[Paleopsych] PNAS: Analyzing bioterror attack on the food supply: The case of botulinum toxin in milk
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Analyzing bioterror attack on the food supply: The case of botulinum toxin in
milk
Proceedings of the National Academy of Sciences
9984-9989 PNAS July 12, 2005 vol. 102 no. 28 Lawrence M. Wein* and Yifan Liu
[First the summary from CHE. Reading the article, I don't see what the fuss
from the feds was about. The article did not tell anyone how to pull off the
botulinum attack, only about how the poision would spread and what should be
done to prevent such attacks. Sorry about the bad formatting, but that's the
best Adobe Professional can do, and I spent a lot of time cleaning even that
up.]
In Defiance of Federal Agency, Scientific Journal Describes How to Poison
Milk Supply
News bulletin from the Chronicle of Higher Education, 5.6.29
http://chronicle.com/prm/daily/2005/06/2005062901n.htm
[54]By RICHARD MONASTERSKY
Disregarding a request from the federal government, the National
Academy of Sciences published a paper on Tuesday that describes how
terrorists could kill tens of thousands of people by dropping a few
grams of botulinum toxin into a milk truck or storage silo.
The Proceedings of the National Academy of Sciences had planned on
publishing the paper, by two Stanford University researchers, a month
ago, but the academy balked when it received a letter from the U.S.
Department of Health and Human Services. The letter described the
paper as "a road map for terrorists" and said that "publication is not
in the interests of the United States" ([69]The Chronicle, June 17).
But after meeting with government representatives, the academy's
leaders decided that the benefits of publishing the paper outweighed
any threats it posed.
In an editorial accompanying the paper, Bruce Alberts, the academy's
president, said that "we are convinced that the guidance offered in
this article on how to anticipate, model, and minimize a
botulinum-toxin attack can be valuable for biodefense."
The paper, "Analyzing a Bioterror Attack on the Food Supply: The Case
of Botulinum Toxin in Milk," was written by Lawrence M. Wein, a
professor of management science at Stanford's business school, and
Yifan Liu, a graduate student in computational and mathematical
engineering.
Bill Hall, a spokesman for the Health and Human Services Department,
said the agency does not agree with the academy's decision. "Our
concern is that if the academy is wrong on this," he said, "the
consequences could be severe and could be dire, and it will be HHS and
not the academy that will end up having to deal with it."
In their paper, Mr. Wein and Mr. Liu describe how the milk industry is
vulnerable because individual farmers send their product to central
processing facilities, thereby allowing milk from many locations to
mix. Terrorists could poison the supply by putting botulinum toxin
into one of the 5,500-gallon trucks that picks up milk daily at farms
or by dropping the toxin into raw-milk silos, which hold roughly
50,000 gallons each. Pasteurization would destroy some but not all of
the toxin, and a millionth of a gram of toxin may be enough to kill a
person.
The standard tests to detect botulinum toxin take too long to be
useful, Mr. Wein said in an interview, although he recently learned of
a new test that takes only 15 minutes to perform. Such assays could
mitigate, or even thwart, an attack, he said.
To alleviate the risk of such an attack, the Food and Drug
Administration has issued guidelines for the dairy industry, such as
making sure that milk trucks and tanks are locked. But Mr. Wein argues
that those voluntary measures should be required by law. "If a dairy
industry in the hands of a terrorist is as dangerous as a nuclear
facility or a chemical facility," he said, "then voluntary is not
commensurate with the threat."
James S. Cullor, associate dean and director of a teaching and
research center on veterinary medicine at the University of California
at Davis, said Mr. Wein should not have submitted his paper for
publication. "He put millions of children at risk, not only in this
country, but around the world," he said. "And for what? It's an issue
that the industry is already working on. In fact, these are issues
that the industry taught him about. We all knew this was sensitive
information, and we should handle it with care."
But Mr. Wein said he did not provide any information that was not
already available. "Anyone who could seriously pull off this kind of
attack," he said, "is sophisticated enough to track what was going on
in the bioterror world." Using Google, he said, "it would take you all
of 30 seconds to pull up these things."
The paper is available on the journal's[70] Web site.
_________________________________________________________________
Background articles from The Chronicle:
* [71]Federal Officials Ask Journal Not to Publish Bioterrorism
Paper (6/17/2005)
* [72]Final Rules Issued on Microbes (4/1/2005)
* [73]Journal Editors and Scientists Call for More Caution in
Publishing Potentially Dangerous Research (2/17/2003)
* [74]Publish and Perish? As the Nation Fights Terrorists,
Scientists Weigh the Risks of Releasing Sensitive Information
(10/11/2002)
* [75]One More Frightening Possibility: Terrorism in the Croplands
(10/26/2001)
References
54. mailto:rich.monastersky at chronicle.com
70. http://www.pnas.org/cgi/reprint/0408526102v1
71. http://chronicle.com/weekly/v51/i41/41a01102.htm
72. http://chronicle.com/weekly/v51/i30/30a03802.htm
73. http://chronicle.com/daily/2003/02/2003021704n.htm
74. http://chronicle.com/weekly/v49/i07/07a01601.htm
75. http://chronicle.com/weekly/v48/i09/09a02001.htm
E-mail me if you have problems getting the referenced articles.
----------------------------
*Graduate School of Business and Institute for Computational and Mathematical
Engineering, Stanford University, Stanford, CA 94305
Edited by Barry R. Bloom, Harvard University, Boston, MA, and approved April
20, 2005 (received for review November 16, 2004)
To whom correspondence should be addressed. E-mail: lwein at stanford.edu.
Summary
We developed a mathematical model of a cows-to-consumers supply chain
associated with a single milk-processing facility that is the victim of a
deliberate release of botulinum toxin. Because centralized storage and
processing lead to substantial dilution of the toxin, a minimum amount of toxin
is required for the release to do damage. Irreducible uncertainties regarding
the dose–response curve prevent us from quantifying the minimum effective
release. However, if terrorists can obtain enough toxin, and this may well be
possible, then rapid distribution and consumption result in several hundred
thousand poisoned individuals if detection from early symptomatics is not
timely. Timely and specific in-process testing has the potential to eliminate
the threat of this scenario at a cost of <1 cent per gallon and should be
pursued aggressively. Investigation of improving the toxin inactivation rate of
heat pasteurization without sacrificing taste or nutrition is warranted.
Keywords: bioterrorism, mathematical modeling
Among bioterror attacks not involving genetic engineering, the three scenarios
that arguably pose the greatest threats
tohumansareasmallpoxattack,anairborneanthraxattack,and release of botulinum
toxin in cold drinks (1) The methods of dissemination in these three scenarios
are, respectively, the person-to-person spread of contagious disease, the
outdoor dispersalofahighlydurableandlethalagent,andthelarge-scale storage and
production and rapid widespread distribution and consumption of beverages
containing the most poisonous substance known. The first two scenarios have
been the subject of recent systems modeling studies (2–5) and here we present
detailedsystemsanalysisofthethirdscenario.Forconcreteness, we consider release
in the milk supply, which, in addition to its symbolic value as target, is
characterized by the rapid distribution of 20 billion gallons per year in the
U.S. indeed, two natural Salmonella outbreaks in the dairy industry each
infected 200,000people(6).Nonetheless,ourmethodsareapplicableto similar food
products, such as fruit and vegetable juices, canned foods (e.g. processed
tomato products) and perhaps grain- based and other foods possessing the
bow-tie-shaped supply chain pictured in Fig. 1.
The Model
The mathematical model considers the flow of milk through nine-stage
cows-to-consumers supply chain associated with single milk-processing facility
(Fig. 1) Supporting Appendix, which is published as supporting information on
the PNAS web site, contains detailed mathematical formulation of the model,
discussion of the modeling assumptions, and the specification of parameter
values, some of which are listed in Table 1. The supply-chain parameter values
are representative of the California dairy industry, which produces20% of the
nation‘ milk (California dairy facts, www.dairyforum.orcdf.html, accessed
onMay18,2004).Inourmodel,cowsaremilkedtwicedaily,and the milk from each farm is
picked up once per day by 5,500-gallon truck, which makes two round trips daily
between various farms and the processing plant. Upon truck’s arrival at the
processing plant, the milk is piped into one of several raw milk silos, each
capable of holding 50,000 gallons. Raw milk is piped into the processing
facility, goes through sequence of processes (e.g. separation, pasteurization,
homogenization, and vitamin fortification) where each processing line may
simultaneously receive milk from several silos, and is held in
10,000gallonpostpasteurizationtanksbeforebeingbottled. Inourbase case, we
assume that milk from different silos does not mix during downstream processing
and relax this assumption later; although downstream mixing is physically
possible at many facilities, it is not always done. Bottled milk is stored as
finished- goods inventory before traveling through the downstream distribution
channel, eventually being purchased and consumed.
We assume that botulinum toxin is deliberately released in
eitheraholdingtankatadairyfarm,atankertrucktransporting
milkfromafarmtotheprocessingplant,orarawmilksiloatthe processing facility. Each
of these release locations leads to identical consequences, because the toxin
is eventually well mixed throughout the contents of a raw milk silo. The crux
of our analysis is to calculate the amount and toxin concentration of
contaminated milk (see Fig. 4, which is published as supporting information on
the PNAS web site) By California state law, raw milk silo must be cleaned after
72 of operation. During these 72 h, the silo is initially filled up, then
replenished (i.e. simultaneously filled and drained) for most of the 72-
period, and finally drained empty by 72 h. Because the toxin concentration in
the silo drops exponentially during the replenishment interval, each
postpasteurization holding tank has different concentration level. Moreover,
the amount of contaminated milk and the concentration distribution are
themselves random quantities,dependinguponwheninthe72-hsilooperationcycle the
deliberate release occurs. Because of the difficulty of terrorist in scheduling
the release for maximum impact, we assume the release occurs randomly
throughout the filling and replenishment intervals and report the mean number
of poisoned people averaged over the random release time within the
cycle.Usingheat-inactivationdataforfoodswithsimilarpH(7) we estimate that the
heat-pasteurization process [170° (77°C) for 15 min] inactivates 68.4% of the
toxin.
Each gallon of purchased milk is continuously consumed by four people (one
child and three adults) over 3.5-day period. Children aged 2–11 and adults have
differential milk consumption rates and dose–response curves in our model.
probit dose–response model dictates the precise timing of each poisoning.
Ourdose–responserelationshipisbasedonscanthuman data for adults, ID50 0.43 for
children) (8, 9) The attack can be detected via either early symptomatics or
in-process testing results, whichever occurs first. We assume the outbreak is
detected when the 100th person develops symptoms [the incubation period, which
is the interval between the time of poisoning and the onset of symptoms, is log
normal with median of 48 and dispersal factor of 1.5 (10)] and an
Fig. 1. The milk supply chain.
additional 24 are required to identify the attack as being milkborne, at which
time all consumption is halted. As with current antibiotic residue testing, we
assume in-process botulinumtestingisperformedonmilkfromeachtruckjustbeforethe
milk is piped into raw milk silo at the processing facility. We have two tests
at our disposal: the Food and Drug Administration- approved mouse assay with
detection limit of 16 pml (11) and testing delay of 48 h, and an ELISA test
with detection limit of 80 pml (12) and testing delay of h. Because the mouse
assay is not practical for widespread use (assays are processed at only several
U.S. laboratories, and the
mousesupplyislimited),weassesstwostrategies:theELISAtest used in isolation
(i.e. consumption is stopped after positive
ELISAresult)andasequentialstrategyinwhichthemouseassay is used as confirmatory
test after positive ELISA result (i.e. consumption is halted after positive
mouse result) The latter strategy has detection limit of 80 pml and testing
delay of 51 h. The ELISA test in isolation is practical only if the test has an
extremely small false-positive rate (no data have been published on ELISA test
specificity in milk) otherwise, the sequential strategy is the only viable
alternative.
Results
In the absence of any detection (i.e. every gallon of contaminated milk is
consumed) the mean number of people who consume contaminated milk is 568,000
(Fig. 2) Less than1gof
toxinisrequiredtocause100,000meancasualties(i.e.,poisoned individuals) and 10
poison the great majority of the 568,000 consumers (Fig. 2) Most of the
casualties occur on days 3–6, although they happen somewhat faster for larger
releases, because less consumption is required for poisoning. Due to children’s
higher consumption rate and greater toxin sensitivity, the percentage of
casualties who are children in Fig. decreases
from99.97%fora0.1-grelease,to61%fora1-grelease,to28% for 10- release.
Early symptomatic detection avoids of the casualties in Fig. (see Fig. 3) but
still allows100,000 mean casualties for release of 10 g. Relative to no
testing, the sequential testing strategy cuts the number poisoned approximately
in half, resulting in tens of thousands of cases. The ELISA testing strategy
used in isolation prevents nearly all cases, e.g. if kg is released then the
mean number poisoned is 2.82, and six people are
poisonedeveniftheterroristchoosestheworst-casereleasetime within the silo
cleaning cycle.
Table 2 contains the results of sensitivity analysis of isolated
changesin10keyparametersintheno-testingcase.Fiveofthese 10 changes impact the
number of casualties in the no-detection case (Table 3) Graphs corresponding to
Tables and appear in Supporting Appendix. The first of these 10 changes involve
milk storage and processing. Reducing the time between silo cleanings from 72
to 48 lowers the number poisoned by 30% in large attack with no detection but
otherwise has modest impact. Increasing the silo size from 50,000 to 100,000
gallons (several raw milk silos in California hold up to 200,000 gallons) while
varying the number of silos so that the total silo capacity is fixed at 400,000
gallons, and maintaining dedicated processing line for each silo leads to
slightly fewer casualties for small releases but up to twice as many poisoned
for large releases and no detection. Similarly, allowing milk from four silos
to mix during downstream processing can quadruple the number of casualties in
large attack with no detection. Because the toxin inactivation rate may be very
sensitive to the pasteurization temperature and time in the neighborhood of the
current pasteurization formula (7) we consider pasteurization process that
causes 2-log reduction in active toxin. This leads to huge reduction in
casualties if the release size is 10 or less but has no impact for 1-kg
release.
The remaining six changes are from the downstream portion
ofthesupplychain.Wecouldnotfindreliabledataonthespeed of the distribution
channel. More rapid distribution leads to
Table 1. Base-case values for model parameters
Parameter description Value
Production rate 10 gallons per cow per day
Silo size 50,000 gallons
Silos per processing line 1
Time between silo cleanings 72 hr
Speed of distribution channel 80% of milk purchased within 48 hr
Consumers per gallon of milk 4
Time to consume 1 gallon of milk 84 hr
Dose-response probit slope 4.34 Median incubation (adult and child) 48 hr
Dispersal factor of incubation 1.5
Number of symptomatics until detection 100
Time to detect attack is milkborne 24 hr
Testing-detection limit (mouse, ELISA) 60.6 ngallon, 303 ngallon
Testing-time delay (mouse, ELISA) 48 hr, 3 hr
Fraction of toxin not inactivated by pasteurization 0.316
Fraction of milk consumers who are children 0.25
Fraction of milk consumed by children 0.4
earlier consumption and faster diagnosis, and the former effect appears to
dominate, leading to larger attack sizes. Our base- case value for the time to
drink gallon of milk is based on the conservative assumption that everyone has
the same consumption rate. However, there is considerable heterogeneity in
consumption rates across the population, which causes heavier consumers to buy
milk more frequently. Hence, we assume it takes 24 rather than 84 for gallon to
be consumed. As in the case of rapid distribution, higher consumption rate
leads to more casualties. The dose–response data in Tables and are based on
monkey data, which are more plentiful than human data. As in the pasteurization
case, the monkey data lead to drastic reduction in casualties for small release
but have little effect in large release. Because children rarely eat in
restaurants or eat home-canned food, nearly all of the historical
incubationdataarebasedonadults.Weassumethatthemedian
Fig. 2. The mean cumulative number of people poisoned over time for various
release sizes in the absence of any detection.
Fig. 3. The mean total number of people poisoned vs. release size for various
detection scenarios.
incubation time for children is reduced from 48 to 12 because of their smaller
mass and larger consumption of tainted milk, which lead to earlier detection
and many fewer casualties. Our last two changes relate to detection time. The
Centers for Disease Control and Prevention maintains well established national
surveillance system for botulism (14) that has been enhanced in the last
several years. Botulism in virtually all jurisdictions is an immediately
reportable disease, and the characteristic clinical features of botulism
suggest that the outbreakmightberecognizedpromptly(e.g.,bythepresentation of
the 10th case) Moreover, because most metropolitan areas
haveonlyoneortwochildren’shospitals,andbecausemilkisone of the few staples in
children’s diets, the time to detect the outbreak as milkborne might be rather
quick (e.g. 12 h) Not surprisingly, both changes lead to reduction in the
number of people poisoned.
Discussion
Combating bioterrorism requires an appropriate mix of prevention, mitigation,
detection, and response. Our observation that, due to the successive mixing
operations in the upstream portion of the supply chain, the impact of
deliberate release upstream
oftheprocessingplantisindependentofthepreciselocationmay aid in prioritizing
resources for prevention. foodborne attack is much more preventable than an
airborne or mailborne attack, due to the restricted number of release
locations. Requiring all tanks, trucks, and silos to be locked when not being
drained or filledwouldbeanobviousstepforward,aswouldsecuritychecks
forpersonnelwhohaveaccesstoprebottledmilk(farmlaborers, truck drivers,
receiving labor at the processing facility, and plant engineers) and requiring
one person from each stage of the supply chain to be present while milk is
transferred from one stage to the next (15) Although these and other measures
are included in proposed Food and Drug Administration guidelines (16) they are
currently voluntary. Homeland security officials need to engage industry
leaders to establish the most appropriate way to guarantee these guidelines are
enforced. Although enforcement options range from voluntary guidelines to new
laws, the most promising approach may be to develop International Organization
for Standardization (ISO) security standards that are analogous to the ISO 9000
standards for quality management and the ISO 14000 standards for environmental
----------------
Table 2. Sensitivity analysis for 10 parameters in the no-testing case
Release size
Case description 0.1 g 1 g 10 g 100 g 1 kg
Base case 1.7
103 3.2
104 1.2
105 1.6
105 1.7
105
Time between silo cleanings
48 hr 2.0
103 3.5
104 1.2
105 1.5
105 1.5
105
Silo size
100,000 gallons 2.1
102 3.2
104 1.6
105 2.7
105 3.0
105
Silos per processing line
4 4.4
101 2.7
104 1.9
105 4.5
105 5.3
105
Inactivation by pasteurization
0.99 6.6
103iigggChild ID50 0.43 11 5.0 1.3
104 7.3
104 1.5
105
Distribution: 90% purchased
24 hr 2.0
103 4.5
104 1.8
105 2.4
105 2.6
105
Time to consume a gallon
24 hr 1.9
103 6.2
104 1.5
105 1.7
105 1.7
105
ID50 (adult, child)
g 1.8
10g, 30 70 16 6.7
103 4.3
103 4.2
104 1.3
105
Median child incubation
12 hr 6.4
102 7.5
103 3.4
104 5.6
104 6.0
104
Symptomatics until detection
10 1.1
103 2.0
104 8.4
104 1.2
105 1.2
105
Milkborne detection time
12 hr 1.5
103 2.0
104 6.9
104 9.3
104 9.6
104
Each change from the base-case value in Table 1 was made in isolation and shown
are the mean number of poisoned people computed for five different release
sizes.
--------------
management (www.iso.ciseiso9000–1400index.html, accessed on November 12, 2004)
Turning to mitigation, botulinum toxin cannot be completely inactivated by
radiation (17) or any heat treatment that does not adversely affect the milk’s
taste. Ultrahigh-temperature (UHT) pasteurization (performed to provide
extended shelf life)
appearscapableofcompletelyinactivatingbotulinumtoxininmilk, but UHT milk has
not been embraced by U.S. consumers. Nonetheless, it is worthwhile to perform
pasteurization studies to determine whether more potent inactivation process
can be used without compromising nutrition or taste, particularly because the
inactivation rate appears to be quite sensitive to the pasteurization
temperature and time in the neighborhood of the current pasteurization formula
(7) Reducing the time between silo cleanings decreases the number of people
poisoned in, at most, linear manner, but more frequent cleanings would not
onlyincreasevariablematerialandlaborcostsbutwouldpossibly require fixed
investments in additional silos.
Before discussing detection, we note that, on the response side, 60% of
poisoned individuals would require mechanical
ventilation(6).Giventhesmallnumberofventilatorsandlimited
amountofantitoxininthenationalstockpile,thedeathratefrom large attack would
likely be closer to the pre-1950s 60% rate (18) or the 25% rate incurred in the
1950s than to the 6% death rate experienced in the 1990s (19) Moreover, the
current treatment, passive immunization with equine antitoxin, does
notreverseexistentparalysis,andpostexposureprophylaxiswith antitoxin has
adverse side effects (19) Although an economic impact assessment of this
scenario is beyond the scope of our study, the economic cost (including direct
medical costs and lost productivity due to illness and death) from hypothetical
botulism outbreak that poisons 50,000 people was estimated to be 8.6 billion
(20) using direct medical cost (assuming ample ventilators and antitoxin) per
hospitalized patient of $55,000 (based on Canadian dollars in 1993–1994) In
contrast, two recent U.S. victims receiving injections of ‘‘fake Botox’ each
incurred $350,000 medical bill in the first weeks of illness [S. Z. Grossman
(lawyer of Botox victims) personal communication] If this latter amount was
spent on each survivor in an attack that poisoned several hundred thousand
people, then the total medical costs would be tens of billions of dollars. Our
study highlights the value of rapid in-process testing for detecting an attack,
and because stockpiling sufficient ventilators and antitoxin in the event of
large-scale attack would be exorbitantly expensive, it seems wise to
aggressively invest in rapid, sensitive, and specific in-process testing.
variety of different
botulinumtestingtechnologiesarebeinginvestigatedasalternatives to the mouse
assay [summary of the National Institute of Allergy and Infectious Diseases
(NIAID) expert panel on botulinum diagnostics, May 23, 2003,
www2.niaid.nih.goNrdonlyres BB1DDC43-1906-4450-8983-DB0BE374474bottoxinsmtg.
pdf,accessedonNovember15,2004],althoughpublisheddataexist
onlyfortheELISAassay.ThecurrentELISAtestappearstobe orders of magnitude more
sensitive than needed: if milk in truck contains 300 ng per gallon, which is
the detection limit of the assay (12),themilkgetsdilutedbyafactorof
20duringprocessing,and hence each person consumes
ng in their quart of milk, which is logs less than the estimated ID50 for
children, using the human data. Therefore, the current test can afford to lose
some of this sensitivityifitleadstoincreasedspecificityorspeed.Analternative
less-sensitive ELISA assay based on the catalytic activity of the toxin is also
available for botulinum toxin (21) [List Biological
Laboratories(Campbell,CA),www.listlabs.com,accessedonJuly1, 2004] and may be
more specific in foods (unlike milk) where the toxin is unstable.
Current antibiotic residue testing takes 45 min, during which
timethetruckwaitsbeforehavingitscontentsdrainedintoasilo. -----------
Table 3. Sensitivity analysis for five parameters in the no-detection case
Release size
Case description 0.1 g 1 g 10 g 100 g 1 kg
SOCIAL
SCIENCES
Base case 2.3
103 1.5
105 5.0
105 5.7
105 5.7
105
Time between silo cleanings
48 hr 2.8
103 1.6
105 3.8
105 3.9
105 3.9
105
Silo size
100,000 gallons 2.1
102 1.4
105 8.4
105 1.1
106 1.1
106
Silos per processing line
4 4.4
101 1.1
105 1.2
106 2.2
106 2.2
106
Inactivation by pasteurization
0.99 6.6
1006Rv0noh11 5.0 3.8
104 3.6
105 5.7
105
ID50 (adult, child)
g 1.8
10g, 30 70 16 6.7
103 7.5
103 2.1
105 5.3
105
Each change from the base-case value in Table 1 was made in isolation, and the
mean number of poisoned people was computed for
four different release sizes.
------------
test that takes45 min is impractical because it either would increase the
waiting time for each truck (if milk is not released to the silo until the test
results are received) or would need to have near-perfect specificity (if milk
is released before the test results are received) In contrast, three possible
approaches can be used to deal with positive result from sub-45-min test: the
truckcanbehelduntilaconfirmatorymouseassayisperformed, the milk can be
discarded, or the milk can be routed to processing line for
ultra-high-temperature pasteurization, which kills all of the botulinum toxin.
The likelihood that positively
testedmilkcontainstoxinmaybeextremelysmall,e.g.,byBayes’ rule, if there is 10%
probability of an attack occurring in the
U.S.overthenext5years,andthefalse-positiverateis104,then the probability that
positively tested milk contains toxin is only 105. Regardless of which of the
three options is used, it seems clear that sub-45-min test is necessary from
practical perspective. Even if such test is not perfectly specific, it could
still be an immensely useful tool that could essentially eliminate
thethreatofthisscenario.Evenifthetotalcostofatestwas$50,
testingeach5,500-gallontruckwouldincreasethecostofmilkby only cent per gallon.
In addition, because thousands of people would be poisoned per hour in this
scenario, it is imperative to perfect the design and implementation of
near-instantaneous product recall and disposal strategy. To understand the
impact of changing these processing parameters and to assess the danger of
bioterror threats to various food industries, we need some understanding of the
terrorists’ capabilities. To put the release sizes in Figs. and into
perspective, we note that the maximum concentration of
botulinumtoxinincultureis2– 106mouseunitsperml(22),where mouse unit is the
mouse intraperitoneal LD50 in micrograms.
Inthe1980s,theIraqibioweaponsprogramapparentlyincreased this concentration 5-to
10-fold with the use of sulfuric acid (23) If so, it would appear that
terrorists should be capable of concentration of at least 107 mouse units per
ml per gallon. That is, terrorist with this technology could easily deliver 10
of toxin without any special gear. Referring to Fig. 2, in the absence of
detection, this amount would poison 400,000 people. Delivering 100 or more with
this technology would be more cumbersome and would greatly increase the
likelihood of intercepting the attack. Amplification technologies have advanced
significantly in recent years (24) and hence terrorists may be capable of
concentrations considerably higher than per gallon. Section of Supporting
Appendix analyzes three additional interrelated issues: secondary cases due to
crosscontaminated milk, product tracing, and product recall. Two locations in
the supplychain,trucksthatarecleaneddailybutthatmaketwotrips daily and
processing lines that are cleaned daily, offer the opportunity for
uncontaminated milk to become tainted by uncleaned residue from the primary
release. The secondary effect from release in truck has an 50% chance of
causing damage equivalent to release that is later and 0.5% as large as the
primary release. According to Figs. and 3, secondary casualties would be
significant only in cases when the primary release poisons nearly all of its
consumers (in the absence of detection) The secondary impact due to tainted
processing lines is likely to be much smaller, but the resulting milk
concentrations are more difficult to estimate.
Thispotentialforcrosscontamination,coupledwithconsumer anxiety, would probably
cause the supply chain’s entire milk supply to be recalled and discarded at the
time of detection. For the values in Tables and 5, which are published as
supporting information on the PNAS web site, this amounts to 4.83 million
gallons, which includes 2.24-million-gallon containers of partially consumed
milk that need to be recalled from consumers (Eq. 29 in SupportingAppendix) In
addition, 640,000 gallons per
dayoffreshlyproducedmilkwouldneedtobediscardeduntilthe attack is effectively
investigated, the supply chain is turned back on, and consumer confidence
returns. This delay could be
hastenedbyeffectiveproducttracing,decontamination,andrisk
communication.TheU.S.dairyindustrytraceseverymilkcarton back to its processing
facility, which, at least in theory, prevents 300milliongallons)frombeing
discardedandrecalled.Inotherfoodscenarioswherethereisno risk of
crosscontamination (e.g. fresh produce packaged in the field) the ability to
trace product back through the particular path it takes in Fig. could lead to
significant reduction in the amount of product recalled and discarded. As an
illustration, we compute (Eq. 30 in Supporting Appendix and Table 6, which is
published as supporting information on the PNAS web site) the amount of milk
that needs to be discarded as function of the release location (farm, truck, or
silo) and the stage (cow, farm,
truck,silo,orprocessingfacility)towhichthemilkcanbetraced, hypothetically
assuming no crosscontamination.
Our sensitivity analysis suggests there are three types of
variables.Variablesofthefirsttype(timebetweensilocleanings,
silosize,andnumberofsilosperprocessingline)causeavertical shift in the number
poisoned vs. release size graphs (Fig. a–c, which is published as supporting
information on the PNAS web site) and underscore the subtle relationship
between high production efficiency and the consequences of bioterror attack.
Economies of scale can represent double-edged sword: increasing the time
between silo cleanings, silo size, or number of silos per processing line
increases the amount of contaminated milk but reduces the toxin concentration
of this milk, thereby mitigating the impact of small release and exacerbating
the effect of large release. However, for the parameter regimes considered
here, the reduction in casualties in small release is very modest, whereas the
increase in casualties in large release with no testing and poor detection is
in the hundreds of thousands. Variables of the second type (ID50,
pasteurization inactivation) result in horizontal shift in the number poisoned
vs. release size graphs (Fig. and g) More precisely, to cause equivalent
damage, the release size for the monkey ID50s needs to be 70 times larger than
the release size for the human ID50s. Similarly, to generate an equivalent
casualty level, the release
sizeinthe99%inactivationscenarioneedstobe1–0.681–0.99
31.6timeslargerthanthereleasesizeinthe68.4%inactivation scenario. Variables of
the third type (distribution speed, consumption rate, children’s incubation,
number of symptomatics until detection, and milkborne detection time) all
relate to the speed of various events and have no impact on the casualty level
if the attack is not detected. In the no-testing case, the resulting graphs
(Fig. e– and h–j) are very similar to one another and, for the parameter values
considered here, the change in the children’s incubation has the biggest
impact, and the consumption rate has the smallest impact.
Conclusion
In closing, it is important to stress that several elements of the model
contain enough irreducible uncertainty to preclude estimating the impact of an
attack to within several orders of magnitude. First and foremost is the
dose–response curve. The paucity of human data makes an estimate of the ID50
difficult task,andareliableestimateoftheprobitslopeisimpossible.The ID50 values
used here are not close to the worst-case estimate, due to the possibility that
several sublethal (injected or oral) doses collectively containing 1–10% of the
LD50 may be lethal, as in guinea pigs, rabbits, and mice (25) There are also
three aspects of the model that have not been discussed in the open literature,
although presumably studies can and perhaps have
beenperformed:theinactivationrateattainedbypasteurization, the specificity of
an ELISA test in milk, and the release size that terrorist organization is
capable of. Such studies would allow our results to be sharpened considerably.
The dose–response curve, pasteurization inactivation rate, and terrorists’
release- size capabilities each contain several orders of magnitude of
uncertainty, and together they essentially determine the release threshold
required to achieve sufficiently high milk concentration. There is much less
uncertainty about how many people would drink this contaminated milk. There is
irreducible uncertainty due to the timing of the release within the silo
operation cycle,whichcausesthenumberpoisonedtoberoughlyuniformly distributed
between half and twice the mean values (with an additional point mass at the
latter value with probability 0.26) reported in Figs. and 3.
Takentogether,wehaveareasonablyaccurateestimateofthe number of people who could
be poisoned but very poor
estimateofhowmuchtoxinisrequiredtocausealargeoutbreak. The main uncertainties
related to the number of people who could be poisoned are how quickly the
attack would be detected via early symptomatics and how quickly and completely
consumption would be halted: we optimistically assumed that consumption is
halted instantaneously and completely within 24 after the early symptomatics
are detected, even though it took several weeks to identify the source of the
two large but more subtle Salmonella outbreaks in the dairy industry (26, 27)
Even if the reducible uncertainty resolves itself favorably (e.g. heat
pasteurization inactivates 99% of toxin rather than 68.4%)
catastrophiceventisnotimplausible,andthewayforwardseems
clear:investinprevention,investigateinactivationprocessesthat do not affect
nutrition or taste and, most importantly, develop and deploy sub-45-min highly
specific in-process test.
Although the U.S. government appears to be working diligently on the latter two
issues, it is not clear how quickly and thoroughly the dairy supply chain is
being secured. The use of voluntary Food and Drug Administration guidelines is
not commensurate with the severity of this threat, and the government needs to
act much more decisively to safeguard its citizens
fromsuchanattack.Moreover,althoughthedairyindustryisan obvious target, the
government needs to force other food processing industries to quickly assess
the impact of deliberate botulinum release in their supply chains and to do
what is necessary to prevent and mitigate such an event.
---------------------
L.M.W. thanks Stephen Arnon, Larry Barrett, Seth Carus, Richard Danzig, Clay
Detlefson, Leland Ellis, Jerry Gillespie, Steve Jerkins, Eric Johnson, Laura
Kelley, David Montague, Keith Ward, and Dennis Wilson for helpful
conversations. This research was partially supported by the Center for Social
Innovation, Graduate School of Business, Stanford University. 16. U.S. Food and
Drug Administration (2003) Dairy Farms, Bulk Milk Transporters, Bulk Milk
Transfer Stations and Fluid Milk Processors: Food Security
PreventiveMeasuresGuidance (U.S.FoodandDrugAdmin.,Washington,DC)
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4theentirenation’smilksupply(3iig
Adult ID50 1 2ggg3iig(ID50 g
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