modelling the risk of foot and mouth disease transmission ......population of buffaloes in knp...

Modelling the risk of Foot and Mouth Disease transmission at the wildlife/livestock interface of 

Kruger National Park

Ferran Jori & Eric Etter UPR 22, CIRAD

FMD context in RSA

• Endemic in KNP

• Efficient control measures: – Fence– FMD zonation– Systematic vaccination– Regular surveillance– Mouvement control

• No Outbreaks of FMD since 1983: Free status by OIE since 1995

• At least 6 declared outbreaks since 2000 

• A  diversity of factors are considered responsible for this situation

In this context, there is a need • to understand and quantify the pathways leading to FMDV transmission between wildlife and cattle

• to quantify parameters having a major contribution to the risk of transmission

• to identify those areas and scenarios where the risk is higher in order to target surveillance and control efforts

Risk Analysis

A systematic method to deal with risk.

RISK COMMUNICATION

HAZARDIDENTIFICATION RISK ASSESSMENT

Release assessment

Exposure assessment

Consequence assessment

Risk estimation

RISK MANAGEMENT

Risk evaluation

Option evaluation

Implementation

Monitoring and review

Materials and Methods• Risk =annual probability for one 

cattle being infected by FMDV due to contacts with wild buffalo at the KNP interface.

• Two events were considered:

1

2

– Event 1: buffaloes escaping from KNP

– Event 2: Cattle entering KNP

Materials and Methods• Quantitative risk assessement was 

carried using software package @Risk (Palissade Corporation)

• Inputs are probability distributions calculated according to the information available or produced

• They are combined with each other several thousands of iterations to produce  final estimation of risk.

Quantitative estimation of risk

• Monte Carlo simulation• Stochastic & iterative approach

Mathematic modelProb buffalo excreting

Prob buffalo crossing fence

Output (Risk) :Buffalo excreting and crossing the fence

Event pathway considered

Is the buffalo carrying transmissible

virus?

No (1-P1)

P1

R=P1

yes

Event pathway considered

Is the buffalo carrying transmissible

virus?

No (1-P1)

P1

R=P1

Is thisbuffalo crossing

the fence?

yes

Event pathway considered

Is the buffalo carrying transmissible

virus?

No (1-P1)

P1

R=P1xP2

Is thisbuffalo crossing

the fence?

yes

P2 yesNo (1-P2)

Event pathway considered

Is the buffalo carrying transmissible

virus?

No (1-P1)

P1

R=P1xP2xP3

Is thisbuffalo crossing

the fence?

yes

P2 yesNo (1-P2)Is there any

contact with cattleOUTSIDE of the KNP?

yes

No (1-P3)P3

Event pathway considered

Is the buffalo carrying transmissible

virus?

No (1-P1)

P1

R=P1xP2xP3xP4

Is thisbuffalo crossing

the fence?

yes

P2 yesNo (1-P2)Is there any

contact with cattleOUTSIDE of the KNP?

yes

No (1-P3)P3 yes

No (1-P4)

Is this contact responsible of transmission?

P4

Event pathway considered

Is the buffalo carrying transmissible

virus?

No (1-P1)

P1

R=P1xP2xP3xP4

Is thisbuffalo crossing

the fence?

yes

P2 yesNo (1-P2)Is there any

contact with cattleOUTSIDE of the KNP?

yes

No (1-P3)P3 yes

No (1-P4)

Is this contact responsible of transmission?

Risk appearsP4

Event pathway considered

Is the buffalo carrying transmissible

virus?

No (1-P1)

P1

R=(P1xP2xP3xP4)+(P5xP4)

Is thisbuffalo crossing

the fence?

yes

P2 yesNo (1-P2)Is there any

contact with cattleOUTSIDE of the KNP?

yes

No (1-P3)P3 yes

No (1-P4)

Is this contact responsible of transmission?

Risk appears

Is there any contact with cattle

IN the KNP?

No (1-P5)

yesP5

P4yes

No (1-P4)

Is this contact responsible of transmission?

P4

+

Epidemiology of FMD in buffalo.• Seasonal breeders. Most calves born in summer (Dec-April)• Each calf cohort within a herd generally becomes infected from 6

months onwards when calves are weaned.• During acute infection there is considerable excretion of virus in all

body secretions during a period ranging between 1 and 14 days.• At 1 year age, most calves have become infected with FMD virus• After that period, 17 to 70 % of the animals remain carriers

thereafter and this status might persist up to 24 years (Vosloo et al.,2007) or will heal completely.

• Transmission of virus from adult carriers to susceptible animals appears to be a rare event.

Inputs Release AssessmentName Function Mean value Source

Population of buffaloes in KNP (1995-2006) RiskNormal(23377; 5293,1) 23377 KNP records

Fraction of young in KNP herds RiskNormal(0,1171; 0,0264) 0,117 KNP records

Probability for one buffalo to leave KNP RiskNormal(0,0031;0,00106) 0,003105885 KNP/Mpumalaga VS Records

Probability for a young buffalo to leave KNP RiskNormal(0,158;0,092) 0,17 Mpumalaga VS records

Prevalence in young buffalo (< 1 year) 0,89 Thomson, 1984

Prevalence in adult buffalo (> 1 year) 0,92-0,98 Thomson, 1984

Viraemy (in days) RiskPert(1; 6; 14) 6,5 Gainaru et al., 1986

Age of weaning (in months) RiskPert(2;4;6) 4 R. Bengis, pers. comm.

Probability for a young to excrete (Age of weaning-12)/12*days of viraemia/365 0,016 Thomson, 1984Probability of an excreting buffalo becoming a carrier RiskPert(0,17;0,6;0,7) 0,54 Vosloo, 2007

Inputs Exposure AssessmentName Function Mean value Source

Total cattle population in KNP buffer zone Fixed value: 84105 Data from 2007

Vaccination coverage (1996-2006) RiskNormal(0,754545;0,0665) 0,7545157 Mpumalaga VS records

Probability of observing a contact RiskBeta(18+1; 30-18+1) 0,59375 Farmers questionnaire Bushbuckridge

Time of contact (mns) buffalo / cattle RiskDiscrete({8.54.114.1194.2197};{0,33.0,22.0,167.0,167.0,11}) 467,5 Farmers questionnaire

Bushbuckridge

Probability for a cattle to cross the fence RiskBetal(22543;601974;0;1) 0,036038994 Fence questionnaireProbability of contact bw buffalo and cattle IN the park RiskBeta(371+1;8760-371+1) 0,04245606 Farmers questionnaire

BushbuckridgeEstimated time of contact for cattle IN the park (mns) RiskDiscrete({2160.720}; {12.365}) 765,8356 Farmers questionnaire

Bushbuckridge

Consequence Assessment

• Transmission rate for carriers (Trc) 0,0148± 0,014 infs/carrier/month calculated out of 9 experiments buffalo –cattle

• We considered transmission rate for young excreting buffalo to be 100%        

• (Pty) = 1 if Tc> 360 mnIf Tc<360 mn then Pty=0.5

Results

Model Outputs

Probabilities on escaped buffalo

On average, It is 13 times more likely to get a carrier buffalo crossing the KNP fence than an acutely excreting buffalo doing so

-10 0 10 20 30 40 50 60

Valu

es x

10^

4

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

Final risk estimationsEvent 1

Event 2

5,0% 90,0% 5,0%

0,0066 0,1675

-0,0

5

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

0,50

0

2

4

6

8

10

12

14

FR1=Probability of one cattle to be infected outside KNP

FR1=Probability of one unvaccinated cattle to be infected by FMDV outside KNP

Minimum 7,244E-005Maximum 0,4895Mean 0,0606Std Dev 0,0557Values 5000

Crude sensitivity analysis FR1

-0,3

-0,2

-0,1 0,0

0,1

0,2

0,3

0,4

0,5

0,6

F

Sensitivity analysis FR2

Results

Scenario playing

Scenario comparison of FR1 depending on numbers of escaped buffaloes

Influence of drop in vaccination coverage in risk of transmission

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

ConclusionsAreas of improvement

• Can be improved as new information is producedCf: Vaccination efficiency

• Some inputs still require additional data to reduce uncertainty– Contacts wildlife/cattle difficult 

to assess– Number of young animals 

escaping

• Model requires peer review & validation before it can be used for management purposes

Positive points

• Development of a tool useful to start modelling transmission at the wildlife/livestock interface

• Provides consistent responses to major FMD control strategies:• escapes of buffaloes,• vaccination coverage

• Highly flexible, integrative and relatively easy to use/ communicate

Possible applications

• Scenario playing / discussion with stakeholders– What if scenarios (cf. Increase 

of buffalo populations in KNP)• Compare risk in different areas 

or settings– Limpopo vs Mpumalanga 

Province– KNP vs LNP or GNP interface

• Identification of high risk areas in absence of efficient reporting systems

• Identify information gaps

Acnowledgements• Bruce Gummow, University of Pretoria (JCU)• Ben Du Plessis, Mpumalanga Veterinary Services• Roy Bengis, KNP• Nada Abu Samra, University of Pretoria • Livio Heath, Wilna Vosloo (ARC‐OVI)• Dipa Brahmbatt, TAMU

Thank you for your attention!