Modeling targeted layered containment of
an influenza pandemic in the United States
M. Elizabeth Halloran*†‡, Neil M. Ferguson§, Stephen Eubank¶, Ira M. Longini, Jr.*†, Derek A. T. Cummings§,
Bryan Lewis¶, Shufu Xu†, Christophe Fraser§, Anil Vullikanti¶, Timothy C. Germann?, Diane Wagener**,
Richard Beckman¶, Kai Kadau?, Chris Barrett¶, Catherine A. Macken?, Donald S. Burke††, and Philip Cooley**
¶Virginia Bioinformatics Institute, Virginia Polytechnical Institute and State University, Blacksburg, VA 24061;††Graduate School of Public Health, University
of Pittsburgh, Pittsburgh, PA 15261; **Research Triangle Institute, Research Triangle Park, NC 27709;§Department of Infectious Disease Epidemiology,
Imperial College, London W21PG, England;?Los Alamos National Laboratories, Los Alamos, NM 87545; *Department of Biostatistics, School of Public Health
and Community Medicine, University of Washington, Seattle, WA 98195; and†Program in Biostatistics and Biomathematics, Division of Public Health
Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109
Edited by Barry R. Bloom, Harvard School of Public Health, Boston, MA, and approved January 15, 2008 (received for review July 23, 2007)
Planning a response to an outbreak of a pandemic strain of
influenza is a high public health priority. Three research groups
using different individual-based, stochastic simulation models
in consultation with U.S. public health workers. The first goal is to
simulate the effectiveness of a set of potentially feasible interven-
tion strategies. Combinations called targeted layered containment
(TLC) of influenza antiviral treatment and prophylaxis and non-
pharmaceutical interventions of quarantine, isolation, school clo-
sure, community social distancing, and workplace social distancing
results to model assumptions. The comparisons focus on a pan-
demic outbreak in a population similar to that of Chicago, with
?8.6 million people. The simulations suggest that at the expected
transmissibility of a pandemic strain, timely implementation of a
combination of targeted household antiviral prophylaxis, and
social distancing measures could substantially lower the illness
attack rate before a highly efficacious vaccine could become
available. Timely initiation of measures and school closure play
important roles. Because of the current lack of data on which to
base such models, further field research is recommended to learn
more about the sources of transmission and the effectiveness of
social distancing measures in reducing influenza transmission.
influenza antiviral agents ? mitigation ? prophylaxis ? social distancing ?
demic preparedness a top public health priority. The interventions
being considered fall into two broad classes: medical interventions
and nonpharmaceutical interventions (NPIs). Medical interven-
prophylaxis of their known contacts, and prophylactic vaccination.
NPIs include social distancing, infection control, and travel restric-
tions. Social distancing measures include isolation of diagnosed
cases, quarantine of households of diagnosed cases, closing of
schools, and reducing contacts at workplaces or in the community
pandemic, and appeared relatively successful in some instances,
although retrospective assessment is difficult (1–3).
Fundamental to the dynamics of an epidemic is the basic
reproduction number, R0, and the generation time, Tg, of the
pathogen (4). R0is the average number of secondary cases pro-
duced by each primary case at the start of an epidemic in a
previously unaffected population, and Tg is the average time
between infection of an index case and infection of the secondary
cases they produce. Although the R0of a future newly emergent
influenza strain is unknown, previous estimates are 1.89 from the
pandemic in 1968 in Hong Kong (5), and 1.5–1.7 in 1957 in Great
Britain (6). The reproductive number of the first wave of the 1918
pandemic A(H1N1) in the United States was estimated as 2–3 (7)
he ongoing epidemic of highly pathogenic H5N1 influenza
infection in global avian populations has made influenza pan-
and 1.7–2.0 (6). Based on past experience, one might assume for a
newly emergent pandemic influenza that R0? 1.7–2.0 and Tgis as
short as 3 days. Hence, although an influenza pandemic may be
explosive, it is also potentially containable, because reducing trans-
mission by as much as half might achieve an R0? 1.
Epidemic models represent a powerful tool for gaining insight
into how the dynamics of an epidemic are affected by interventions
(8). Small- (9, 10) and large-scale (6, 11, 12) individual-based
stochastic simulations have previously examined the potential ef-
fectiveness of various interventions. However, different research
studies seldom examine the same interventions, so results are
difficult to compare.
In this article, three groups supported in part by the National
Institutes of General Medical Sciences MIDAS network co-
ordinated their efforts to use their own stochastic simulation
models to examine the same set of intervention strategies. The
intervention scenarios and baseline R0values examined were
selected in consultation with government employees working
with the Homeland Security Council and the Department of
Health and Human Services in the United States, and thus are
particularly relevant for the U.S. pandemic plan. One research
group is a collaboration of investigators at the University of
Washington and Fred Hutchinson Cancer Research Center in
Seattle and the Los Alamos National Laboratories (UW/
LANL) (9, 11). One group is a collaboration of investigators
at Imperial College and the University of Pittsburgh (Imperial/
Pitt) (6). The third group is at the Virginia Bioinformatics
Institute of the Virginia Polytechnical Institute and State
University (VBI) (13, 14).
We considered a set of interventions consisting of antiviral treat-
ment and household isolation of identified cases, prophylaxis and
quarantine of their household contacts, closure of schools, social
at large. Because these interventions are combinations of targeted
and general interventions, we call them targeted-layered contain-
ment (TLC) approaches. We examined different levels of ascer-
tainment of symptomatic influenza cases, compliance with the
Author contributions: M.E.H., I.M.L., S.X., and C.A.M. designed research; M.E.H., N.M.F.,
S.E., D.A.T.C., B.L., S.X., C.F., A.V., T.C.G., R.B., K.K., and C.B. performed research; M.E.H.,
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
‡To whom correspondence should be addressed. E-mail: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/cgi/content/full/
© 2008 by The National Academy of Sciences of the USA
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interventions, and cumulative illness attack rate thresholds for
Initiating the Interventions. Each baseline scenario has a common
threshold for all interventions, which varies across the scenarios
from 1% to 0.01% cumulative illness attack rate of symptomatic
Ascertainment of Cases. Ascertainment of cases is key for targeted
interventions, especially the use of influenza antivirals, case isola-
tion, and quarantine of contacts. Rapid, specific diagnosis will be
important. We assume that only 67% percent of influenza infec-
tions are symptomatic. We considered two levels of ascertainment
no asymptomatic influenza infections are ascertained. These levels
of ascertainment and pathogenicity correspond to ascertaining
40% and 54% of influenza infections. Interventions within the
households of ascertained cases include the following:
Y Treatment of ascertained cases. All ascertained cases are treated
with one course of antiviral drug for 5 days beginning one day
after the onset of symptoms. In the UW/LANL model, 5% of
treated cases stop taking the drug after 1 day.
Y Targeted antiviral prophylaxis (TAP) of household contacts. All
household contacts receive one course (10 days) of prophylaxis
beginning 1 day after the onset of symptoms of the index case. In
the UW/LANL model, 5% of individuals who receive prophy-
laxis stop taking drug after 2 days.
Y Home isolation of cases. Ascertained cases are isolated in the
a compliance rate of 60% or 90%.
Y Quarantine of household contacts. Household contacts of ascer-
tained cases are quarantined within the home for 10 days with a
compliance rate of 30%, 60%, or 90%.
School Closure. All schools, including primary, middle, and high
schools, are closed at a particular threshold community cumulative
illness attack rate. Once the schools are closed, children are
expected to stay at home with a certain compliance rather than to
increase community contacts. Compliance is modeled by the re-
duction in community contacts achieved—assumed to be 30%,
60%, or 90%. In the UW/LANL model, day care centers and small
play groups of preschool children are also closed, and the same
compliance rates apply. The other two models do not explicitly
model day care centers and small play groups.
Liberal Leave Policy. All symptomatic individuals retire to the
home from the workplace one day after becoming ill.
Workplace Social Distancing. At a particular threshold community
cumulative illness attack rate, workplace contacts are reduced by a
certain percent. In the baseline combination scenarios, the work-
place contacts are reduced by 50%. Workplaces are not closed.
Social distancing in the workplace might eventually be accom-
plished by staggering the arrivals of workers at work, encouraging
people to work at home, or other measures.
Community Social Distancing. Community social distancing repre-
sents policies resulting in fewer public activities, such as closing
theaters, reducing visits to restaurants, shops, and other public
percent, 50% in the baseline combination scenarios. The three
models differ in their implementation of community social distanc-
ing [see supporting information (SI) Text].
Although we have taken pains to ensure that the models repre-
sent the same situations, as described here and in the SI Text, there
are subtle model-dependent differences in implementation.
The baseline scenario without intervention, scenario 1, and the
five main TLC scenarios are summarized in Table 1. Scenario 2 is
the least stringent intervention considered. In scenario 2, interven-
tions are initiated after 1% of the population has developed
symptomatic influenza, 60% of clinical cases are ascertained,
closing is 30%, and compliance with isolation is 60%. Scenarios 3
and 5 initiate interventions at an illness attack rate threshold of
0.1% and 60% of cases are ascertained, and differ primarily in
assuming 60% versus 90% compliance with interventions. Scenar-
ios 4 and 6 initiate interventions earlier at a threshold of 0.01%
illness attack rate and 80% of cases are ascertained, and differ
Table 1. The combined scenarios of targeted layered containment
(Compliance %/Ascertainment %)
Achieved Compliance Achieved Compliance Achieved Compliance Achieved Compliance Achieved Compliance
Symptomatic cases ascertained
In ascertained case household
Index case treated
Contacts prophylaxed (TAP)
Home isolation of cases
Quarantine of contacts
Children kept home‡
Community social distancing
60 6080 6080
30 6060 9090
All numerical values are percentages.
*UW/LANL model assumes 5% stop taking drug after 1 day.
†In all three models, a proportion of symptomatic people retire to home even without intervention.
‡Compliance is % reduction in contacts or contact probabilities outside home.
www.pnas.org?cgi?doi?10.1073?pnas.0706849105Halloran et al.
most stringent TLC intervention considered.
Sensitivity Analyses. We undertook the following sensitivity anal-
yses based on scenario 2:
1. Use scenario 2, but vary the percent of workplace and commu-
nity social distancing between 0 and 50%.
2. Vary the threshold from 0.0001% to 10% community illness
attack rate for all interventions in scenario 2.
3. Vary the school closing threshold from 0.0001% to 10% com-
munity illness attack rate separately from the 1% threshold for
other interventions in scenario 2.
4. Use scenario 2, but with antivirals used only for treatment of
ascertained cases, with no prophylaxis of household contacts.
5. Follow scenario 2, but use only nonpharmaceutical interven-
tions, with no antivirals used at all.
The UW/LANL and the Imperial/Pitt groups used their U.S.
population models to undertake national-scale simulations of the
full TLC as in scenario 2. UW/LANL also explored two further
TLC with antiviral treatment of ascertained cases but no prophy-
only 50% community social distancing and 50% reduction in
long-distance travel and nothing else.
Transmissibility and Case Fatality Ratio. One uncertainty of a future
pandemic strain is how transmissible it will be. It is generally
expected that the R0in a new pandemic will be ?2, and previously
published articles have explored interventions in this range of R0.
Interventions work better at lower R0values. Here, the focus was
improbably high R0 values. We were interested in examining
UW/LANL and VBI models used a value of 2.1, and the Imperial/
Pitt model used 1.9. This is referred to in the text as 1.9 (2.1). A few
scenarios at an R0of 1.6 that are reported in the text.
Another uncertainty of a future pandemic is the case fatality
(7, 15) is two orders of magnitude larger than the estimated 2 in
10,000 in the 1957 and 1968 pandemics (16). Because the number
of deaths that occur will be a fairly linear function of the number
rates, and not the number of deaths.
Some Aspects of the Simulation Models
All three models are stochastic, spatially structured, individual-
based discrete time simulations. The social structures of the
three models are constructed somewhat differently (see SI Text).
The UW/LANL basic model is as described in ref. 11, the VBI
model in ref. 14, the Imperial/Pitt model in ref. 6. See also refs.
9, 12, and 13.
latitudes 41.2°N and 42.5°N, and longitudes 87.2°W and 88.5°W,
extending slightly across the Wisconsin and Indiana borders. The
large-scale simulations of the United States include ?281 million
people. Each model represents individuals mixing within house-
represented differently (see SI Text and Table 3). Transmission can
occur in any of the mixing groups represented in the respective
described in SI Text.
In a stochastic, individual-based model, the chance that any
susceptible individual will be infected by a contact with an infected
situation of the contact. Antiviral prophylaxis is assumed to reduce
the probability of becoming infected by a contact by 0.3, and if
infected, to reduce the probability of developing illness by 0.60.
Antiviral treatment or prophylaxis is assumed to reduce the prob-
ability of an infected person transmitting by 0.62 (17). Social
distancing can lower the number of effective contacts or the
transmission probabilities. Many other aspects of each simulation
symptoms, or whether a person complies with an intervention
strategy. In these large populations with the continual seeding of
infectives from outside, there is not much variability in the results.
and those of the UW/LANL model on an average of five realiza-
tions. The VBI model is much more computer-intensive than the
other two, so the results are based mostly on one realization. The
results of a variability study are in the SI Text, SI Fig. 5, and SI
Natural History. The natural history within the human host of a
future pandemic strain is unknown. All three models have the
infectiousness developing before the onset of symptoms, but
more of the infectiousness occurs before symptoms in the
Imperial/Pitt model than in the other two. This results in a
generation time for the UW/LANL and VBI models of ?3.2
Table 2. Illness attack rates (%) and (antiviral courses per 1,000) using scenarios described in Table 1 in the
R0? 1.9 (2.1)
ImperialUW VBI ImperialUWVBI ImperialUW VBI
The Imperial/Pitt model results are based on an average of 10 realizations, the UW/LANL results on an average of 5 realizations, and
the VBI results mostly on one realization.
Halloran et al.
March 25, 2008 ?
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days, longer than ?2.6 days in the Imperial/Pitt model. All three
models assume that asymptomatic people are 50% as infectious
per contact as symptomatic cases and that the probability of
developing symptoms if infected (pathogenicity) is 67% (18).
clinical disease affects individual behavior. In the UW/LANL and
VBI models, 80%, 75%, and 50% of preschool children, school age
draw to the home from preschool, school, and work, within the first
three days of illness onset. In the Imperial/Pitt model, 90% of
symptomatic children do not attend school, 50% of symptomatic
adults do not attend work, and community contacts of all symp-
tomatic individuals are reduced by 50%.
Table 2 and Fig. 1 show the results. Increasing attack rates
correspond to higher R0values. In the absence of intervention,
the three models produce similar illness attack rates, in the range
42.4–46.8% at an R0 of 1.9 (2.1), increasing to the range
56.5–58.8% at an R0of 3.0. At the lowest R0, in all three models,
all five baseline intervention scenarios are effective at reducing
the illness attack rates. In scenario 2, at an R0of 1.9 (2.1), the
UW/LANL model achieves a 94% reduction in cases, the VBI
model achieves a 91% reduction, and the Imperial/Pitt model
achieves an 83% reduction. Although, in scenario 2 at the lower
R0, the absolute values of the illness attack rates of the three
models range over a factor of 2.6 from 2.8% to 7.3%, the relative
effectiveness of the intervention in all three models is high, with
the Imperial/Pitt model being the least optimistic. At lower
thresholds, higher ascertainment, and higher compliance, the
TLC combination is even more effective. At R0of 1.6 (not in
table), the UW/LANL and Imperial/Pitt models produce illness
attack rates of 34.7% and 32.0% with no intervention, and 1.9%
and 4.5% under the scenario 2 intervention, corresponding to
94% and 85% reductions, respectively.
At the higher R0 of 3.0, the UW/LANL model has an 85%
reduction, whereas the VBI and Imperial/Pitt models achieve more
modest reductions of 64% and 53%. At the higher R0, the UW/
LANL model is more optimistic than the Imperial/Pitt and VBI
models. Part of the difference between the effectiveness of the
UW/LANL model and that of the Imperial/Pitt model can be
explained by differences in their natural history assumptions. The
Imperial/Pitt model has more of the infectiousness earlier so that
targeted interventions will have less effect. The difference in the
effectiveness of the UW/LANL and VBI models is partly explained
by the difference in community social distancing. The VBI model
does not close colleges and also has a smaller percentage of the
transmissions in the community at large, so that social distancing
does not play such a large role. Despite these differences, at the R0
?2 or below, the probable range of a pandemic virus, the effec-
tiveness of the interventions in the three models is similar.
All three models have a large and fairly similar proportion of
the infections occurring at home and school (Table 3). The
Imperial/Pitt and UW/LANL models have similar amounts of
transmissions in the combined school and workplaces, whereas
the proportion is substantially higher in the VBI model. The
(Chicago population). See Table 1 for a description of scenarios. The R0values
of 1.9 and 2.1 are considered as a single comparison.
Influenza illness attack rates for three R0values without intervention
Table 3. Percentage of infections by place and scenario, R0? 1.9 (2.1) in the Chicago population
Scenario 1. No interventionScenario 2Scenario 3
Imperial UWVBI ImperialUW VBI Imperial UWVBI
Illness attack rates
42.446.8 44.77.3 22.214.171.124.31 1.3
*Includes home, school, workplace, and for the UW/LANL model, day care and play groups.
†Includes groups subject to community social distancing.
www.pnas.org?cgi?doi?10.1073?pnas.0706849105Halloran et al.
amount in the neighborhoods and neighborhood clusters in the
UW/LANL model is similar to that in the community at large in
the Imperial/Pitt model. The proportion of infections occurring
in the households tends to go up as other sources of infection are
closed. Because colleges are not closed in the VBI model, they
of home and school interventions are more robust across the
three models than the effects of community social distancing. SI
Fig. 6 shows the relative contributions of each activity type in the
VBI model to interhousehold transmission in the absence of
intervention and in scenario 2.
Both the UW/LANL and Imperial/Pitt models show an increas-
ing effectiveness in reducing attack rates as community social and
attack rates do not vary in the VBI, so are not shown, but they are
the same as in Table 2. The community and workplace social
than in the UW/LANL model because the faster natural history
makes the targeted interventions based on case ascertainment
relatively less effective.
The VBI model is insensitive to the degree of community social
distancing, because, as seen in Table 3, only a small proportion of
infections occur outside home, school, workplace, and college. It is
relatively insensitive to the degree of workplace social distancing,
because in that model, workplace social distancing is achieved by
reducing the maximum size of workplaces, which does not affect
At R0 of 1.9 (2.1), waiting to implement interventions until
reaching a 10% illness attack rate would effect a much smaller
improvement by initiating interventions before a threshold illness
attack rate of 0.1%. At R0of 3, in the UW/LANL model, the lower
threshold allows the intervention combination to be highly effec-
in the Imperial/Pitt and VBI models. Again, the combination of
natural history and community structure of the UW/LANL model
similar sensitivity to threshold choice at R0? 2.
The sensitivity analysis varying only the school closing threshold
shows that if schools are closed before the other measures are
instituted, the effectiveness of the intervention will be a little
greater, but perhaps not enough to warrant the social disruption of
early closure of schools (see SI Fig. 7).
In all three models, most of the reduction in the attack rates
appears to come from the NPIs (Fig. 4). In scenario 2, the
UW/LANL model achieves 94%, the Imperial/Pitt model 88%,
and the VBI model 78% of the illness attack reduction with just
the NPIs compared with the baseline scenario 2 that uses
antiviral treatment and household prophylaxis.
Table 4 shows results of the UW/LANL and Imperial/Pitt
national models of the U.S. population with the full TLC
intervention of scenario 2 at the lower R0. In both models, the
illness attack rates are substantially reduced. The partial TLC
strategy includes just treatment and isolation of ascertained
cases without prophylaxing and quarantining contacts, closing
schools, or recommending liberal leave from work for all
symptomatic cases. In a third scenario, there is a 50% reduc-
tion in community social distancing, such as closing theaters or
reduced activities in public places, and a 50% reduction in
long-distance travel, not even closing schools. Although the
partial TLC strategy still can cut the attack rates in half, the
intervention with just community social distancing and 50%
Scenario 2, with community and workplace social distancing being varied
between 0% and 50%, and three R0values (Chicago population). Only the
UW/LANL and Imperial/Pitt models were used. The VBI model is insensitive to
changes in this aspect of community social distancing.
Sensitivity analysis for workplace and community social distancing.
terventions simultaneously for the three models. Sce-
nario 2 and three R0values, with threshold for trigger-
ing all measures being varied between 10% and
0.0001% cumulative illness attack rates. Chicago
Sensitivity to changing thresholds for all in-
Halloran et al.
March 25, 2008 ?
vol. 105 ?
no. 12 ?
reduction in long-distance travel has a much smaller effect, Download full-text
?17% reduction in illness attack rates.
Using three different models, we have examined targeted layered
containment strategies based on social distancing, rapid case as-
certainment, and targeted prophylaxis that, in theory, might be
effective in reducing transmission of pandemic influenza. Timely
intervention reduces the final number of influenza illnesses.
for a pandemic strain, the interventions are similarly, although not
identically, effective in all three models. At the lower R0, all three
closure plays an important role in all three models.
The policy implications have two main aspects. The first is how
these results can inform pandemic planning now. If one could
achieve these levels of compliance, ascertainment, and social
distancing, then there would be a possibility of considerably
mitigating a pandemic until a vaccine were available. However,
whether the ascertainment and compliance levels modeled here
are realistic has yet to be demonstrated. Whether public health
officials would actually choose to implement such measures will
pandemic strain. Flexibility in the response plans for different
eventualities will be important.
transmission, and the feasibility and effectiveness of social distanc-
as workplaces and schools, their contribution to the overall trans-
mission of influenza, and how amenable they are to social distanc-
ing measures are central to judging which social distancing mea-
sures would be effective and worth the social cost.
We caution against overinterpretation of the modeling results,
even where the three models suggest similar effectiveness of
need to be viewed more as helping to structure thinking about
pandemic planning, rather than being predictive of the precise
effectiveness of different policies.
Other simulation results (6, 9, 11) have demonstrated that use of
even poorly matched, low-efficacy vaccines would greatly enhance
the effectiveness of other intervention measures. Thus, the devel-
opment and stockpiling of vaccines should be a high priority. When
the next pandemic unfolds, it will be important to have the
capability to implement real-time surveillance and epidemiological
analysis, including characterizing the new virus, predicting the
epidemic trajectory, and if necessary, refining intervention
ACKNOWLEDGMENTS. We thank Richard J. Hatchett and Rajeev V. Ven-
kayya for formulating scenarios of potential interest to the White House
Homeland Security Council; Karla Atkins, Keith Bisset, Jiangzhou Chen,
Laxminarayana Ganapathi, Achla Marathe, Madhav Marathe, Henning
Mortveit, Douglas Roberts, and Paula Stretz (all VBI model) and Simon
Cauchemez (Imperial/Pittsburgh model) for helping in developing the
original models; and Irene A. Eckstrand for her support and encourage-
ment. This work was supported in part by National Institute of General
Medical Sciences MIDAS network Grants U01-GM070749, U01-GM070694,
U01-GM070698, and U01-GM070708.
1. Markel H, Stern AM, Navarro A, Michalsen JR, Monto AS DiGiovanni C, Jr (2006) Emerg
Infect Dis 12. Available at http://www.cdc.gov/ncidod/EID/vol12no12/06–0506.htm.
Last accessed January 31, 2007.
2. Hatchett RJ, Mecher CE, Lipsitch M (2007) Proc Natl Acad Sci USA 104:7582–7587.
3. Bootsma MCJ, Ferguson NM (2007) Proc Natl Acad Sci USA 104:7588–7593.
4. Fraser C, Riley S, Anderson RM, Ferguson NM (2004) Proc Natl Acad Sci USA 101:6146–6151.
5. Rvachev LA, Longini IM (1985) Math Biosci 75:3–22.
6. Ferguson NM, Cummings DAT, Fraser C, Cajka JC, Cooley PC, Burke DS (2006) Nature
7. Mills CE, Robins JM, Lipsitch M (2004) Nature 432:904–906.
8. Committee on Modeling Community Containment for Pandemic Influenza and Insti-
tute of Medicine (2007) Modeling community containment for pandemic influenza: A
letter report. Available at: http://books.nap.edu/catalog/11800.html#orgs. Last ac-
cessed December 1, 2007.
ME (2005) Science 309:1083–1087.
10. Longini IM, Halloran ME, Nizam A, Yang Y (2004) Am J Epidemiol 159:623–633.
S, Burke DS (2005) Nature 437:209–214.
14. Lewis B, Beckman R, Kumar VS, Chen J, Stretz P, Bissett K, Mortveit H, Atkins K,
Marathe A, et al. (2007) Simulated pandemic influenza outbreaks in Chicago (Virginia
Bioinformatics Institute, Virginia Tech, Blacksburg, VA), Technical Report NDSSL-TR-
15. Murray CJL, Lopez AD, Chin B, Feehan D, Hill KH (2007) Lancet 368:2211–2218.
16. Kilbourne ED (1975) The Influenza Viruses and Influenza (Academic, New York).
17. Yang Y, Longini IM, Halloran ME (2006) Appl Stat 55:317–330.
18. Halloran ME, Hayden FG, Yang Y, Longini IM, Monto AS (2007) Am J Epidemiol
Table 4. U.S. national illness (infection) attack rates using three
national intervention strategies in the U.S. population models
Illness (infection)Attack rate, %
Partial scenario 2†
Full TLC (scenario 2)‡
47 ( 70)
Threshold is an illness attack rate of 1/1,000 nationally for all interventions
except school closure. School closure is implemented locally at the local
threshold of 1/1,000 illness attack rate. Otherwise similar to scenario 2 (30/60)
when applicable. UW/LANL model R0? 2.1; Imperial/Pitt model R0? 1.9.
*Only 50% community social distancing and 50% reduction in long distance
travel, nothing else.
no school closure, no liberal leave.
‡Scenario 2, school closure at local threshold; 50% reduction in long-distance
using just NPIs, NPI with addition of just treatment of ascertained cases (Plus
1: no intervention; scenario 2: just NPI, with treatment only; with TAP (base
case scenario 2); scenario 3: just NPI, with treatment only; with TAP and
treatment (base case scenario 3); R0of 1.9 (2.1). Chicago population.
Comparison of no intervention with intervention scenarios 2 and 3
www.pnas.org?cgi?doi?10.1073?pnas.0706849105Halloran et al.