

##### Implementation

###### Responsibility and Authority 

Federal, state, and local public health departments have authority to
close schools. The local school boards, in conjunction with or separate
from private school leadership bodies, can close their own doors. The
actual decision will almost definitely be made between all of these
bodies and the elected leaders of those jurisdictions. These leaders
will probably aim to make one decision for all the schools of a region.
Because each college, junior college, and university has its own
independent leadership and is often an afterthought in current
discussion, it is less likely they will be included in an attempt at a
singular decision. These schools also have a layered choice of canceling
classes before closing the school, the latter being an extreme step they
are unlikely to consider. Timing is critical. Longini concludes that
school closures must occur before reaching the 3-percent attack rate in
a community.[^117] By 10 percent, the school closures will have no
effect.

###### Major Steps

-   Conduct meetings among local public health departments and local
    public and private school authorities and decide to act alone or in
    concert with other jurisdictions; these local jurisdictions will
    look to the State Board of Education for information and guidelines

-   Establish plans for resumption of classes, perhaps based on
    immunization records

-   Negotiate with teachers unions on their pay, distance learning, and
    immunization during the closure

-   Negotiate new state and federal funding formulas, not based on
    absenteeism, and develop national requirements for college
    admissions recognizing various local closure durations

###### Required resources

-   Funding may be needed to support extended school year

-   Specialized manpower may be needed to provide distance-learning
    systems

-   Logistical support may be required including:

-   Continuing free meals to at-risk kids

-   Keeping the kids home and out of the malls

-   Quarantined or social-distanced daycare may be needed for parents
    who are poor, first responders, public health providers, or critical
    infrastructure personnel

###### Problems and Issues

-   Research by Steven Bankes of the CDC concludes that a series of
    smaller and less-costly interventions is superior to school
    closure[^118]

-   Compliance levels; for example, if schools stay open, handling
    teacher and student absenteeism in an equitable manner will present
    challenges. If schools close, some parents may insist or unwittingly
    continue to send their kids to school and some teachers may insist
    on attending to serve those students.

-   The decision on school closure, which is likely to be a product of a
    bureaucracy of bureaucracies, must be made ***quickly*** as a
    pandemic is just starting in a community or it is not effective

-   Will higher education institutions be subject to the same closure
    policies?

-   School closure must be in conjunction with a level of household
    quarantine or the overall attack rate could be only slightly reduced
    as children infect each other at other social events

-   Would a jurisdiction's Public Health Department close local schools
    without their consent?

-   If schools close, will buildings remain open to serve meals or
    provide a safe haven in dangerous neighborhoods?

```{=html}
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```
-   A survey by the Harvard School of Public Health found that 25
    percent of students do not have anyone to take care of them at
    home[^119]

-   The Chicago Public Schools serve 400,000 meals per day, with at
    least 80 percent of those going to children who may not get any
    other meal

###### Effectiveness

The effectiveness of school closure is not widely debated. Only Bankes
appears dubious and many have vocally disagreed with his research.[^120]
As previously noted, school closure must be done in conjunction with a
level of household quarantine or the overall attack rate could be only
slightly reduced as children infect each other at other social events.

The compliance rate will probably not exceed 75 percent. A Harvard study
shows school closure is the public health mandate during a pandemic with
the lowest acceptance and, hence, the lowest likely compliance.[^121]
Compliance by students may decrease with increasing age. Some honor-roll
teenagers, near the end of high school, and college students, focused on
a career, may be unwilling to have their career plans delayed by a year.

School closures are time-sensitive; schools need to close at the
pandemic onset.

###### Duration

Correct closure duration is difficult to predict, because influenza can
travel in multiple waves. Schools definitely would prefer to close only
once and not have to engage the complex bureaucracy multiple times.

The feasible duration of school closure is 90 to 300 days. Some of the
factors to be considered, in addition to the persistence of the
pandemic, are

-   Assuming that supplies will be limited, state and local government
    will be receiving vaccine and antiviral shipments over the course of
    months rather than days or weeks. Immunization is desirable before
    reopening schools.

-   The slow arrival of prophylaxis will stretch the distribution
    process over nearly a year in highly populated states and large
    municipalities

-   Typical summer closure is 2.5--3.5 months, and there will be some
    pressure to keep the duration of school closure less than that
    length so the closure can be made up over the summer

-   Epidemiology---The speed of the spread of the disease through a
    group of communities that close their schools together will
    influence the duration of the closure

-   Stockpiles of antivirals and vaccines---Speed of distribution and
    total stockpile size in a group of communities that closed their
    schools together will influence the duration of the closure

-   As with other "childhood disease" immunizations, children may be
    kept out of school until they have a record of their vaccination

##### Impacts

School closures will affect all economic sectors because parents of
students are employed in all sectors and will need to provide or arrange
childcare. The types of impacts expected are

-   Worker absenteeism

-   Cost of continuing to make and deliver free lunches

-   Cost of paying teachers and administrators for an extended school
    year

-   Cost of distance learning during closure

-   Funding shortfalls as current formulas are based on absenteeism

-   Delay in flow of newly trained workers into the workforce

Following is text from an analysis of MIDAS and other
disease/nonpharmaceutical intervention models currently available. This
analysis identifies limitations of models generally, but highlights
school closure assumptions as a major shortcoming:

> Another limitation of current models is their focus on a narrow set of
> influenza- related outcome measures, which ignores the broader
> cost-benefit tradeoffs of alternative intervention strategies. For
> example, one might weigh the costs associated with a school closure
> against its benefits. Costs need not necessarily be measured in
> dollars; costs can also include other benefits forgone (e.g., health
> benefits). The potential benefits of school closing might take the
> form of reductions in influenza transmission to the adults living in
> households that include school-age children. However, the costs or
> risks associated with extended school closings are largely
> non-influenza-related and mostly affect the children themselves. For
> example, some might experience increased exposure to violence in
> communities or poor nutrition due to lack of free or subsidized school
> lunches. These costs would not be captured in a model that focuses
> only on influenza-related outcomes. It would be important to weigh
> benefits and costs for epidemics of different severity. Indeed,
> employing a broad range of social interventions may entail a cost
> greater than the pandemic itself.[^122]

##### School Closure Costs

For reasons cited above, including employee contracts, building
maintenance and alternative uses, child care, and security, it is
unlikely that temporary school closures will result in significant
savings from operating budgets. Instead, CIPDSS has taken the
perspective that operating costs will remain close to normal, but
education will not be accomplished. Therefore, the operating budgets
represent a loss, because the education will have to be made up after
the closure at additional cost. A representative unit cost (per student)
can be derived from the operating budget of the Chicago schools based on
the assumption that closure will result in little change in budget but
full loss of the product, education, during the closure.

> Chicago Public Schools (CPS)
>
> FY 2007 Operational Budget: \$4.6 billion
>
> CPS Budget Allocations
>
> Federal 18% \$828,000,000 (reimbursement)
>
> State 38% \$1,748,000,000 (grant programs)
>
> Local 44% \$2,024,000,000 (38% property tax; corporate taxes,
> lunchroom fees, miscellaneous tax)
>
> Summary: If CPS had to close for 300 days, it would lose \$3.7 billion
> (\$12,602,739 per day). Of the \$3.7 billion in losses, federal
> reimbursement losses would total \$680,547,900.
>
> CPS Operational Budget - Per Day Cost
>
> \$4.6 billion/365 = \$12,602,739/day
>
> Operational budget loss estimates:
>
> 1 day = \$12,602,739
>
> 1 week/7 days = \$88,219,173
>
> 6 weeks/42 days = \$529,315,038
>
> 8 weeks/56 days = \$705,753,384
>
> 12 weeks/84 days = \$1,058,630,076[^123]
>
> 300 days = \$3,780,821,700
>
> Operational losses--federal allocation (student reimbursement)
>
> CPS Federal Budget allocation at 18 percent or \$828 Million
>
> Federal reimbursement loss estimates:
>
> \$828 M/365 = \$2,268,493/day
>
> 1 week = \$15,879,451
>
> 6 weeks = \$95,276,706
>
> 8 weeks = \$127,035,608
>
> 12 weeks = \$190,553,412[^124]
>
> 300 days = \$680,547,900

Sources: Chicago Public Schools FY 2007 Budget; Federal Emergency
Management Agency (FEMA) State and Local Mitigation Planning Guide:
identifying hazards and estimating losses.

Based on a 2000 enrollment of 435,470, the loss would be about \$10,600
per student per year for a full year closure.

Additional cost data from Montgomery County, Maryland, are available in
similar detail. Unit cost is \$12,422 per student per year.

## Home Isolation and Quarantine {#home-isolation-and-quarantine .unnumbered}

##### Intervention

This mitigation measure applies public health system-mandated or
voluntary home isolation of confirmed or presumed influenza cases and
home quarantine of their contacts (pre-symptomatic exposed persons),
respectively. The goal of these interventions is to reduce community
transmission from members of households in which there is a person ill
with pandemic influenza. Members of households in which there is an ill
person may be at increased risk of becoming infected with a pandemic
influenza virus. As determined on the basis of known characteristics of
influenza, a significant proportion of these persons may shed virus and
present a risk of infecting others in the community despite being
asymptomatic or only having a minimally symptomatic illness that is not
recognized as pandemic influenza disease. Thus, members of households
with ill individuals may be required to stay home for an incubation
period following the end of the infectious period of the infected
household member. If other family members become ill during this period,
the recommendation is to extend the home quarantine for another
incubation period, starting from the time that the last family member
becomes ill. In addition, consideration may be given to combining this
intervention with provision of influenza antiviral medication to persons
in quarantine if such medications are effective and quantities are
sufficient and a feasible means of distributing them is in place.[^125]

##### Implementation

###### Responsibility and Authority 

Many of the actions that may be warranted during a pandemic influenza
will require legal authority; most notably, enacting quarantine and/or
isolation.[^126] Numerous federal and state statutes authorize public
health actions to address pandemic influenza.[^127] The federal
government has statutory authority to order a quarantine to prevent the
transmission of communicable diseases across national or state
boundaries.[^128] The HHS provides general guidance and delegates
responsibility for preparedness to the state and municipal levels.[^129]
The states, which enact quarantine statutes pursuant to their police
powers, are primarily responsible for quarantine within their borders.
Local health departments have responsibilities on the frontline for
responding to public health emergencies, although the role and
organization of local heath departments varies considerably across the
U.S.[^130]

###### Major Steps

-   Develop adequate statutory authority and administrative procedures
    for all movement restrictions and monitoring measures for confirmed
    and suspected pandemic influenza cases and their contacts

-   Implement protocols for imposing, maintaining (including enforcing
    when applicable), monitoring, and terminating home isolation or
    quarantine

-   Set up monitoring/contact systems to ascertain and record compliance
    and health status of quarantined persons

-   Implement procedures for providing medical care, food needs, and
    other essential services for those isolated and quarantined

-   Provide N95 masks, disposable thermometers, and other medical
    supplies to households where persons are providing home care to
    patients

-   Provide public messages explaining the purpose and implementation
    methods for quarantine

-   Provide individuals requested to self-isolate with information
    regarding infection control, symptom monitoring and identification,
    and the importance of health department notification of symptom
    development

-   Address where those who refuse to submit to home
    isolation/quarantine shall be held and who will care for them

-   Address income loss for families experiencing isolation/quarantine

###### Required Resources 

-   Quarantining contacts will require extensive public health
    resources. The success of quarantining as a containment and control
    strategy is contingent upon early implementation, the thoroughness
    of contact tracing, and ongoing monitoring. When combined with the
    demands of providing direct services, the data management of
    isolation/quarantine and communications between all affected parties
    can be overwhelming for health department staff.[^131]

-   Delivery systems will be required for providing essentials such as
    food and medicine (both prescription and nonprescription drugs) to
    quarantined households. Needed medical supplies may include masks
    and other medical consumables, antivirals, thermometers, and other
    symptom monitoring supplies/equipment. Other supportive services
    will be needed, such as transportation to medical treatment. In
    addition, vital, life-sustaining supports will need to be continued
    for vulnerable populations, such as the frail elderly, disabled,
    homeless, and so forth). Mental health and psychological support
    services also may be required by those facing extended quarantine
    periods.[^132]

-   State and local law enforcement agencies, with assistance from their
    state's National Guard as needed, may be needed to enforce
    quarantines.[^133] Based on the Severe Acute Respiratory Syndrome
    (SARS) experience, which showed that most people will comply, it is
    anticipated that a majority of individuals will comply with
    quarantine or isolation orders.[^134]

-   At least daily contact will be made by a representative of the
    health department to ascertain situation and needs. This could be by
    phone or in person for situations when the risk of exposure and
    subsequent development of the disease is high and the risk of
    delayed recognition of symptoms is moderate.[^135]

-   Personal protective and communications equipment will be required
    for workers placed at risk because their job duties require them to
    impose, maintain, enforce, monitor, or terminate quarantine

-   Phone lines and a hotline for notification of symptoms or personal
    needs will be required to monitor and respond to isolated and
    quarantined individuals and households.[^136]

-   Protocols, plans, standards, and so forth, will need to be developed
    to manage cases and contacts (such as plans that address
    telecommunications, essential services, and financial support)[^137]

-   For those isolated and quarantined, 1 set of instructional handouts
    per household, 1 disposable thermometer per person in the household,
    and 2 masks per person per day for 14 days will need to be supplied.

###### Problems and Issues

-   The U.S. has not implemented a large-scale quarantine during the
    past 80 years and, consequently, there is little to no foundation
    from which to assess U.S. best practices or lessons learned.
    However, it was necessary to initiate 21 days of isolation (but not
    quarantine) for dozens of people in the monkey pox outbreak of 2003.
    Also, while the use of quarantine during the SARS epidemic provides
    some valuable insights, there are important differences between the
    epidemiologic parameters of the SARS and influenza viruses. In
    particular, influenza has a serial interval of 2--4 days and
    infectivity is maximal early in the illness, while for SARS the
    serial interval is 8--10 days and infectivity peaks during week 2 of
    the illness. These factors allow little time for instituting the
    quarantine and isolation interventions that were essential for
    controlling SARS.[^138]

-   Requirements for success of this intervention include the prompt and
    accurate identification of an ill person in the household for
    isolation, identification of their contacts for quarantine,
    voluntary compliance with isolation and quarantine by household
    members, commitment of employers to support the recommendation that
    employees living in a household with an ill individual stay home,
    the ability to provide needed support to households that are under
    isolation or voluntary quarantine (such as food, supplies, and
    medications), guidance for infection control in the home, and
    actions to enforce involuntary isolation and quarantine when needed
    (such as guards, bracelets, and facilities).[^139]

-   There may be difficulty in reaching a large proportion of the
    affected households. Acquisition of adequate mask and thermometer
    supplies may also be difficult without some stockpiling.

-   Quarantine is not effective in controlling multiple influenza
    outbreaks in large, immunologically naive populations because the
    disease spreads too rapidly to identify and control chains of
    transmission. Even if quarantine was somewhat effective in
    controlling influenza in large populations, it would not be feasible
    to implement and enforce with available resources and would damage
    the economy by reducing the workforce. Isolation is only partially
    effective if there is an asymptomatic period of viral shedding
    because cases will be missed.[^140]

-   First responders and other sick health workers should be isolated or
    they will spread disease to naive patients. Exposed workers who are
    not ill can be closely monitored (for example, daily temperatures)
    instead of quarantined, unless there is an asymptomatic shedding
    period early in the infection course. In that case, they may need at
    least a temporary quarantine from the shortest incubation period to
    the end of the asymptomatic infection period.

-   The occurrence of large numbers of cases would prevent contact
    tracking and quarantine monitoring due to inadequate numbers of
    health department staff. Although there may be new technologies to
    assist, they have not been tested.

-   Quarantine strategies are limited to the extent that they can be
    implemented only after cases are identified[^141]

-   Adherence to ethical principals in use of quarantine during
    pandemics, along with proactive anti-stigma measures, would need to
    be assured[^142]

-   The risk of noncompliance increases as the duration increases[^143]

-   Effectiveness and compliance depend on public confidence in
    disease-control policies and information

-   Quarantine or isolation may infringe on personal movement and cause
    a feeling of isolation from family and friends and may also lead to
    loss of income or employment[^144]

-   Many view the community hospital as a \"safe haven,\" a place to go
    for food, shelter, protection, and medical attention. However, in
    the event of transmissible infectious diseases such as pandemic flu,
    the traditional concept of safe haven may not be applicable. The
    public will need to be educated about the concept of the home as a
    safe haven.[^145]

##### Effectiveness

###### Percent of Population Affected by or Participating in the Intervention by Age Group

The percentage of population affected by or participating in the
intervention by age group would vary with the number of confirmed cases.
Assuming an average household size of 3 persons, it would be reasonable
to expect that 2 people would be quarantined for every reported case.
The program would also need to consider potential quarantine of
coworkers, schoolmates, and others.

###### Compliance Rate

The compliance rate is the proportion of the affected or
potentially-affected population that is likely to comply with
constraints for the prescribed duration of the intervention. The
estimated range is 70 to 90 percent.

During the SARS outbreaks, public health departments were required to
work closely with law enforcement personnel to ensure compliance with
quarantine measures. Methods of enforcement varied widely, as did the
success of the chosen tactics. For example, in Toronto, where the
transmission of SARS was limited primarily to hospitals and households
that had contact with patients, the public was cooperative with home
quarantine orders. In other cases, however, quarantines caused public
protest and even violence.[^146]

##### Duration

###### Feasible Duration of Intervention Measure

For the quarantine of individuals exposed to patients with pandemic
influenza, the contact remains separated from others for a specific
period (up to 10 days after exposure) during which time the contact is
regularly assessed for signs and symptoms of disease.[^147]

###### Factors Limiting Duration

-   Declining public compliance as the quarantine period increases

-   Workforce to support both life needs and compliance

-   Insufficient supplies of masks or thermometers

##### Impacts

###### Sectors Affected

All sectors of the economy will be affected.

###### Type of Impact

-   Worker absenteeism

-   High costs for system administration, household monitoring,
    household supply, and so forth in the public health sector

-   Public health worker illness

Cost estimates for support of a home quarantine program are provided in
the following table for a city of 5 million. For these estimates, CIPDSS
assumed that 25 percent of households are expected to quarantine at some
time during the pandemic.

Home Isolation and Quarantine

  ---------------------------------------- -------- ---------------------
  Fraction of households affected          0.25     total households

  Duration, weeks                          6        480769

  Food, \$/week                            \$150    \$432,692,308

  Medical Supplies, \$/week                \$50     \$144,230,769

  Income replacement \$/week               \$446    \$1,929,807,692

  Total                                             \$2,506,730,769
  ---------------------------------------- -------- ---------------------

##### Key Terms

**Isolation:** Isolation is the separation of a person or a group of
persons infected or believed to be infected with a contagious disease to
prevent the spread of infection. Ill persons are usually isolated in a
hospital, but they may also be isolated at home or in a designated
community-based facility, depending on their medical needs.[^148]

**Quarantine**: Quarantine is the separation and restriction of movement
or activities of persons who are not ill, but who are believed to have
been exposed to infection, for the purpose of preventing transmission of
diseases. People are usually quarantined in their home, but may also be
quarantined in community-based facilities.[^149]

## Social Distancing Policies---Voluntary Social Withdrawal {#social-distancing-policiesvoluntary-social-withdrawal .unnumbered}

##### Intervention

Individuals isolate themselves at home to reduce their risk of exposure
to influenza.

##### Implementation

###### Responsibility and Authority

Individuals make self-determined decisions to not work outside the home,
avoid public gatherings, and so forth.

###### Major Steps

-   Develop policies, plans, and guidelines for advising the public
    about the practicalities, best practices, and benefits of voluntary
    social withdrawal

-   Ensure information regarding social withdrawal is compatible with
    restricted transportation and public gathering guidelines

-   Implement the decision to self-isolate

###### Required Resources

-   Funding (public and/or private) to develop and disseminate
    information regarding voluntary social withdrawal

###### Problems/Issues

-   Economic and productivity impacts of protective self-isolation
    include absenteeism and reduced participation in the economy.

#####  Effectiveness

The percentage of population affected by or participating in the
intervention by age group:

> Child (0--4): 50--75 percent
>
> Youth (5--18): 25--50 percent
>
> Adult (19--65): 10--30 percent
>
> Senior (66+): 20--50 percent
>
> Compliance rate: 10--40 percent

##### Duration

###### Feasible Duration of Intervention

-   10--30 days

###### Factors Limiting Duration

-   Ability of individuals/families to re-supply food and staples

-   Loss of household income

##### Impacts

###### Sectors Affected

-   All

###### Type of Impact

-   Cost to develop and disseminate information regarding voluntary
    social withdrawal, which is included in the public information and
    education description above

-   Lost value-added (including lost revenue to business and voluntary
    organizations); this cost is already included in CIPDSS calculations

-   Costs incurred to support families in self-isolation, including
    delivery of food and medicine. Without such support, compliance
    levels are expected to be far lower.

## Social Distancing Policies--Restrict Large Public Gatherings {#social-distancing-policiesrestrict-large-public-gatherings .unnumbered}

##### Intervention

Government will implement protective measures that reduce risks of
influenza exposure by restricting large public gatherings.[^150]

##### Implementation 

###### Responsibility and Authority

-   State and, in some cases, local authorities can issue orders
    restricting large public gatherings. State law (emergency management
    statutes and public health statutes) must be consulted to determine
    specifically what powers are held by which authorities.[^151]

-   The endorsement of state and federal authorities may help validate
    local recommendations to restrict public gatherings through the
    moral and scientific support of a larger jurisdiction

-   In the absence of coercive authority or as an alternative to it,
    voluntary compliance by businesses and organizations such as
    churches may be an important component of public protection

###### Major Steps

-   Develop recommendations for limiting public gatherings based on
    current knowledge of disease threat and vectors for
    transmission[^152]

-   Disseminate public education information ***prior*** to emergencies,
    including public-gathering guidelines

-   Distribute protective orders and/or recommendations ***during*** an
    emergency

-   Implement orders and/or recommendations in response to infection
    phases

###### Required Resources

-   Legal authorization to restrict public gatherings

-   Funding (public and/or private) to develop and disseminate
    public-gathering guidelines

-   Commitment of business and organization personnel and resources to
    participate as stakeholders in the development of public-gathering
    guidelines

-   Ability to deputize larger-than-usual public health workforce to
    report and enforce violations (such as a public health authority to
    direct police or other workers)

###### Problems and Issues

-   Lack of political will to develop and disseminate public-gathering
    prohibitions and guidelines

-   Resistance by the public, business community, or other organizations
    to restrictions on public gatherings[^153]

-   Lost business revenue, employment, and community morale caused by
    absence of public gatherings

##### Effectiveness

Percentage of population affected by or participating in the
intervention by age group:

> Child (0--4): 40--60 percent (with child care included)
>
> Youth (5--18): 40--60 percent (assuming schools are closed but youth
> gatherings are not enforced)
>
> Adult (19--65): 40--60 percent
>
> Senior (66+): 40--60 percent
>
> Compliance rate: 40--80 percent

##### Duration

###### Feasible Duration of Intervention

-   30--90 days

###### Factors Limiting Duration

-   Public resistance and lack of political will

-   Loss of revenue to sports, entertainment, church, and other
    organizations; may be able to partly address with digital
    alternatives

-   Loss of value-added to the economy[^154]

##### Impacts

###### Sectors Affected

-   Services

-   Manufacturing

-   Government

-   Voluntary services

-   Entertainment

-   Religion

-   Education

###### Type of Impact

-   Cost to develop public gathering reduction guidelines

-   Cost to disseminate public gathering information and guidelines

-   Lost value-added (including lost revenue to business and voluntary
    organizations)

Social Distancing Restriction of Public Gatherings

  ----------------------------- -------- -------------- --------------------
                                         Unit Cost or   Extended Cost or
                                         Revenue        Revenue

  Duration, weeks               12                      

  Small events per week per     10       15             
  100,000                                               

  Average attendance            500                     \$900,000.00

  Large events per week per     10       40             
  100000                                                

  Average attendance            10,000                  \$48,000,000.00

  Theater showings per week per 500      7              
  100,000                                               

  Average attendance            100                     \$4,200,000.00

  Churches services per week    40       10             
  per 100,000                                           

  Average attendance            500                     \$2,400,000.00

                                         Total          \$55,500,000.00
  ----------------------------- -------- -------------- --------------------

## Social Distancing Policies---Restrict Public Transportation[^155] {#social-distancing-policiesrestrict-public-transportation .unnumbered}

##### Intervention

Transportation authorities will implement local public policies that
reduce the likelihood of influenza exposure contacts by restricting
public transportation.[^156]^,^[^157] Public transportation restrictions
could also be implemented by altering schedules or service such that
passenger density is reduced (for example, extending hours of rail
service, modifying bus routes, and waiving high-occupancy vehicle
\[HOV\] lane restrictions).

##### Implementation

###### Responsibility and Authority

-   State or local government (commuter rail and municipal buses)

-   Federal government and business (airlines and interstate rail)

###### Major Steps

-   Develop scheduling and service reduction options (service providers)

-   Disseminate schedule and service reduction information

-   Implement transportation service reduction in response to infection
    phases

-   Provide public information regarding the service changes

###### Required Resources

-   Funding (public and/or private) to disseminate service reduction
    information

-   Manpower required of transportation service providers to develop
    service revision policies, modify services, and revise schedules

###### Problems and Issues

-   Public resistance and lack of political will to restrict freedom of
    movement

-   Need for management and labor support to reduce public
    transportation service

-   Loss of revenue to transportation businesses and agencies that
    typically have cash-flow problems

-   Loss of income to idled transportation workers

-   Disruption of businesses whose workers commute; absenteeism higher
    than normal

-   Loss of revenue to retail businesses whose customers use public
    transportation to get to them

-   Increased traffic congestion and delays

##### Effectiveness

Percentage of population affected by or participating in the
intervention by age group

> Child (0--4)
>
> Youth (5--18): 5--25 percent
>
> Adult (19--65): 20--50 percent
>
> Senior (66+): 20--40 percent
>
> Compliance rate: 80--90 percent

##### Duration

###### Feasible Duration of Intervention

-   30--90 days

###### Factors Limiting Duration

-   Public resistance and lack of political will

-   Loss of revenue by transportation businesses and agencies

-   Loss of value-added to the economy

##### Impacts

###### Sectors Affected

-   Services

-   Government

###### Type of Impact

-   Cost to develop scheduling and service reduction options

-   Cost to disseminate schedule and service reduction information

-   Lost value-added (including lost revenue to service providers);
    ***already included in CIPDSS calculations***

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# Appendix D: Summary of Cost Estimates {#appendix-d-summary-of-cost-estimates .unnumbered}

Cost Estimates for Non-Pharmaceutical Mitigation Measures[^158]

  --------------------------------------- --------------- ----------------
  ***Pre-Outbreak Education and Awareness                 
  Program Costs***                                        

  Newspaper inserts, brochures,                           
  pamphlets, and flyers                                   

  replacement cycle, years[^159]          2               

  target audience fraction                0.7             

  number of items                         10              

                                          unit cost       extended cost

  layout and design                       \$2,000.00      \$10,000.00

  printing                                \$0.48          \$1,680,000.00

  distribution                            \$0.25          \$875,000.00

                                          total           \$2,565,000.00

  Video and Audio Public Service                          
  Announcements                                           

  replacement cycle, years                0.25            

  number of messages[^160]                5               

  languages                               3               

  frequency, per year                     1000            

                                          unit cost       extended cost

  Design and production                   \$35,000.00     \$2,100,000.00

  broadcasting[^161]                      \$0.00          \$0.00

  Joint Information System Pandemic flu                   
  capability                                              

                                          staff is        
                                          in-place, no    
                                          additional cost 

  Web-site                                staff-years     extended cost

  \$/staff year, computer specialists     \$150,000.00    

  Development and maintenance of a        10              \$1,500,000.00
  standard format central National                        
  information site and state/local                        
  sites.[^162]                                            

  RSS feeds[^163]                         5               \$750,000.00

  Establishment and utilization of social 0               \$0.00
  networking sites[^164]                                  

  Total Annual Cost, Pre-pandemic                         \$6,915,000.00
  --------------------------------------- --------------- ----------------

  --------------------------------------- --------------- -----------------
  ***Outbreak Crisis Messaging***[^165]                   

  Newspaper inserts, brochures,                           
  pamphlets, and flyers                                   

  replacement cycle, years                0.25            

  target audience fraction                1               

  number of items                         10              

                                          unit cost       extended cost

  layout and design                       \$2,000.00      \$80,000.00

  printing                                \$0.48          \$19,200,000.00

  distribution                            \$0.25          \$10,000,000.00

                                          Sub-total       \$29,280,000.00

  Video and Audio Public Service                          
  Announcements                                           

  replacement cycle, years                0.02            

  number of messages                      5               

  languages                               3               

  frequency, per year                     1000            

                                          unit cost       extended cost

  Design and production                   \$10,000.00     \$7,500,000.00

  broadcasting                            \$0.00          \$0.00

                                          Sub-total       \$7,500,000.00

  Utilization of DHS PrepNet              In-place, no    
                                          specific        
                                          pan-flu costs   

  Reverse 911[^166]                       unit cost,      extended cost
                                          \$/call         

                                          0.2             \$100,000.00

  fraction called[^167]                   0.5             

  Web-based information                                   

  servers to handle increased demand for  1               per 5,000
  information                                             population

                                          Unit cost       extended cost
                                          \$/server       

                                          4000            \$800,000.00

  maintenance                             500             \$100,000.00

  Joint Information Center focused on Pan                 
  flu                                                     

  staff per center                        16              

  Population per center                   3000000         

  cost per staff                          75000           

  total operating cost for one year                       \$400,000.00

  Total during pandemic                                   \$38,180,000.00
  --------------------------------------- --------------- -----------------

  ------------------------------- ---------- ------------- -----------------
  ***Social Distancing                                     
  Restriction of Public                                    
  Gatherings***[^168]                                      

                                             unit cost or  extended cost or
                                             revenue       revenue

  Duration, weeks                 12                       

  small events per week per       10         15            
  100,000                                                  

  average attendance              500                      \$900,000.00

  large events per week per       10         40            
  100000                                                   

  average attendance              10000                    \$48,000,000.00

  theater showings per week per   500        7             
  100,000                                                  

  average attendance              100                      \$4,200,000.00

  churches services per week per  40         20            
  100,000                                                  

  average attendance              500                      \$4,800,000.00

                                             Total         \$57,900,000.00
  ------------------------------- ---------- ------------- -----------------

+------------------------------------+--------------+-----------------+
| *Home Isolation and                |              |                 |
| Quarantine*[^169]                  |              |                 |
+------------------------------------+--------------+-----------------+
| Fraction of households affected    | 0.5          | total           |
|                                    |              | households      |
+------------------------------------+--------------+-----------------+
| Duration, weeks                    | 6            | 192307.6923     |
+------------------------------------+--------------+-----------------+
| Food, \$/week                      | 150.00       | \               |
|                                    |              | $173,076,923.08 |
+------------------------------------+--------------+-----------------+
| Medical Supplies, \$/week[^170]    | 50.00        | \$57,692,307.69 |
+------------------------------------+--------------+-----------------+
|                                    | Total        |   -----         |
|                                    |              | --------------- |
|                                    |              |   \$1           |
|                                    |              | ,002,692,307.69 |
|                                    |              |                 |
|                                    |              |   -----         |
|                                    |              | --------------- |
+------------------------------------+--------------+-----------------+

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[^1]: NISAC is part of the U.S. Department of Homeland Security, Office
    of Infrastructure Protection, Infrastructure Analysis and Strategy
    Division.

[^2]: CIPDSS is funded by the U.S. Department of Homeland Security,
    Science and Technology Directorate.

[^3]: Powell, D., B. Bush, L. Dauelsberg, D. LeClaire, S. DeLand, and M.
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[^4]: Holmberg, S. D., C. M. Layton, G. S. Ghneim, and D. K. Wagener,
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[^5]: Bush, B., L. Dauelsberg, R. LeClaire, D. Powell, S. DeLand, and M.
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[^6]: Murray, J. D., 1993, *Mathematical Biology*, Springer, New York;
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[^7]: McCraig, L. F., and C. W. Burt, 2005, "National Hospital
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[^8]: NCHS (National Center for Health Statistics), 2005, *Health,
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[^11]: NISAC/CIPDSS (National Infrastructure Simulation & Analysis
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[^12]: Ewers, M., and L. R. Dauelsberg, 2007, "Pandemic Influenza
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[^13]: BEA (Bureau of Economic Analysis), 2006, "U.S. Economic
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[^16]: The reviewers were John Glasser of the Centers for Disease
    Control and Prevention and Captain Terry Adirim of the DHS Office of
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[^17]: Mills, C. E., J. M. Robins, M. Lipsitch, 2004, "Transmissibility
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[^18]: Murray, 1993 (See Footnote 6)

[^19]: Spicer, C. C., 1979, "The Mathematical Modelling \[sp\] of
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[^20]: Mills et al., 2004 (See Footnote 7)

[^21]: The interquartile range is the range of the middle 50 percent of
    the data and is defined as the 75^th^ percentile minus the 25^th^
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[^22]: Collins, S. D., 1931, "Age and Sex Incidence of Influenza and
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[^23]: Noble, G. R., 1982, "Epidemiological and Clinical Aspects of
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[^24]: Simonsen, L., M. J. Clark, L. B. Schonberger, N. H. Arden, N. J.
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[^25]: Board of Global Health, 2005, "The Threat of Pandemic Influenza:
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[^26]: CDC (U. S. Centers for Disease Control and Prevention), 2007
    "Interim Pre-Pandemic Planning Guidance: Community Strategy for
    Pandemic Influenza Mitigation in the United States---Early,
    Targeted, Layered Use of Nonpharmaceutical Interventions," February,
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[^27]: Chan, P. K. S., 2002, "Outbreak of Avian Influenza A (H5N1) Virus
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[^28]: Zelicoff, A., 2007, "Avian Flu: Risk of a Human Pandemic from
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[^29]: Frank A. L., L. H. Taber, C. R. Wells, J. M. Wells, W. P. Glezen,
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    **144**:433--41

[^30]: Hall C. B., R. Dolin, C. L. Gala, D. M. Markovitz, Y. Q. Zhang,
    P. H. Madore, F. A. Disney, W. B. Talpey, J. L. Green, A. B.
    Francis, and M. E. Pichichero, 1987, "Children with influenza A
    infection: treatment with rimantadine," *Pediatrics* **80**:275--82,
    August

[^31]: Foy H. M., M. K. Cooney, I. D. Allan, and J. K. Albrecht, 1987,
    "Influenza B in households: virus shedding without symptoms or
    antibody response," *American Journal of Epidemiology*
    **126**:506--15; and Monto A. S., J. S. Koopman, I. M. Longini, Jr.,
    1985, "Tecumseh study of illness. XIII. Influenza infection and
    disease, 1976--1981," *American Journal of Epidemiology*,
    **121**:811--22 (hereinafter referred to as Monto et al., 1985)

[^32]: Monto et al., 1985 (See Footnote 31)

[^33]: Hayden F. G., R. Fritz, M. C. Lobo, W. Alvord, W. Strober, and S.
    E. Straus, 1998, "Local and systemic cytokine responses during
    experimental human influenza A virus infection. Relation to symptom
    formation and host defense," *The Journal of Clinical Investigation*
    **101**:643--9 (hereinafter referred to as Hayden et al., 1998)

[^34]: CDC, 2007 (See Footnote 26)

[^35]: Ibid.; Cooper, B. S., R. Pitman, W. J. Edmunds, and N. Gay, 2006,
    "Delaying the international spread of pandemic influenza," *PLoS
    Medicine* **3**:845-855; and Wu, J. T., S. Riley, C. Fraser, and G.
    M. Leung, 2007, "Reducing the impact of nest influenza pandemic
    using the household-based public health interventions," *PLoS
    Medicine* **3:** 1532--1540 (hereinafter referred to as Cooper et
    al., 2006)

[^36]: CDC, 2007 (See Footnote 26)

[^37]: HHS (U.S. Department of Health and Human Services), 2005, *HHS
    Pandemic Influenza Plan*, November,
    <http://www.hhs.gov/pandemicflu/plan/pdf/HHSPandemicInfluenzaPlan.pdf>
    (hereinafter referred to as HHS, 2005)

[^38]: WHO (World Health Organization), 2005, "H5N1 avian influenza \--
    first steps towards development of a human vaccine," World Health
    Organization, 12 August (hereinafter referred to as WHO, 2005)

[^39]: Zelicoff, A., 2007, "Avian Flu: Risk of a Human Pandemic from
    H5N1?" *World Bank Working Paper Avian Flu Task Force,"* New Mexico

[^40]: Elder A. G., B. O\'Donnell, E. A. McCruden, I. S. Symington, and
    W. F. Carman, 1996, "Incidence and recall of influenza in a cohort
    of Glasgow healthcare workers during the 1993-4 epidemic: results of
    serum testing and questionnaire," *BMJ* **313**:1241--2; and Glezen
    W. P., W. A. Keitel, L. H. Taber, P. A. Piedra, R. D. Clover, and R.
    B. Couch, 1991, "Age distribution of patients with
    medically-attended illnesses caused by sequential variants of
    influenza A/H1N1: comparison to age-specific infection rates,
    1978-1989," *American Journal of Epidemiology* **133**:296--304

[^41]: WHO, 2005 (See Footnote 38)

[^42]: Hayden et al., 1998 (See Footnote 33)

[^43]: Stohr K., 2005, "Avian influenza and pandemics, research needs
    and opportunities," *New England Journal of Medicine*
    **352**:405--7; and WHO, 2005 (See Footnote 38)

[^44]: Fraser, C., S. Riley, R. A. Anderson, and N. M. Ferguson, 2004,
    "Factors that make an infectious disease outbreak controllable,"
    *National Academy of Sciences* **101:**6146--51 (hereinafter
    referred to as Fraser et al., 2004)

[^45]: Ibid.

[^46]: Anderson, R. M., and R. M. May, 1991, *Infectious diseases of
    humans, Dynamics and control,* Oxford University Press, Oxford,
    England ; and Salpeter, E. E., and S. R. Salpeter, 1998,
    "Mathematical Model for the Epidemiology of Tuberculosis, with
    Estimates of the Reproductive Number and Infection-Delay Function,"
    *American Journal of Epidemiology*, **147**(4):398--406

[^47]: Hethcote, H. W., and P. van den Driessche, 1995, "An SIS epidemic
    model with variable population size and delay," *Journal of
    Mathematical Biology* **34:**177--94

[^48]: Zhang, Z., 2007, "The outbreak pattern of SARS cases in China as
    revealed by a mathematical model," *Ecological Modelling*
    **204**(3&4):420-6, 16 June

[^49]: Due to lack of reliable information on the labor structure for
    other infrastructures, they were omitted from this analysis.

[^50]: HHS, 2005 (See Footnote 37)

[^51]: Ehrenstein, B. P., F. Hanses, and B. Salxberger, 2006, "Influenza
    Pandemic and Professional Duty: Family or Patients first? A Survey
    of Hospital Employees" *BMC Public Health* **6:**311-313

[^52]: Monto, A. S., 2006, "Vaccines and Antivirals in Pandemic
    Preparedness," *Emerging Infectious Diseases,* **12**:55--60
    (hereinafter referred to as Monto, 2006); Schwartz, B., and B.
    Gellin, 2005, "Vaccination Strategies for an Influenza Pandemic,"
    *Journal of Infectious Diseases* **191**:1207--9; and Bresson, J.
    L., C. Perrone, O. Launay, C. Gerdil, M. Saville, J. Wood, K.
    Hoschler, and M. C. Zambon, 2006, "Safety and Immunogenicity of an
    Inactivated Split-Virion Influenza A/Vietnam/1194/2004 (H5N1)
    Vaccine: Phase I Randomized Trial," *Lancet* **367**, 1657--1664, 6
    June (hereinafter referred to as Bresson et al., 2006)

[^53]: Monto, 2006 (See Footnote 52)

[^54]: Stephenson, I., 2006, "H5N1 Vaccines: How Prepared Are We for a
    Pandemic?" *Lancet* **368**:965--6

[^55]: CDC, 2007 (See Footnote 26)

[^56]: Fraser et al., 2004 (See Footnote 44)

[^57]: Ferguson, N. M., D. A. T. Cummings, C. Fraser, J. C. Cajka, P. C.
    Cooley, and D. S. Burke, 2006, \"Strategies for mitigating an
    influenza pandemic,\" *Nature* **442**:448-452 (hereinafter referred
    to as Ferguson et al., 2006)

[^58]: Carrat, F., J. Luong, H. Lao, A. V. Sallé, C. Lajaunie, and H.
    Wackernegel, 2006, "A 'Small-World-Like' Model for Comparing
    Interventions Aimed at Preventing and Controlling Pandemics," *BMC
    Medicine* **4:**26, October

[^59]: Cooper et al., 2006 (See Footnote 35)

[^60]: Stroud, P. D., S. Y. Del Valle, S. M. Mniszewski, J. M. Reise,
    and S. J. Sydoriak, 2007, "EpiSimS Pandemic Influenza Sensitivity
    Analysis (Part of the "National Infrastructure Impacts of Pandemic
    Influenza Phase 2 Report"), Los Alamos National Laboratory
    LA-UR-07-1989

[^61]: Ferguson, N. M., D. A. T. Cummings, S. Cauchemez, C. Fraser, S.
    Riley, A. Meeyai, S. Iamsirithaworn, and D. S. Burke, 2005,
    \"Strategies for containing an emerging influenza pandemic in
    Southeast Asia,\" *Nature* **437**:209-214

[^62]: Ferguson et al., 2006 (See Footnote 57)

[^63]: Fraser et al., 2004 (See Footnote 44)

[^64]: Gani R., H. Hughes H, D. Fleming, T. Griffin, J. Medlock, and S.
    Leach, 2005, "Potential impact of antiviral drug use during
    influenza pandemic," *Emerging Infectious Diseases* **11**:1355-1362

[^65]: Germann, T. C., K. Kadau, I. M. Longini, and C. A. Macken, 2006,
    \"Mitigation strategies for pandemic influenza in the United
    States,\" *Proceedings of the National Academy of Sciences of the
    United States of America* **103**: 5935-5940

[^66]: Glass R. J., L. M. Glass, W. E. Beyeler, and H. J. Min, "
    Targeted social distancing design for pandemic influenza," *Emerging
    Infectious Diseases*, **12**:1671-1681

[^67]: Haber, M. J., D. K. Shay, X. M. Davis, R. Patel, X. Jin, E.
    Weintraub, E. Orenstein, and W. W. Thompson, 2007, \"Effectiveness
    of interventions to reduce contact rates during a simulated
    influenza pandemic,\" *Emerging Infectious Diseases* **13**:581-589

[^68]: Klinkenberg, D., C. Fraser, and J. A. P. Heesterbeek, 2006, \"The
    effectiveness of contact tracing in emerging epidemics,\" *European
    Journal of Epidemiology* **21**:64-65

[^69]: Longini, I. M., M. E. Halloran, A. Nizam, and Y. Yang, 2004,
    \"Containing pandemic influenza with antiviral agents,\" *American
    Journal of Epidemiology* **159**:623-633

[^70]: Menon, D. K., B. L. Taylor, and S. A. Ridley, 2005, \"Modelling
    the impact of an influenza pandemic on critical care services in
    England,\" Intensive Care Society, United Kingdom, *Anaesthesia*
    **60:**952-954

[^71]: Wu, J. T., S. Riley, C. Fraser, and G.M. Leung, 2007, "Reducing
    the impact of nest influenza pandemic using the household-based
    public health interventions," *PLoS Medicine* **3:** 1532--1540

[^72]: This capacity is expanding; WHO suggests an increase in capacity
    from 300 to 350 million doses. Sources: UN (United Nations), 2006,
    *"*Increased Flu Vaccine Production Needed," influenza.un.org, 23
    October (hereinafter referred to as UN, 2006); World Health
    Organization, <http://influenza.un.org/index.asp?PageID=166>; and
    WHO, 2005 (See Footnote 38)

[^73]: 430 million doses are sufficient to envelope the dose
    distribution in all sensitivity cases.

[^74]: The simulation experiments run for this analysis assume that a
    single vaccine dose is dispensed to each recipient. However, it is
    likely that 2 doses separated by about 28 days will be required for
    maximum immunity. This 2-dose "course" suggests a global demand of
    12 billion doses. This distinction between dose and course is
    recognized in the U.S. plans for pandemic influenza.

[^75]: Pre-pandemic vaccine may be purchased on the international
    market.

[^76]: Maloy, S., R. L. Berkelman, G. H. Cassell, M. T. Osterholm, M. A.
    Hamburg, K. I. Berns, R. M. Atlas, and J. M. Campos, 2005, "ASM
    Statement on Pandemic Influenza Plan," American Society for
    Microbiology letter to The Honorable Michael O. Leavitt, Secretary
    of Health and Human Services, 5 December

[^77]: Gellin, B., 2005, "Pandemic Influenza Preparedness," testimony
    before the Subcommittee on Health, Committee on Energy and Commerce,
    U.S. House of Representatives, 26 May

[^78]: Japsen, B., 2005, "U.S. Lags on Bird Flu Vaccine," *Chicago
    Tribune*, Chicago, Illinois (hereinafter referred to as Japsen,
    2005); and Lavenda, L., and P. Barollier, 1006, "Sanofi Pasteur
    Influenza Vaccine Production Tops 170 Million Doses in 2006," Sanofi
    Pasteur, Lyon, France, and Swiftwater, Pennsylvania

[^79]: CIDRAP (Center for Infectious Disease Research and Policy), 2007,
    "Pandemic Influenza" (hereinafter referred to as CIDRAP, 2007a)

[^80]: Mair, M., R. W. Grow, J. S. Mair, and L. J. Radonovich, 2006,
    "Universal Influenza Vaccination: The Time to Act is Now,"
    *Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and
    Science* **4**(1):20--40 (hereinafter referred to as Mair et al.,
    2006)

[^81]: Japsen, 2005 (See Footnote 78)

[^82]: Here, Dr. Chan has made the additional error of referring to
    current trivalent seasonal flu vaccine capacity as trivalent
    pandemic flu vaccine capacity. Such capacity is virtually zero at
    this time. This error has propagated through news reports. The
    correct analysis, as provided by Dr. David Heymann, WHO
    representative for Avian and Pandemic Influenza, is that the maximum
    current capacity of the seasonal vaccine is between 400 and 500
    million doses per year. Note that this shows substantial continued
    growth in capacity over 2006 estimates. Source: Chan, M., 2007,
    "Opening Remarks at the Meeting on Options for Increasing the Access
    of Developing Countries to H5N1 and Other Potential Pandemic
    Vaccines," World Health Organization, Geneva, Switzerland, 25 April

[^83]: Mair et al., 2006 (See Footnote 80); CIDRAP, 2007 (See Footnote
    79)

[^84]: CTV (Canadian Television), 2006, "Making bird flu vaccine proving
    problematic," *Canadian Press*,
    <http://www.ctv.ca/servlet/ArticleNews/story/CTVNews/20060510/bird_flu_vaccine_060510/20060510?hub=Health>,
    10 May

[^85]: Greater than 50 percent of test subjects demonstrated a
    significant immune response. Source: Treanor, J. J., J. D. Campbell,
    K. M. Zangwill, T. Rowe, and M. Wolff, 2006, "Safety and
    Immunogenicity of an Inactivated Subvirion Influenza A (H5N1)
    Vaccine," *The New England Journal of Medicine* **354**:1343--51, 30
    March

[^86]: Parker, G. W., 2007, "Pandemic Influenza Preparedness: Update on
    the Development & Acquisition of Medical Countermeasures," testimony
    before the Committee on Appropriations; Subcommittee on Labor,
    Health, and Human Services; Education and Related Agencies, U.S.
    Senate, 24 January (hereinafter referred to as Parker, 2007)

[^87]: Fauci, A. S., 2005, "Pandemic Influenza: The Road to
    Preparedness," testimony before the Committee on Foreign Relations,
    U.S. Senate, 9 November

[^88]: Some of the stockpile is approaching the end of its shelf life,
    which is about 1 year. Source: Parker, 2007 (See Footnote 86)

[^89]: CIDRAP, 2007a (See Footnote 79)

[^90]: Bresson et al., 2005 (See Footnote 52)

[^91]: Alternatively, the government could respond to the success of an
    adjuvant by reducing the capacity investment. However, contracts are
    already in place to create the capacity needed for 20 million
    courses without the use of an adjuvant.

[^92]: CIDRAP, 2007a (See Footnote 79)

[^93]: AP (Associated Press), 2007, "Vaccine may fight different bird
    flu strains," *The Associated Press*. London, 6 March,
    <http://www.msnbc.msn.com/id/17480849/>; and Manning, A., 2007,
    "Experimental bird flu vaccine could be doubly promising." *USA
    Today*

[^94]: Ghendon, Y., 1994, \"Influenza Vaccines: A Main Problem in
    Control of Pandemics.\" *European Journal of Epidemiology*
    **10**:485--6

[^95]: Heffner, S., 2007, "Biomarket Trends: Cell Culture Landscape
    Changes Rapidly," *GEN (Genetic Engineering & Biotechnology) News,*
    **27**:9, 1 May

[^96]: Ibid.

[^97]: CIDRAP (Center for Infectious Disease Research and Policy),
    2007b, "Panel Supports EU Approval of Cell-Based Flu Vaccine,"
    *CIDRAP News*

[^98]: This Federal goal does not recognize the potential growth rate in
    the United States population. On the high side, the U.S. Census
    Bureau has projected a 2015 population of 350 million. The vaccine
    production capacity goal will not be met until 2011--2013.

[^99]: Parker, 2007 (See Footnote 86)

[^100]: Henry Wang, professor of chemical and biomedical engineering,
    and doctoral student Lyle Lash.

[^101]: UM (University of Michigan) News Service, 2006, "Existing
    Vaccine Facilities Can Handle Flu Pandemic, New Research Suggests"
    Ann Arbor, Michigan, 25 September,
    <http://www.umich.edu/~urecord/0607/Sept25_06/05.shtml> (hereinafter
    referred to as UM News Service, 2006)

[^102]: News Service, 2006 (See Footnote 101)

[^103]: UN, 2006 (See Footnote 72)

[^104]: Milstien, J., and B. Candries, 2001, "Economics of Vaccine
    Development and Implementation: Changes over the Past 20 Years,"
    World Health Organization, Geneva, Switzerland

[^105]: Ibid.

[^106]: Parker, 2007 (See Footnote 86)

[^107]: Ibid.

[^108]: CIDRAP, 2007a (See Footnote 79)

[^109]: Roumeliotis, G., 2006, "Roche hikes US Tamiflu production
    capacity,"
    http://www.in-pharmatechnologist.com/news/ng.asp?id=70624-roche-ampac-tamiflu-bird-flu-shikimic-acid

[^110]: Indifference equivalencies can be expressed in terms of any of
    the metrics; for example, 1 illness is equivalent to 0.002
    fatalities.

[^111]: In this analysis, the quantitative information includes multiple
    decision criteria, including economic and human health impacts, for
    24 different mitigation measure scenarios, with uncertainty
    characterized by 243 different runs for each scenario (a total of
    5,832 different model runs).

[^112]: Equivalent Cost = Cost of the Intervention Strategy + Economic
    Losses + Fatalities \* \$5,000,000 + Non-Fatal Illness \* \$10,000.
    In most of the simulation runs, more than 95 percent of the
    contribution to equivalent cost comes from the high number of
    fatalities.

[^113]: 34,600,000 fatalities = \$173,000,000,000,000/\$5,000,000 per
    fatality

[^114]: Preference measure = (1--p) \* Preparation Costs + p \*
    (Preparation Costs + Pandemic Costs), where p = probability of a
    pandemic in the next 5 years.

[^115]: CDC (Centers for Disease Control and Prevention), 2007b, "The
    Health Alert Network (HAN)," U.S. Department of Health and Human
    Services, Emergency Preparedness & Response,
    http://www.bt.cdc.gov/DocumentsApp/HAN/han.asp

[^116]: Ferguson, N. M., D. A. T. Cummings, C. Fraser, J. C. Cajka, P.
    C. Cooley, and D. S. Burke, 2006, \"Strategies for mitigating an
    influenza pandemic,\" *Nature* **442**:448-452 (hereinafter referred
    to as Ferguson et al., 2006)

[^117]: Longini, I. M., A. Nizam, S. F. Xu, K. Ungchusak, W.
    Hanshaoworakul, D. A. T. Cummings, and M. E. Halloran, 2005,
    \"Containing pandemic influenza at the source,\" *Science*
    **309**:1083--1078

[^118]: Bankes, S., S. Popper, J. Aledort, and S. Bozzette, 2006, "The
    RAND Model on Non-Pharmaceutical Interventions for Pandemic
    Influenza," PowerPoint presentation at the Institute of Medicine
    Workshop on Modeling Community Containment of an Influenza Pandemic,
    Washington, D.C., 25 October (hereinafter referred to as Bankes et
    al., 2006)

[^119]: Blendon, R. J., J. M. Benson, K. J. Weldon, and M. J. Herrmann,
    2006, "Pandemic Influenza and the Public: Survey Findings," Harvard
    School of Public Health Project on the Public and Biological Study,
    Presented at the Institute of Medicine, 26 October (hereinafter
    referred to as Blendon et al., 2006)

[^120]: Bankes et al., 2006 (See Footnote 118)

[^121]: Blendon et al., 2006 (See Footnote 119)

[^122]: Ferguson et al., 2006 (See Footnote 116)

[^123]: Center for Disease Control and Prevention (CDC) recommends
    closing schools for 12 weeks during a Category 4 or 5 pandemic, per
    their "Interim Pre-pandemic Planning Guidance: Community Strategy
    for Pandemic Influenza Mitigation in the U.S.," U.S. Department of
    Health and Human Services, Washington, D.C., 2007
    <http://www.pandemicflu.gov/plan/community/community_mitigation.pdf>
    (hereinafter referred to as CDC, 2007)

[^124]: Ibid.

[^125]: CDC, 2007 (See Footnote 123)

[^126]: NACCHO (National Association of County and City Health
    Officials), 2007, "The National Connection for Local Public Health"
    website, http://www.naccho.org/

[^127]: HHS (U.S. Department of Health and Human Services), 2005, "HHS
    Pandemic Influenza Plan," Washington, D.C., November

[^128]: HSC (Homeland Security Council), 2006, "National Strategy for
    Pandemic Influenza Implementation Plan," The White House,
    Washington, D.C., May (hereinafter referred to as HSC, 2006)

[^129]: Juckett, G., 2006, "Avian Influenza: Preparing for a Pandemic,"
    *American Family Physician* **74**(5):783-790

[^130]: CRS (Congressional Research Service), 2005, "An Overview of the
    U.S. Public Health System in the Context of Emergency Preparedness,"
    Order Code RL31719, The Library of Congress, Washington, D.C., March

[^131]: Public Health Agency of Canada, 2006, *The Canadian Pandemic
    Influenza Plan for the Health Sector,* Annex M: "Public Health
    Measures," No. HP40-10/2006E-PDF), Immunization and Respiratory
    Infections Division, Centre for Infectious Diseases Prevention and
    Control (CIDPC) Ottawa, Ontario,
    <http://www.phac-aspc.gc.ca/cpip-pclcpi/>; and Knobler, S., 2004,
    *Learning from SARS: Preparing for the Next Disease Outbreak,
    Workshop Summary*, Forum on Microbial Threats, Board on Global
    Health, Institute of Medicine of the National Academies, Washington,
    D.C., 27 January

[^132]: CDPHE (Colorado Department of Public Health and Environment),
    2006, "CDPHE Internal Emergency Response Implementation Plan, Annex
    U: Disease Outbreak, Appendix 1: Pandemic Influenza, Attachment 7:
    Community Containment Measures, Including Non-Hospital Isolation and
    Quarantine," Disease Control and Environmental Epidemiology
    Division, Denver, Colorado (hereinafter referred to as CDPHE, 2006)

[^133]: HSC (Homeland Security Council), 2005, "National strategy for
    pandemic influenza," The White House, Washington, D.C.

[^134]: Santa Clara County Public Health Department, 2006, "Pandemic
    Influenza Preparedness and Response Plan, Critical Capacity Module
    IV, Limiting the Spread of Disease," Pandemic Influenza Preparedness
    and Response Planning Team, Santa Clara, California

[^135]: Richards, E. P., K. C. Rathbun, C. S. Brito, and A. Luna, 2006,
    "The role of Law Enforcement in Public Health Emergencies," No. NCJ
    214333), Office of Justice Programs, U.S. Department of Justice,
    Washington, D.C.; and CDPHE, 2006

[^136]: State of Illinois, 2006, "Pandemic influenza preparedness and
    response plan," (Final Draft Version 2.05), Rod R. Blagojevich,
    Governor, Springfield, Illinois; and HHS (U.S. Department of Health
    and Human Services), 2005, "HHS Pandemic Influenza Plan,"
    Washington, D.C., November (hereinafter referred to as State of
    Illinois, 2006)

[^137]: Florida Department of Health, 2006, "Pandemic Influenza Annex,
    Emergency Operations Plan, Appendix 9: Monitoring, Isolation, And
    Quarantine Protocols, No. Version 10.4)," Office of Emergency
    Operations, Division of Emergency Medical Operations, Tallahassee,
    Florida

[^138]: CSIS (Center for Strategic and International Studies), 2005,
    "Model Operational Guidelines for Disease Exposure Control"
    (pre-publication draft), Homeland Security Program, Washington,
    D.C.; and WHO (World Health Organization) Writing Group, 2006,
    "Nonpharmaceutical Interventions for Pandemic Influenza,
    International Measures," *Emerging Infectious Diseases* **12**(1):
    81-87 (hereinafter referred to as CSIS, 2005)

[^139]: CDC, 2007 (See Footnote 123)

[^140]: State of Illinois, 2006 (See Footnote 135)

[^141]: CDC (Centers for Disease Control and Prevention), 2007 (See
    Footnote 123)

[^142]: CDC (Centers for Disease Control and Prevention), 2007 (See
    Footnote 123)

[^143]: HHS, 2005 (See Footnote 126)

[^144]: Ibid.

[^145]: Health Systems Research, Inc., 2007, "Providing Mass Medical
    Care with Scarce Resources: A Community Planning Guide No. 07-0001,"
    Agency for Healthcare Research and Quality (AHRC), Rockville,
    Maryland

[^146]: CSIS, 2005 (See Footnote 137); WHO (World Health Organization)
    Writing Group, 2006, "Nonpharmaceutical Interventions for Pandemic
    Influenza, International Measures," *Emerging Infectious Diseases*
    **12**(1): 81-87; and Svoboda, T., B. Henry, L. Shulman, E. Kennedy,
    E. Rea, W. Ng, T. Wallington, B. Yaffe, E. Gournis, E. Vicencio, S.
    Basrur, and R. Glazier, 2004, "Public Health Measures to Control the
    Spread of the Severe Acute Respiratory Syndrome During the Outbreak
    in Toronto," *New England Journal of Medicine* **350**(23):
    2352-2361, 3 June

[^147]: CDHS (California Department of Health Services), 2006, "Pandemic
    Influenza Preparedness and Response Plan," September 8,
    http://www.dhs.ca.gov/ps/dcdc/izgroup/pdf/pandemic.pdf

[^148]: State of Illinois, 2006 (See Footnote 135)

[^149]: CDPHE, 2006 (See Footnote 131)

[^150]: This would include gatherings at schools, restaurants, theatres,
    churches, and so forth, or sporadic gatherings such as parades. It
    does not include school closings, which are covered separately.

[^151]: Emergency powers and authorities for dealing with any emergency,
    including disease outbreaks, reside with the Governor. Depending on
    state law, a public health emergency may also trigger special powers
    on the part of local government executives (such as the Mayor or
    County Board Chair) or on the part of state or local Health
    Directors. In addition to these mechanisms, most large venues (for
    example, stadiums and coliseums) are subject to permitting or
    licensing practices (building codes), public health law, and other
    legal handles, such as emergency suspension of permits due to a
    health hazard.

[^152]: The establishment of uniform policies or recommendations helps
    avoid confusion when neighboring jurisdictions take different
    actions (which lowers public understanding of what actions they are
    to take and erodes public confidence in the authorities recommending
    them).

[^153]: Fairly applied, uniform action recommendations combined with
    effective public education reduce public resistance.

[^154]: Secondary effects of lost work days due to school closings are
    not included here as they are considered under school closure.

[^155]: Public transportation is used here in the broadest sense to
    include commuter rail, busses, airlines, and all conveyances that
    require dense contact. This does NOT include school buses, because
    school closings are considered separately.

[^156]: A range of strategies to alter public transportation services
    could be implemented to achieve this goal, ranging from shutting
    down services to reducing ridership (such as asking people not to
    ride except for absolute necessity) or maintaining ridership, but
    reducing density on particular conveyances (for example, reducing
    the number of cars on trains while increasing the frequency of
    service).

[^157]: Certain interventions, such as mandatory travel restrictions or
    disease-controlling barriers around entire communities, may in some
    cases not be contemplated for implementation at the state or local
    level because they seem infeasible under any foreseeable
    circumstance (for example, see Florida Department of Health,
    Pandemic Influenza Annex, State of Florida Emergency Operations
    Plan, October 2006).

[^158]: Based on a served population of one million

[^159]: This is the average effective lifetime of a flyer or other
    printed item. After this time it needs to be revised and
    redistributed to keep it current or to maintain interest by the
    public.

[^160]: This is the number of active messages at any given time for both
    radio and TV

[^161]: Broadcast is assumed to be provided as a public service

[^162]: Assuming 1 staff year for national, 0.1 for each state (5
    total), and 4 distributed over local jurisdictions

[^163]: Assuming part-time effort by existing staff

[^164]: This network is assumed to rely on voluntary participation
    without paid professional support. The resulting information should
    be monitored for accuracy; hence, the staff time at a national
    level.

[^165]: Assume this is ongoing for one full year. Estimates are based on
    a served population of 1 million

[^166]: Assume capital equipment is already in place

[^167]: This fraction assumes an average household size of about 2.6 and
    one call per household plus some additional calls for multiple lines

[^168]: Attendance values are based on a population of 1 million

[^169]: Quarantine services are for a fraction of the full population of
    one million for the indicated duration

[^170]: Includes masks, antiseptic agents, over-the-counter medications,
    personal protective equipment; does not include antivirals or
    vaccines