modelling tools and methods for decision support on contagious animal diseases

Monique Mourits and Helmut Saatkamp

Modelling tools and methods for decision

support on contagious animal diseases

Monique Mourits and Helmut Saatkamp

Wageningen University – Business Economics

Aim of presentation

Aim:

� To present a short overview of some modelling tools and

methods for decision support in contagious livestock

diseases

� Emphasis: � Emphasis:

� Usefulness, possibilities

� Limitations

� NOT: complete methodological overview

Regulations contagious livestock diseases

� Aim/goal of decision makers: minimization of disutility

(discomfort, impact, …)

� by considering the impact on various stakeholder groups

affected (whole society): producers, agri-business,

consumers/citizens, region/country

� Decision making criteria:

� Until 1980s: veterinary

� Late 1980s-early 1990s: financial-economic

� Late 1990s: animal welfare, socio-ethical

� Late 1990s-early 2000s: human health

Regulations contagious livestock diseases

� Choice of strategies; how?

� Sets of measures (discrete alternatives)

� Prevention, monitoring and control

� Satisfies the goals/aims/preferences

� Within given constraints: legal, resources, …� Within given constraints: legal, resources, …

� Complex decision making problem:

� Need for decision support

Models for decision support

All models are wrong, but some are useful

(Zeger, S.L. (1991) Statistical reasoning in epidemiology. American Journal of

Epidemiology 134: 1062-1066)

Usefulness criterion: improvement of decision making with

use of models (compared to without use):

� Insight, understanding (qualitative)� Insight, understanding (qualitative)

� Model output (quantitative)

Models: a tool to support decision making by providing

insight and useful information, as an input for improved

reasoning, aiming at taking better decisions


Models:

� Conceptual models:

� Qualitative relationships of a system

� Very important, usually underestimated

� Use: before AND after mathematical modelling� Use: before AND after mathematical modelling

� Mathematical models

� Quantitative relationships, equations and data

� Simulation (what-if), particularly if empirical research is

not possible or very expensive


‘Pure’ epidemiological modeling of livestock diseases:

� Aimed at understanding the disease dynamics

� ‘Part’ of the ‘real world’, scientifically oriented, detailed

Economic modeling of livestock diseases:Economic modeling of livestock diseases:

� Aimed at decision support (discrete alternatives)

� Holistic, comprehensive, integration of ‘all required’

knowledge (data problems)

� Bridge between mono-disciplinary approaches

Two examples

Two model studies performed to evaluate;

� The probability of introduction of CSFV and the

impact of preventive measuresimpact of preventive measures

� The impact of various FMD control strategies

Models for decision support 1:

introduction/prevention

Aim of the model:

� Estimation of probability of introduction of CSF in The

Netherlands

Analysis of the main risk factors for introduction of CSF in � Analysis of the main risk factors for introduction of CSF in

The Netherlands

� Analysis of the impact of prevention measures



Methodology:

� Pathway diagram for introduction of CSF

� Excel/@Risk model

Data: statistical, epidemiological, expert opinion� Data: statistical, epidemiological, expert opinion

� Scenario tree analysis

� Sensitivity analysis


introduction/prevention Pathway diagram:

Pathways

for virus

introduction

into a region

Effect of

preventive

measures

Endogenous pathwaysExogenous pathways

Import of batch of

domestic animals

At least 1

animal infected/

contaminated

Illegal import

of live animals

At least 1

animal infected/

contaminated

Import of

wild animals

At least 1

animal infected/

contaminated

Virus not

detected by

screening/testing

Returning

livestock trucks

Virus contaminated

livestock truck

Livestock truck

not disinfected

properly

Wildlife

Infected aerosols

Air currents LaboratoriesIllegal import of

animal products

Virus contaminated

animal products

Virus not

inactivated during

processing/maturing

Import of animal

products for

human consumption

Humans carrying

virus

Tourists Professional

people

Harbours,

airportsImport of

genetic material

Virus

contaminated

material

Virus sources

Virus not

detected by

screening/testing

Virus contaminated

kitchen offal

At least 1

animal infected

Illegal import of

genetic material

Import of

manure

Virus contaminated

manure

Virus not

inactivated during

transport/treatment

Birds, pets,

arthropods

and rodents

Virus contaminated

animalsInfection or

contamination

with virus

Virus

contaminated

material

Genetic material

not detected by

custom controls

Animals not

detected by

custom controls

Virus contaminated

animal products

Products not

detected by

custom controlsmeasures

Destination

for life

Destination

for slaughter

Swill

Virus not inactivated

by heat treatment

Infective dose

PRIMARY

OUTBREAK !

screening/testingproperly

Direct or indirect contact with

susceptible domestic animals

processing/maturing

Products fed as swill

screening/testing

Main routes of

virus transfer

to susceptible

domestic animals

transport/treatmentcustom controls custom controls custom controls


introduction/preventionMatrix for target country:

� Pathways (n)

� Countries of origin (m)

Split-up of n * m scenario trees

Each scenario tree: contribution to PCSF

Totalization of all contributions: total PCSF



0.03

0.035

0.04

0.045

Pro

ba

bility

of C

SF

V in

tro

du

cti

on

in

to th

e N

eth

erl

an

ds

Types of result : Probability of CSFV introduction into NL per

epidemic and per year from various countries of origin

0

0.005

0.01

0.015

0.02

0.025

Ger

man

y

Franc

e

Italy

Bel

gium

Luxe

mbo

urg

Uni

ted

Kin

gdom

Irela

nd

Den

mar

k

Gre

ece

Spa

in

Por

tuga

l

Aus

tria

Finla

nd

Sw

eden

Country of origin

Pro

ba

bility

of C

SF

V in

tro

du

cti

on

in

to th

e N

eth

erl

an

ds

Per epidemic

Per year


introduction/prevention Types of result: Relative contribution of pathways to the

annual probability of CSFV introduction in the NL

Pork products

1.3%Piglets

6.6%

Breeding pigs

Livestock trucks

64.8%

Breeding pigs

17.6%

Fattening pigs

9.7%



Main conclusions:

� careful use of absolute values (estimated Pcsf)

� indication importance of countries of origin and pathways

� priority setting strategic preventive measures

� additional tactical preventive measures� additional tactical preventive measures

Useful in Benefit/Cost-studies on prevention measures;

how to achieve considerable risk reduction at reasonable

costs?

Models for decision support 2: impact/control

Aim:

� Analysis of the social –economic consequences of FMD

outbreaks under various disease control strategies

� What-if simulation

� Uncertainty� Uncertainty


Methodology:

� Integrated epidemiological-economic modeling

� Epidemiological modeling (InterSpread approach):

� Spatial: exact farm location and characteristics

� Stochastic (Monte Carlo): disease spread and disease � Stochastic (Monte Carlo): disease spread and disease

control mechanisms

� Dynamic: parameter values can change with time


� Output: descriptive statistics of the simulated epidemic:

� # farms infected/detected

� # farms (pre-emptively) culled

� # farms vaccinated

� Duration (days)

� Size of Surveillance zone

� Output: input for economic analysis


� Economic analysis:

� Direct and direct consequential costs (accounting of

the epizootic): Excel, accounting

� Indirect consequential costs (price effects):

• Supply and demand of animals and products• Supply and demand of animals and products

• Partial-equilibrium modeling: producers and

consumer surpluses

• Mathematical programming

What to expect……….

when a FMD epidemic starts at a dairy farm surrounded by

240 farms in a radius of 10 km (SPLA)

1923 farms in a radius of 10 km (DPLA)1923 farms in a radius of 10 km (DPLA)

and controlled by EU compulsorymeasures?

Basic results

SPLA (A) DPLA (B)

50% 95% 50% 95%

# infected farms 3 38 1583 23621# preventively culled 8 49 679# preventively culled 8 49 679

1053# farms in MCZ (10km) 335 4406 12895 58165

Epidemic length (days) 38 105 375 end

Alternative strategies DPLA – 95% results

EU Prev-1 Vacc-2 Vacc-4

Farms

depopulated 24.674 2.425 178 169

vaccinated 0 0 1.210 2.519

in MCZ 58.165 10.484 8.478 7.808in MCZ 58.165 10.484 8.478 7.808

Animals (*1000)

culled 6.211 463 43 40

vaccinated 0 0 284 541

Length (days) > 400 200 84 75

Alternative strategies DPLA – 95% results

(in mln €)

EU Prev-1 Vacc-2 Vacc-4

‘to live’ ‘to kill’ ‘to live’ ‘to kill’

Control >4.620 421 94 174 80 235

Business Business interruption > 2.540 200 166 121 171 111

Market losses > 499 511 623 467 636 477

Total >7.659 1.132 883 762 886 824


Main conclusions: cons

� Model requires a lot of specific input

� Contact structure

� Farm location, type and numbers per animal type (area specific??)� Farm location, type and numbers per animal type (area specific??)

� Epidemiological data scarce; use of expert opinions

� Sensitivity analyses a must (ranking alternatives)


Main conclusions: pros

� Simulation realistic:

� farm data + location, spread mechanisms, control

measures, etc.measures, etc.

� Very flexible to include uncertainty

� Helpful to identify knowledge gaps

� Helpful for decision support (in peace time)


Main conclusions: pros

� Helpful for decision support (in peace time);

� Control-driven; capacity problems, definition of risk areas.....

� Allocation of resources; trade off possibilities between prevention,

monitoring and control

� Evaluation of epidemic disease risk financing instruments

� Evaluation of economic instruments to reduce market disruptions

� Evaluation harmonization of cross border regulations

� ....


� Probability of introduction of CSF and impact of preventive measures:

De Vos et al. (2004) Risk Analysis 24: 237-253

� Effectiveness of movement-prevention regulations to reduce the

spread of foot-and-mouth disease in The Netherlands: Velthuis and

Mourits (2007) Preventive Veterinary Medicine 82 (3-4): 262-281

� Consequences of CSF and control measures: Mangen et al. (2003)

European Review of Agricultural Economics 30(2): 125-154

� Multi Criteria Decision Making to evaluate control strategies of

contagious animal diseases: Mourits et al.(2010) Preventive

Veterinary Medicine 96 (3-4): 201-210.

modelling tools and methods for decision support on contagious animal diseases

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