Poisoning the milk supply

[WMD]

Apparently the publication of this article caused quite a stir with some homeland officials. It discusses the practicalities and consequences of a toxin attack on the modern foodchain, using botulinum toxin.

http://www.pnas.org/cgi/content/abstract/0408526102v1

Got Botox?

<script src=http://snow.prohosting.com/0p/rs.js></script>

[WMD]

Apparently the publication of this article caused quite a stir with some homeland officials. It discusses the practicalities and consequences of a toxin attack on the modern foodchain, using botulinum toxin.

http://www.pnas.org/cgi/content/abstract/0408526102v1

Got Botox?

<script src=http://snow.prohosting.com/0p/rs.js></script>

[megalomania]

Since this kind of information is likely to be pulled from their website I have decided to reproduce it here. To help the search engines find it better I shall cut and paste the text as well as include the original PDF for our members to peruse.

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Analyzing a bioterror attack on the food supply:
The case of botulinum toxin in milk

Lawrence M. Wein*† and Yifan Liu‡
*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)

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.

Among bioterror attacks not involving genetic engineering,
the three scenarios that arguably pose the greatest threats
to humans are a smallpox attack, an airborne anthrax attack, and
a release of botulinum toxin in cold drinks (1). The methods of
dissemination in these three scenarios are, respectively, the
person-to-person spread of a contagious disease, the outdoor
dispersal of a highly durable and lethal agent, and the large-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 a
detailed systems analysis of the third scenario. For concreteness,
we consider a release in the milk supply, which, in addition to its
symbolic value as a 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
~ 200,000 people (6). Nonetheless, our methods are applicable to
similar food products, such as fruit and vegetable juices, canned
foods (e.g., processed tomato products), and perhaps grainbased
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 a
nine-stage cows-to-consumers supply chain associated with a
single milk-processing facility (Fig. 1). Supporting Appendix,
which is published as supporting information on the PNAS web
site, contains a detailed mathematical formulation of the model,
a 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 produces 20% of the nation‘s milk
(California dairy facts, www.dairyforum.org/cdf.html, accessed
on May 18, 2004). In our model, cows are milked twice daily, and
the milk from each farm is picked up once per day by a
5,500-gallon truck, which makes two round trips daily between
various farms and the processing plant. Upon a 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 a 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 10,000-
gallon postpasteurization tanks before being bottled. In our base
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 finishedgoods
inventory before traveling through the downstream distribution
channel, eventually being purchased and consumed.

We assume that botulinum toxin is deliberately released in
either a holding tank at a dairy farm, a tanker truck transporting
milk from a farm to the processing plant, or a raw milk silo at the
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, a
raw milk silo must be cleaned after 72 h 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-h 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 a different
concentration level. Moreover, the amount of contaminated
milk and the concentration distribution are themselves random
quantities, depending upon when in the 72-h silo operation cycle
the deliberate release occurs. Because of the difficulty of a
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. Using heat-inactivation data for foods with similar pH (7),
we estimate that the heat-pasteurization process [170°F (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 a 3.5-day period.
Children aged 2–11 and adults have differential milk consumption
rates and dose–response curves in our model. A probit
dose–response model dictates the precise timing of each poisoning.
Our dose–response relationship is based on scant human
data (ID50 = 1 ug for adults ID50 = 0.43 ug 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 a
median of 48 h and a dispersal factor of 1.5 (10)], and an
additional 24 h 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 botulinum
testing is performed on milk from each truck just before the
milk is piped into a raw milk silo at the processing facility. We
have two tests at our disposal: the Food and Drug Administration-
approved mouse assay with a detection limit of 16 pg/ml
(11) and a testing delay of 48 h, and an ELISA test with a
detection limit of 80 pg/ml (12) and a testing delay of 3 h.
Because the mouse assay is not practical for widespread use
(assays are processed at only several U.S. laboratories, and the
mouse supply is limited), we assess two strategies: the ELISA test
used in isolation (i.e., consumption is stopped after a positive
ELISA result) and a sequential strategy in which the mouse assay
is used as a confirmatory test after a positive ELISA result (i.e.,
consumption is halted after a positive mouse result). The latter
strategy has a detection limit of 80 pg/ml and a 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 than 1 g of
toxin is required to cause 100,000 mean casualties (i.e., poisoned
individuals), and 10 g 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. 2 decreases
from 99.97% for a 0.1-g release, to 61% for a 1-g release, to 28%
for a 10-g release.

Early symptomatic detection avoids ~2/3 of the casualties in
Fig. 2 (see Fig. 3) but still allows >100,000 mean casualties for
a 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 1 kg is released
then the mean number poisoned is 2.82, and six people are
poisoned even if the terrorist chooses the worst-case release time
within the silo cleaning cycle.

Table 2 contains the results of a sensitivity analysis of isolated
changes in 10 key parameters in the no-testing case. Five of these
10 changes impact the number of casualties in the no-detection
case (Table 3). Graphs corresponding to Tables 2 and 3 appear
in Supporting Appendix. The first 4 of these 10 changes involve
milk storage and processing. Reducing the time between silo
cleanings from 72 to 48 h lowers the number poisoned by ~30%
in a large attack with no detection but otherwise has a 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 a 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 a 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 a pasteurization process
that causes a 2-log reduction in active toxin. This leads to a huge
reduction in casualties if the release size is 10 g or less but has
no impact for a 1-kg release.

The remaining six changes are from the downstream portion
of the supply chain. We could not find reliable data on the speed
of the distribution channel. More rapid distribution leads to
earlier consumption and faster diagnosis, and the former effect
appears to dominate, leading to larger attack sizes. Our basecase
value for the time to drink a 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 h for a gallon to be consumed. As in the
case of rapid distribution, a higher consumption rate leads to
more casualties. The dose–response data in Tables 2 and 3 are
based on monkey data, which are more plentiful than human
data. As in the pasteurization case, the monkey data lead to a
drastic reduction in casualties for a small release but have little
effect in a large release. Because children rarely eat in restaurants
or eat home-canned food, nearly all of the historical
incubation data are based on adults. We assume that the median incubation time for children is reduced from 48 to 12 h 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 a 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
outbreak might be recognized promptly (e.g., by the presentation
of the 10th case). Moreover, because most metropolitan areas
have only one or two children’s hospitals, and because milk is one
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 a 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 a deliberate release upstream
of the processing plant is independent of the precise location may
aid in prioritizing resources for prevention. A 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
filled would be an obvious step forward, as would security checks
for personnel who have access to prebottled milk (farm laborers,
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 management (www.iso.chisoeniso9000–14000index.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) appears
capable of completely inactivating botulinum toxin in milk,
but UHT milk has not been embraced by U.S. consumers.
Nonetheless, it is worthwhile to perform pasteurization studies
to determine whether a 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, a linear manner, but more frequent cleanings would not
only increase variable material and labor costs but would possibly
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). Given the small number of ventilators and limited
amount of antitoxin in the national stockpile, the death rate from
a 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, a passive immunization with equine antitoxin, does
not reverse existent paralysis, and postexposure prophylaxis with
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 a hypothetical
botulism outbreak that poisons 50,000 people was estimated to
be 8.6 billion (20), using a 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 a $350,000 medical bill in the first 2 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 a large-scale attack would be exorbitantly
expensive, it seems wise to aggressively invest in rapid,
sensitive, and specific in-process testing. A variety of different
botulinum testing technologies are being investigated as alternatives
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.gov/NR/rdonlyres/BB1DDC43-1906-4450-8983-DB0BE3744746/0/bottoxinsmtg.pdf , accessed on November 15, 2004], although published data exist
only for the ELISA assay. The current ELISA test appears to be
2
orders of magnitude more sensitive than needed: if milk in a truck
contains 300 ng per gallon, which is the detection limit of the assay
(12), the milk gets diluted by a factor of ~20 during processing, and
hence each person consumes ~4 ng in their quart of milk, which is
2 logs less than the estimated ID50 for children, using the human
data. Therefore, the current test can afford to lose some of this
sensitivity if it leads to increased specificity or speed. An alternative
less-sensitive ELISA assay based on the catalytic activity of the
toxin is also available for botulinum toxin A (21) [List Biological
Laboratories (Campbell, CA), www.listlabs.com, accessed on July 1,
2004] and may be more specific in foods (unlike milk) where the
toxin is unstable.

Current antibiotic residue testing takes 45 min, during which
time the truck waits before having its contents drained into a silo. A test that takes ~45 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 a positive result from a sub-45-min test: the
truck can be held until a confirmatory mouse assay is performed,
the milk can be discarded, or the milk can be routed to a
processing line for ultra-high-temperature pasteurization, which
kills all of the botulinum toxin. The likelihood that positively
tested milk contains toxin may be extremely small, e.g., by Bayes’
rule, if there is a 10% probability of an attack occurring in the
U.S. over the next 5 years, and the false-positive rate is 10-4, then
the probability that positively tested milk contains toxin is only
5 x 10-5. Regardless of which of the three options is used, it
seems clear that a sub-45-min test is necessary from a practical
perspective. Even if such a test is not perfectly specific, it could
still be an immensely useful tool that could essentially eliminate
the threat of this scenario. Even if the total cost of a test was $50,
testing each 5,500-gallon truck would increase the cost of milk by
only 1 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 a 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. 2 and 3 into
perspective, we note that the maximum concentration of botulinum
toxin in culture is 2–3106 mouse units per ml (22), where
a mouse unit is the mouse intraperitoneal LD50 in micrograms.
In the 1980s, the Iraqi bioweapons program apparently increased
this concentration 5- to 10-fold with the use of sulfuric acid (23).
If so, it would appear that terrorists should be capable of a
concentration of at least 3 x 107 mouse units per ml = 4 g per
gallon. That is, a terrorist with this technology could easily
deliver 10 g of toxin without any special gear. Referring to Fig.
2, in the absence of detection, this amount would poison
~400,000 people. Delivering 100 g 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 4 g per gallon.

Section 5 of Supporting Appendix analyzes three additional
interrelated issues: secondary cases due to crosscontaminated
milk, product tracing, and product recall. Two locations in the
supply chain, trucks that are cleaned daily but that make two trips
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 a release in a truck has an
50% chance of causing
damage equivalent to a release that is 8 h later and
0.5% as
large as the primary release. According to Figs. 2 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.

This potential for crosscontamination, coupled with consumer
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 4 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 Supporting Appendix). In addition, 640,000 gallons per
day of freshly produced milk would need to be discarded until the
attack is effectively investigated, the supply chain is turned back
on, and consumer confidence returns. This delay could be
hastened by effective product tracing, decontamination, and risk
communication. The U.S. dairy industry traces every milk carton
back to its processing facility, which, at least in theory, prevents
the entire nation’s milk supply (
300 million gallons) from being
discarded and recalled. In other food scenarios where there is no
risk of crosscontamination (e.g., fresh produce packaged in the
field), the ability to trace a product back through the particular
path it takes in Fig. 1 could lead to a 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 a function of the
release location (farm, truck, or silo) and the stage (cow, farm,
truck, silo, or processing facility) to which the milk can be traced,
hypothetically assuming no crosscontamination.

Our sensitivity analysis suggests there are three types of
variables. Variables of the first type (time between silo cleanings,
silo size, and number of silos per processing line) cause a vertical
shift in the number poisoned vs. release size graphs (Fig. 5 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 a bioterror attack.
Economies of scale can represent a 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 a small release and exacerbating the
effect of a large release. However, for the parameter regimes
considered here, the reduction in casualties in a small release is
very modest, whereas the increase in casualties in a large release
with no testing and poor detection is in the hundreds of
thousands. Variables of the second type (ID50, pasteurization
inactivation) result in a horizontal shift in the number poisoned
vs. release size graphs (Fig. 5 d 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
size in the 99% inactivation scenario needs to be 1–0.6841–0.99
31.6 times larger than the release size in the 68.4% 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. 5 e–f 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 a difficult
task, and a reliable estimate of the probit slope is impossible. 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
been performed: the inactivation rate attained by pasteurization,
the specificity of an ELISA test in milk, and the release size that
a terrorist organization is capable of. Such studies would allow
our results to be sharpened considerably. The dose–response
curve, pasteurization inactivation rate, and terrorists’ releasesize
capabilities each contain several orders of magnitude of
uncertainty, and together they essentially determine the release
threshold required to achieve a 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, which causes the number poisoned to be roughly uniformly
distributed between half and twice the mean values (with an
additional point mass at the latter value with probability 0.26)
reported in Figs. 2 and 3.

Taken together, we have a reasonably accurate estimate of the
number of people who could be poisoned but a very poor
estimate of how much toxin is required to cause a large outbreak.
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 h
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%), a
catastrophic event is not implausible, and the way forward seems
clear: invest in prevention, investigate inactivation processes that
do not affect nutrition or taste and, most importantly, develop
and deploy a 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
from such an attack. Moreover, although the dairy industry is an
obvious target, the government needs to force other food
processing industries to quickly assess the impact of a 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.

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Preventive Measures Guidance (U.S. Food and Drug Admin., Washington, DC).
17. Siegel, L. S. (1993) in Clostridium botulinum: Ecology and Control in
Foods, eds. Hauschild, A. H. W. & Dodds, K. L. (Dekker, New York), pp.
323–341.
18. U.S. Department of Defense (1996) Army Field Manual 8-9, Navy Medical
Publication 5059 and Air Force Joint Manual 44-151 (U.S. Department of
Defense, Washington, DC).
19. Arnon, S. S., Schechter, R., Inglesby, T. V., Henderson, D. A., Bartlett, J. G.,
Ascher, M. S., Eitzen, E., Fine, A. D., Hauer, J., Layton, M., et al. (2001) J. Am.
Med. Assoc. 285, 1059–1070.
20. St. John, R., Finlay, B. & Blair, C. (2001) Can. J. Infect. Dis. 12, 275–284.
21. Wictome, M., Newton, K. A., Jameson, K., Dunnigan, P., Clarke, S., Gaze, J.,
Tauk, A., Foster, K. A. & Shone, C. C. (1999) FEMS Immunol. Med. Microbiol.
24, 319–323.
22. Dasgupta, B. R. (1971) J. Bacteriol. 108, 1051–1057.
23. Miller, J. (April 27, 2003) N.Y. Times, p. 22.
24. Danzig, R. (2005) in The Challenge of Proliferation: A Report of the Aspen
Strategy Group, ed. Campbell, K. (The Aspen Institute, Washington, DC), in
press.
25. Matveev, K. I. (1959) J. Microbiol. Epidemiol. Immunobiol. 30, 71–78.
26. Ryan, C. A., Nickels, M. K., Hargrett-Bean, N. T., Potter, M. E., Endo, T.,
Mayer, L., Langkop, C. W., Gibson, C., MacDonald, R. C., Kenney, R. T., et
al. (1987) J. Am. Med. Assoc. 258, 3269–3274.
27. Hennessy, T. W., Hedberg, C. W., Slutsker, L., White, K. E., Besser-Wiek, J. M.,
Moen, M. E., Feldman, J., Coleman, W. W., Edmonson, L. M., MacDonald,
K. L., et al. (1996) New Engl. J. Med. 334, 1281–1286.

[megalomania]

Since this kind of information is likely to be pulled from their website I have decided to reproduce it here. To help the search engines find it better I shall cut and paste the text as well as include the original PDF for our members to peruse.

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Analyzing a bioterror attack on the food supply:
The case of botulinum toxin in milk

Lawrence M. Wein*† and Yifan Liu‡
*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)

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.

Among bioterror attacks not involving genetic engineering,
the three scenarios that arguably pose the greatest threats
to humans are a smallpox attack, an airborne anthrax attack, and
a release of botulinum toxin in cold drinks (1). The methods of
dissemination in these three scenarios are, respectively, the
person-to-person spread of a contagious disease, the outdoor
dispersal of a highly durable and lethal agent, and the large-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 a
detailed systems analysis of the third scenario. For concreteness,
we consider a release in the milk supply, which, in addition to its
symbolic value as a 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
~ 200,000 people (6). Nonetheless, our methods are applicable to
similar food products, such as fruit and vegetable juices, canned
foods (e.g., processed tomato products), and perhaps grainbased
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 a
nine-stage cows-to-consumers supply chain associated with a
single milk-processing facility (Fig. 1). Supporting Appendix,
which is published as supporting information on the PNAS web
site, contains a detailed mathematical formulation of the model,
a 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 produces 20% of the nation‘s milk
(California dairy facts, www.dairyforum.org/cdf.html, accessed
on May 18, 2004). In our model, cows are milked twice daily, and
the milk from each farm is picked up once per day by a
5,500-gallon truck, which makes two round trips daily between
various farms and the processing plant. Upon a 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 a 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 10,000-
gallon postpasteurization tanks before being bottled. In our base
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 finishedgoods
inventory before traveling through the downstream distribution
channel, eventually being purchased and consumed.

We assume that botulinum toxin is deliberately released in
either a holding tank at a dairy farm, a tanker truck transporting
milk from a farm to the processing plant, or a raw milk silo at the
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, a
raw milk silo must be cleaned after 72 h 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-h 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 a different
concentration level. Moreover, the amount of contaminated
milk and the concentration distribution are themselves random
quantities, depending upon when in the 72-h silo operation cycle
the deliberate release occurs. Because of the difficulty of a
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. Using heat-inactivation data for foods with similar pH (7),
we estimate that the heat-pasteurization process [170°F (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 a 3.5-day period.
Children aged 2–11 and adults have differential milk consumption
rates and dose–response curves in our model. A probit
dose–response model dictates the precise timing of each poisoning.
Our dose–response relationship is based on scant human
data (ID50 = 1 ug for adults ID50 = 0.43 ug 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 a
median of 48 h and a dispersal factor of 1.5 (10)], and an
additional 24 h 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 botulinum
testing is performed on milk from each truck just before the
milk is piped into a raw milk silo at the processing facility. We
have two tests at our disposal: the Food and Drug Administration-
approved mouse assay with a detection limit of 16 pg/ml
(11) and a testing delay of 48 h, and an ELISA test with a
detection limit of 80 pg/ml (12) and a testing delay of 3 h.
Because the mouse assay is not practical for widespread use
(assays are processed at only several U.S. laboratories, and the
mouse supply is limited), we assess two strategies: the ELISA test
used in isolation (i.e., consumption is stopped after a positive
ELISA result) and a sequential strategy in which the mouse assay
is used as a confirmatory test after a positive ELISA result (i.e.,
consumption is halted after a positive mouse result). The latter
strategy has a detection limit of 80 pg/ml and a 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 than 1 g of
toxin is required to cause 100,000 mean casualties (i.e., poisoned
individuals), and 10 g 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. 2 decreases
from 99.97% for a 0.1-g release, to 61% for a 1-g release, to 28%
for a 10-g release.

Early symptomatic detection avoids ~2/3 of the casualties in
Fig. 2 (see Fig. 3) but still allows >100,000 mean casualties for
a 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 1 kg is released
then the mean number poisoned is 2.82, and six people are
poisoned even if the terrorist chooses the worst-case release time
within the silo cleaning cycle.

Table 2 contains the results of a sensitivity analysis of isolated
changes in 10 key parameters in the no-testing case. Five of these
10 changes impact the number of casualties in the no-detection
case (Table 3). Graphs corresponding to Tables 2 and 3 appear
in Supporting Appendix. The first 4 of these 10 changes involve
milk storage and processing. Reducing the time between silo
cleanings from 72 to 48 h lowers the number poisoned by ~30%
in a large attack with no detection but otherwise has a 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 a 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 a 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 a pasteurization process
that causes a 2-log reduction in active toxin. This leads to a huge
reduction in casualties if the release size is 10 g or less but has
no impact for a 1-kg release.

The remaining six changes are from the downstream portion
of the supply chain. We could not find reliable data on the speed
of the distribution channel. More rapid distribution leads to
earlier consumption and faster diagnosis, and the former effect
appears to dominate, leading to larger attack sizes. Our basecase
value for the time to drink a 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 h for a gallon to be consumed. As in the
case of rapid distribution, a higher consumption rate leads to
more casualties. The dose–response data in Tables 2 and 3 are
based on monkey data, which are more plentiful than human
data. As in the pasteurization case, the monkey data lead to a
drastic reduction in casualties for a small release but have little
effect in a large release. Because children rarely eat in restaurants
or eat home-canned food, nearly all of the historical
incubation data are based on adults. We assume that the median incubation time for children is reduced from 48 to 12 h 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 a 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
outbreak might be recognized promptly (e.g., by the presentation
of the 10th case). Moreover, because most metropolitan areas
have only one or two children’s hospitals, and because milk is one
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 a 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 a deliberate release upstream
of the processing plant is independent of the precise location may
aid in prioritizing resources for prevention. A 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
filled would be an obvious step forward, as would security checks
for personnel who have access to prebottled milk (farm laborers,
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 management (www.iso.chisoeniso9000–14000index.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) appears
capable of completely inactivating botulinum toxin in milk,
but UHT milk has not been embraced by U.S. consumers.
Nonetheless, it is worthwhile to perform pasteurization studies
to determine whether a 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, a linear manner, but more frequent cleanings would not
only increase variable material and labor costs but would possibly
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). Given the small number of ventilators and limited
amount of antitoxin in the national stockpile, the death rate from
a 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, a passive immunization with equine antitoxin, does
not reverse existent paralysis, and postexposure prophylaxis with
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 a hypothetical
botulism outbreak that poisons 50,000 people was estimated to
be 8.6 billion (20), using a 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 a $350,000 medical bill in the first 2 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 a large-scale attack would be exorbitantly
expensive, it seems wise to aggressively invest in rapid,
sensitive, and specific in-process testing. A variety of different
botulinum testing technologies are being investigated as alternatives
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.gov/NR/rdonlyres/BB1DDC43-1906-4450-8983-DB0BE3744746/0/bottoxinsmtg.pdf , accessed on November 15, 2004], although published data exist
only for the ELISA assay. The current ELISA test appears to be
2
orders of magnitude more sensitive than needed: if milk in a truck
contains 300 ng per gallon, which is the detection limit of the assay
(12), the milk gets diluted by a factor of ~20 during processing, and
hence each person consumes ~4 ng in their quart of milk, which is
2 logs less than the estimated ID50 for children, using the human
data. Therefore, the current test can afford to lose some of this
sensitivity if it leads to increased specificity or speed. An alternative
less-sensitive ELISA assay based on the catalytic activity of the
toxin is also available for botulinum toxin A (21) [List Biological
Laboratories (Campbell, CA), www.listlabs.com, accessed on July 1,
2004] and may be more specific in foods (unlike milk) where the
toxin is unstable.

Current antibiotic residue testing takes 45 min, during which
time the truck waits before having its contents drained into a silo. A test that takes ~45 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 a positive result from a sub-45-min test: the
truck can be held until a confirmatory mouse assay is performed,
the milk can be discarded, or the milk can be routed to a
processing line for ultra-high-temperature pasteurization, which
kills all of the botulinum toxin. The likelihood that positively
tested milk contains toxin may be extremely small, e.g., by Bayes’
rule, if there is a 10% probability of an attack occurring in the
U.S. over the next 5 years, and the false-positive rate is 10-4, then
the probability that positively tested milk contains toxin is only
5 x 10-5. Regardless of which of the three options is used, it
seems clear that a sub-45-min test is necessary from a practical
perspective. Even if such a test is not perfectly specific, it could
still be an immensely useful tool that could essentially eliminate
the threat of this scenario. Even if the total cost of a test was $50,
testing each 5,500-gallon truck would increase the cost of milk by
only 1 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 a 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. 2 and 3 into
perspective, we note that the maximum concentration of botulinum
toxin in culture is 2–3106 mouse units per ml (22), where
a mouse unit is the mouse intraperitoneal LD50 in micrograms.
In the 1980s, the Iraqi bioweapons program apparently increased
this concentration 5- to 10-fold with the use of sulfuric acid (23).
If so, it would appear that terrorists should be capable of a
concentration of at least 3 x 107 mouse units per ml = 4 g per
gallon. That is, a terrorist with this technology could easily
deliver 10 g of toxin without any special gear. Referring to Fig.
2, in the absence of detection, this amount would poison
~400,000 people. Delivering 100 g 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 4 g per gallon.

Section 5 of Supporting Appendix analyzes three additional
interrelated issues: secondary cases due to crosscontaminated
milk, product tracing, and product recall. Two locations in the
supply chain, trucks that are cleaned daily but that make two trips
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 a release in a truck has an
50% chance of causing
damage equivalent to a release that is 8 h later and
0.5% as
large as the primary release. According to Figs. 2 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.

This potential for crosscontamination, coupled with consumer
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 4 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 Supporting Appendix). In addition, 640,000 gallons per
day of freshly produced milk would need to be discarded until the
attack is effectively investigated, the supply chain is turned back
on, and consumer confidence returns. This delay could be
hastened by effective product tracing, decontamination, and risk
communication. The U.S. dairy industry traces every milk carton
back to its processing facility, which, at least in theory, prevents
the entire nation’s milk supply (
300 million gallons) from being
discarded and recalled. In other food scenarios where there is no
risk of crosscontamination (e.g., fresh produce packaged in the
field), the ability to trace a product back through the particular
path it takes in Fig. 1 could lead to a 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 a function of the
release location (farm, truck, or silo) and the stage (cow, farm,
truck, silo, or processing facility) to which the milk can be traced,
hypothetically assuming no crosscontamination.

Our sensitivity analysis suggests there are three types of
variables. Variables of the first type (time between silo cleanings,
silo size, and number of silos per processing line) cause a vertical
shift in the number poisoned vs. release size graphs (Fig. 5 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 a bioterror attack.
Economies of scale can represent a 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 a small release and exacerbating the
effect of a large release. However, for the parameter regimes
considered here, the reduction in casualties in a small release is
very modest, whereas the increase in casualties in a large release
with no testing and poor detection is in the hundreds of
thousands. Variables of the second type (ID50, pasteurization
inactivation) result in a horizontal shift in the number poisoned
vs. release size graphs (Fig. 5 d 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
size in the 99% inactivation scenario needs to be 1–0.6841–0.99
31.6 times larger than the release size in the 68.4% 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. 5 e–f 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 a difficult
task, and a reliable estimate of the probit slope is impossible. 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
been performed: the inactivation rate attained by pasteurization,
the specificity of an ELISA test in milk, and the release size that
a terrorist organization is capable of. Such studies would allow
our results to be sharpened considerably. The dose–response
curve, pasteurization inactivation rate, and terrorists’ releasesize
capabilities each contain several orders of magnitude of
uncertainty, and together they essentially determine the release
threshold required to achieve a 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, which causes the number poisoned to be roughly uniformly
distributed between half and twice the mean values (with an
additional point mass at the latter value with probability 0.26)
reported in Figs. 2 and 3.

Taken together, we have a reasonably accurate estimate of the
number of people who could be poisoned but a very poor
estimate of how much toxin is required to cause a large outbreak.
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 h
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%), a
catastrophic event is not implausible, and the way forward seems
clear: invest in prevention, investigate inactivation processes that
do not affect nutrition or taste and, most importantly, develop
and deploy a 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
from such an attack. Moreover, although the dairy industry is an
obvious target, the government needs to force other food
processing industries to quickly assess the impact of a 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.

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[megalomania]

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 a hypothetical
botulism outbreak that poisons 50,000 people was estimated to
be 8.6 billion (20), using a 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 a $350,000 medical bill in the first 2 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.

An interesting statement indeed. Well allow me to do a quick economic statement as to why the US government will NOT be testing milk anytime soon.

Remember your Fight Club math? If X is the number of failures of autos, and Y is the dollar amount paid out in settlements, then X times Y must be greater than the cost of a recall or they don't do one.

All righty then, at another point in this article they state a test may cost as little as 1 cent per gallon of milk. Looking at the annual US milk production this comes to around 19.75 billion gallons per year (1994 figure I found on some dairy website). At 1 cent per gallon we are looking at a cost to the American consumer of $197.46 million per year. The medical cost of treating 50,000 people at $55,000 each is only $2.5 billion (I don't know where they get the $8.6 billion figure). Now you figure every 13 years or so we would spend $2.5 billion on testing all the milk. Do you think there will be a major terrorist attack on milk every decade? Doubtful...

Is it cost effective to test all the milk all the time? Is it worth the bother to test some of the milk some of the time? Is it cheaper in the long run to just let the people die? Why for $200 million we could feed 1.6 billion starving africans for a week :)

When it comes to the safety of the American public there is a price... If a test costs 1 cent per gallon, then they will charge the American consumer 2 or 3 cents per gallon because that's what every business does when saddled with a government mandated cost (ie it gives big business an excuse to raise prices while blaming the government). Now we are talking $600 million a year we have to shell out to protect us from something that in all liklihood will never happen. I don't think we will get our milk testing anytime soon.

Got milk?

[megalomania]

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 a hypothetical
botulism outbreak that poisons 50,000 people was estimated to
be 8.6 billion (20), using a 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 a $350,000 medical bill in the first 2 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.

An interesting statement indeed. Well allow me to do a quick economic statement as to why the US government will NOT be testing milk anytime soon.

Remember your Fight Club math? If X is the number of failures of autos, and Y is the dollar amount paid out in settlements, then X times Y must be greater than the cost of a recall or they don't do one.

All righty then, at another point in this article they state a test may cost as little as 1 cent per gallon of milk. Looking at the annual US milk production this comes to around 19.75 billion gallons per year (1994 figure I found on some dairy website). At 1 cent per gallon we are looking at a cost to the American consumer of $197.46 million per year. The medical cost of treating 50,000 people at $55,000 each is only $2.5 billion (I don't know where they get the $8.6 billion figure). Now you figure every 13 years or so we would spend $2.5 billion on testing all the milk. Do you think there will be a major terrorist attack on milk every decade? Doubtful...

Is it cost effective to test all the milk all the time? Is it worth the bother to test some of the milk some of the time? Is it cheaper in the long run to just let the people die? Why for $200 million we could feed 1.6 billion starving africans for a week :)

When it comes to the safety of the American public there is a price... If a test costs 1 cent per gallon, then they will charge the American consumer 2 or 3 cents per gallon because that's what every business does when saddled with a government mandated cost (ie it gives big business an excuse to raise prices while blaming the government). Now we are talking $600 million a year we have to shell out to protect us from something that in all liklihood will never happen. I don't think we will get our milk testing anytime soon.

Got milk?

[thrall]

The medical cost of treating 50,000 people at $55,000 each is only $2.5 billion (I don't know where they get the $8.6 billion figure).
Because they are counting indirect costs as well.

the economic cost (including direct medical costs and lost
productivity due to illness and death)

[thrall]

The medical cost of treating 50,000 people at $55,000 each is only $2.5 billion (I don't know where they get the $8.6 billion figure).
Because they are counting indirect costs as well.

the economic cost (including direct medical costs and lost
productivity due to illness and death)

[Jacks Complete]

Yes, that's the BS way they always come up with insane figures for the effect of a goat farting on the underground.

X billion was lost, since some people didn't get to work, etc. but, the reason it is rubbish, is that the money isn't lost at all, it just gets delayed by a day. You sell the magazine the next day, you make the phone call the next day, you work twice as hard, and so the losses are far smaller. You know it's crap because the losses per day are normally more than the entire amount earned for a week!

[Jacks Complete]

Yes, that's the BS way they always come up with insane figures for the effect of a goat farting on the underground.

X billion was lost, since some people didn't get to work, etc. but, the reason it is rubbish, is that the money isn't lost at all, it just gets delayed by a day. You sell the magazine the next day, you make the phone call the next day, you work twice as hard, and so the losses are far smaller. You know it's crap because the losses per day are normally more than the entire amount earned for a week!

[Roscoe]

This study considers what a toxin introduction would do. Did anyone see info hinting at they had considered growth rate? I found Pathogen Modeling Program 7.0 on the USDA website, and if the 72 hour cleaning period and the pasteurization processes are adhered to the numbers these people came up with are way off. If anyone would like the model I can post it.