Economic Modeling of Optimal Mitigation Strategies for Animal Related Biodefense Policies

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Economic Modeling of Optimal Mitigation
Strategies for Animal RelatedBiodefense Policies

• Levan Elbakidze
• Bruce A. McCarl
• Texas AM University
• Department of Agricultural Economics
• National Center for Foreign Animal and Zoonotic
  Disease Defense

2
Basic Components of Talk

• The economic problem
• Some theoretical deductions - hypotheses
• Conceptual modeling
• A simplistic first cut
• Broader project efforts

3
The Economic Problem

• Anticipation, prevention, detection, response and
  recovery all take money, much of which is spent
  in the absence of an event. So how do we
• Form best investment strategies considering cost,
  disease vulnerability, risk, and event
  characteristics?
• Best respond to an event?
• Assess effects on markets?
• Manage market information to minimize impacts?

Ex-Ante Invest
Ex-Post Fix
Anticipation Prevention Installation Screening
Detection Response Recovery
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H0 Tilting Factors
Ex-Ante Invest
Ex-Post Fix
Anticipation Prevention Installation Screening
Detection Response Recovery
Tilt toward ex-ante Event is more
likely Ex-ante Activity has multi
benefits Ex-ante Activity is more
effective Ex-ante Activity is cheaper Ex-post
treatment more costly Fast spreading disease More
valuable target Big demand shift -- health
Tilt toward ex-post Event is less
likely Ex-ante Activity is single purpose Ex-ante
Activity is less effective Ex-ante Activity is
expensive Ex-post treatment less costly Slow
spreading disease Less valuable target Little
demand shift -- health
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Background Is the Problem a New One?

• No, has many well known variants
• Food Quality/Safety from contaminants
• Invasive species
• Veterinary disease control
• Water management and impoundment construction
• Farmer machinery investment, crop mix
• Inventory theory, quality control, waiting line
  design
• Capital budgeting
• But with added features of deliberate actions at
  max points of vulnerability. Not an accident.
• All involve ex ante decisions but the ex post
  consequences occur only when event occurs -
  probabilistic

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Project Goals

• Examine the optimal economic allocation of
  portfolio of anticipation, prevention, detection,
  response and recovery actions
• Look at event characteristics (disease spread and
  economic damage consequences) under which
  strategies dominate
• Evaluate anticipation, prevention, detection,
  response and recovery strategy alternatives in a
  value of research or technology adoption context
• Look at market effects and recovery enhancement
  strategies
• Educate on economic principles

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Analytic Conceptualization Three Stages
STAGE 1 Pre Event
STAGE 2 Possible Event
STAGE 3 National and Local Management Decisions
1-p
Normal No Event
No Event Invest in Prevention Detection
Response capability
No Event Recover
Event Respond Prevent
Detect Recover
p
Event Respond Recover
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Analytic Conceptualization

• Major elements
• Irreversibility cannot instantly install
  investments when an event occurs
• Conditional response depending on investments
• Fixed cost versus infrequent occurring events
• Income depends on event and there is a large span
  of possible events
• Tradeoff between ex ante investment cost and
  occasional ex post event needs and associated
  costs
• Best strategy depends on investment cost,
  operating cost and probability

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Analytic Conceptualization
Cost Encountered
Expenditure on Ex-Ante
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Simple Example of model
Suppose we have the following decision. Today we
can invest in a facility which costs 10 and
protects 10 units. During the facility life we
use it under differing price, and yield events
that are uncertain. We have 200 units to
protect. Two projected futures exist. At the
time we use the facilities we know the
conditions. Two states of nature can occur  
Yield Yield with w/o Price
invest invest Probability No event 4
1.2 1.1 (1-pr) Event 3 0.9 0.1 pr
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Simple Example of model
Problem will have 2 stages   Stage 1 Investment
stage when we choose whether to construct
facility for which we define a single variable
Y   Stage 2 Operation stage when we use facility
and know prices, and yield which results in
variable to operate with (I) or without (NI) the
investment under each state of nature (the 4
variables X)  
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Simple Example of model
    Max -10Y(1-pr)4(1.9X1,I1.8X1,NI)prob3(
1.9X2,I0.1X2,NI)   s.t. 10Y X1,I

? 0 X1,I X1,NI
? 200 10Y
X2,I
? 0
X2,I X2,NI ? 200 X,Y gt
0  Result Y20 (invest in facility) if prob gt
0.119 
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Foot and Mouth Disease (FMD)

• 2001 UK Outbreak 2026 cases, 7.6-8.5 billion,
• Effects, Tourism 4.5-5.3, farmers and related
  industries (Mangen, and Barrell 2003),
• Spread air, transportation, artificial
  insemination, milk related transmission, direct
  contact, and wildlife
• Dont show signs of disease for a one or two
  weeks but are contagious. (Garner and Lack, 1995,
  The Economist 2003)

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Very Simplistic Case study

• Impact of prevention and treatment strategies in
  FMD setting
• Region Texas
• Unknown probability
• Investigate adoption of
• Ex-ante periodic animal examinations
• Ex-post ring slaughter of affected animals as a
  treatment strategy
• Look at expenditure balance as influenced by
• Probability level
• Spread rate
• Costs of implementation
• Effectiveness of response
• Recovery programs

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FMD mitigation options

• Vaccination (Schoenbaum and Disney 2003,
  Carpenter and/or Bates, Ferguson 2001,Berentsen
  1992, etc.)
• Slaughter (Schoenbaum and Disney 2003, Carpenter
  and/or Bates, Ferguson 2001,Berentsen 1992, etc.)
  
• Movement Ban (Ferguson 2001)
• Surveillance and Detection (McCauley et al. 1979)
• Monitoring imports (McCauley et al. 1979)
• Monitoring travel
• Tracing
• Recovery/information (Ryan et al. 1987)

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Formation of Animal Disease Management System

• Prevention -- systems where there are actions
  undertaken to try to avoid disease introduction
• Detection -- systems designed to screen animals
  to detect disease early to allow more rapid
  treatment and much lower spread than would
  otherwise be the case
• Response systems which involve actions to stop
  the spread and ultimately eradicate the disease
  and to avoid further economic losses.
• Recovery -- systems put in place to restore lost
  assets or demand shifts due to introduction of
  animal disease

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Simpler Example -Two Stages
P
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P Probability of outbreak L(N,R) - losses
associated with prevention, response and
occurrence of potential FMD outbreak. N -
number of tests performed annually on cattle in
the region. R - response activities in the
state of nature where outbreak occurs. Y -
binary variable representing investment in
surveillance system. CR - costs of response
activities FTC - fixed testing costs VTC -
variable testing costs. H(R) response
effectiveness function. proportion of animals
lost in case of an outbreak under various levels
of response actions D(t) - is the disease
spread function expressed in terms of days that
the disease is allowed to spread before
detection. V Value of losses per infected
herd t Maximum number of days disease is
undetected, 365/(N1)
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Assumptions

• Cost minimization of ex-ante costs plus
  probabilistic weighted cost of response.
• Response effectiveness
• Slaughter (Schoenbaum and Disney, 2003)
• Convexity
• Disease spread
• Exponential (Anderson and May, 1991)and
  Reed-Frost (Carpenter et al. 2004)
• Fast (0.4) and slow (0.15) contact rates
  (Schoenbaum and Disney, 2003)
• Source Elbakidze, Levan, Agricultural
  Bio-Security as an Economic Problem An
  Investigation for the Case of Foot and Mouth
  Disease, In process PhD Thesis, Department of
  Agricultural Economics, Texas AM University,
  2004.

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Model Experimentation

• Event levels Probability 0.001 0.9
• Severity or spread rate slow vs. fast
• Response effectiveness 17 - 30
• Variable costs of detection 0.1TVC, 0.01VTC
• Average herd size 50 to 400.
• Ancillary benefits FTC-50 per herd
• Recovery actions decrease loss of GI per animal
  by 30

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Event probability, Response effectiveness, VTC
costs
Results
RF
i Full variable Costs (VC), Response
Effectiveness (RE)0.17 ii VC, RE 0.3 iii
0.1VC, RE0.17 iv 0.1VC, RE0.3 v 0.01VC,
RE0.17 vi 0.01VC, RE0.3
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Spread Rate
Slow RF
Fast RF
i Full variable Costs (VC), Response
Effectiveness (RE)0.17 ii VC, RE 0.3 iii
0.1VC, RE0.17 iv 0.1VC, RE0.3 v 0.01VC,
RE0.17 vi 0.01VC, RE0.3
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• Herd Size
• Increasing herd size from 50 to 400
• increase of tests. Reached 39 for fast spread.
• Ancillary benefits
• Decrease FTC by 50 per herd
• No change in of tests
• Lower the probability of adoption in slow spreads
• Recovery actions
• Decrease in losses of GI per animal by 30
• Did not have a noticeable effect on surveillance
  intensity.

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Costs of an outbreak with and without ex ante
action
With detection
Without detection, Only response
i Full Variable Costs (VTC), Response
Effectiveness (RE) 0.17 ii VTC, RE0.3 iii
0.1VTC, RE0.17 iv 0.1VTC, RE0.3 v 0.01VTC,
RE0.17 vi 0.01VTC, RE0.3
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Hypotheses / Deductions

• The best investment/management strategy
• For slow spreading attacks addressed at
  low-valued targets with low consumer sensitivity
  would focus investment more on response and
  recovery.
• For rapid spreading attacks addressed at high
  valued targets with high consumer sensitivity
  items would focus more on prevention, rapid
  detection and rapid response (for example hoof
  and mouth).
• Would favor alternatives with value both under
  terrorism events and normal operations as opposed
  to single event oriented strategies (for example
  a comprehensive testing strategy that would also
  catch routine animal diseases).

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Conclusions

• Investigated relationship between detection
  (prevention) and slaughter (response) strategy.
• effort in a priori surveillance increases with
  threat level, cost reductions in surveillance,
  with disease spread rate, lower degree of
  effectiveness in response, and average herd size
• Estimates of lower bounds of losses due to FMD
  outbreak. Trade, consumer scare, other
  industries not considered.

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Conclusions

• Caution functional forms, parameters, cost
  estimates.
• Future
• Explicitly include vaccination, recovery,
• Disaggregate to localized strategies
• Cooperation/non-cooperation
• Include Risk Aversion
• Link to epidemiology model

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Future Work Items of Economic Concern

• Animal categories
• Unaffected
• Euthanized
• Dead from disease
• Impaired by disease
• Vaccinated
• Affected animal disposal
• Market value and use
• Carcass disposal
• Investment study
• Strategy costing
• Risk distribution
• Fixed vs event specific costs
• Markets
• Information management and demand
• Dynamic response
• Demand suppression
• Policy design
• Cooperation

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Future work link to epidemiology

• An economic model linked to epidemiologic model
• Multiple types of outbreaks
• Event occurrence and severity
• Consistency across strategies for comparison
• Broader mix of strategies
• Multiple vs. single purpose strategies
• Risk aversion
• Effects on optimal mix of strategies
• Possibly three stage formulation
• Localized decision making

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Data from Epidemiology

• N(r)(s,k,i,j,h)
• s State of nature
• k - region
• i - vaccinated, dead, infected, preventatively
  slaughtered, unaffected,
• j - stage along supply chain cow/calf,
  stocker, feed yard.
• h - mitigation strategy
• t - days
• r random trial

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N is either a probability distribution across
randomized trials, or there needs to be a table
of the above form for each random trial.
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More General Modeling Conceptualization

• Biological / Economic Input
• Strategy identification
• Strategy and disease spread
• Strategy costing
• Outbreak effect on markets
• Communication and markets
• Cooperative/non coop behavior
• Disease char scenarios

• Integrative/Economic Model
• Multi-strategy evaluation
• Anticipate, prevent, detect
• Respond, recover
• Investment analysis
• Cost of outbreak vs invest cost
• Vulnerability analysis

• Output
• Strategic options
• Emergency response
• systems
• Application of gaming
• Impacts on markets
• and trade

• Epidemiology Model
• Multi Strategy evaluation
• Extent of outbreak
• Effects on population
• Effects of alternative disease characteristics

• Supporting
• Models
• Biophysical
• Environmental

• Database input
• Background probabilities
• Strategy application points

Red areas are economists playground Expands on
existing regional, trade and national modeling
(ASM)
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Modeling Conceptualization

• Big elements
• Multi disciplinary study
• Domain experts, Veterinarians, Epidemiologists,
  Information technologists, Economists
• Ties together a number of models
• Designed for insights not numbers
• Will run backwards to see what characteristics of
  diseases and event probabilities merit what types
  of strategies

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Contemplated Studies

•  Vulnerability / risk assessment
• Effects of events without new strategies
• Cost of waiting in detection
• Attack scope and costs thereof
• Component strategy evaluations
• Anticipation
• Prevention
• Detection
• Response
• Recovery
• Investment / strategy mix study
• Strategy use
• Effects of disease characteristics
• Event probability that mandates actions
• Event specific vs multi outcome strategy value
• Risk / investment assessment
• Other
• Recovery information management
• Carcass disposal

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Plans

• Economists will participate in multidisciplinary
  efforts directed toward
• Development of modeling approaches simulating
  events and consequences of strategy use to
  facilitate event planning and overall agri-food
  terrorism management approaches.
• Construction of a threat simulation gaming
  environment that can be used in training decision
  makers, responders and industry members.

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Plans

• Economists will participate in multidisciplinary
  efforts
• Examination of possible events assessing
  costs/losses and identifying key sources of
  vulnerability
• Study of optimal investment patterns across
  prevention, detection, response and recovery to
  see how "best" total threat management is altered
  by threat characteristics.
• Investigation of the consequences of different
  management strategies for prevention, detection,
  response and recovery investments and operating
  rules.
• Managing information to facilitate faster
  recovery.
• Size of circles of treatment surrounding event
  how far out to euthanize, vaccinate, quarantine,
  test etc.
• Compensation schemes to facilitate compliance and
  discourage concealment.

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Response Effectiveness

• Convex
• Normalized to R1, H(R)0.83 (Schoenbaum and
  Disney,2003)
• R slaughter of herds in direct contact with
  diagnosed herds
• Results in 17 decrease in number of lost
  animals
• H(R)1-0.34R0.17R2

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Value of losses per infected herd

• C is the costs of slaughter, disposal, cleaning
  and disinfection and was assumed to be 69 per
  head (Bates et al, 2003).
• NH is average number of cattle heads per herd in
  Texas, which was found to be around 50 (Ernie
  Davis, Personal Communication, August 2004).
• CV is an average market value per cattle head
  reported to be 610.00.
• GI is gross income for Texas cattle and calves
  operations reported to be 6,829,800,000 in 2001
  (Texas Agricultural Statistics, 2001).
• TN is number of cattle heads in Texas reported to
  be approximately 13,700,000 in 2001.

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• FTC - 42,915,000, which was calculated by
  multiplying per herd testing costs (150) for
  operations of less than 100 animal heads
  (Schoenbaum and Disney, 2003) and the number of
  cattle operations in TX (286,100).
• VTC - are assumed to be 50 per visit per herd
  (Schoenbaum and Disney, 2003), under the scenario
  where an outside expertise is required to conduct
  the screenings at each farm. for the whole Texas
  the costs per visit are 502861114,305,000.
• CR - include expenses for appraisal (300 per
  herd), euthanasia (5.5 per head), and carcass
  disposal (12 per head) (Schoenbaum and Disney,
  2003). 1175 per herd. costs or response
  strategy corresponding to R1 are assumed to be
  37117543475.

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Exponential Spread

• Simulate number of infected herds for slow and
  fast spreads
• Estimate ln(D)bt
• Slow Spread b0.026(310-15), R20.43
• Fast Spread b0.2(310-25), R20.9

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Reed-Frost Formulation

• Simulate number of infected herds for slow and
  fast spreads
• TN Total number of herds in the area
• q Probability of avoiding adequate contact
• 1-q - probability of making adequate contact
  k/(TN-1)
• K number of adequate contacts a herd makes per
  time period
• Slow 0.15, Fast 0.4

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Reed-Frost Formulation

• Use generated data to fit logistic function
• Fast Spread ß1512040, ß2-0.319, R20.99
• Slow Spread ß114554.2, ß2-0.012, R20.97