Transcript Sow Herd Monitoring Tools in PRRSv Control

Sow Herd Monitoring Tools in
PRRSv Control Programs
PRRS Diagnostic and Control Workshop
Thessaloniki, Greece. August, 2012
Jose Angulo DVM
Boehringer Ingelheim Animal Health GmbH – Global PRRS Solutions
[email protected]
Outline
 PRRSv Sow herd stability
 Measurement tools
 Applications (sharing field examples)
PRRS control / Sow Herd Stability
 Prevent Infection
 Maximize Herd Immunity
 Minimize Exposure
Definition: Absent of clinical signs attributed to PRRSv and
NO EVIDENCE of resident virus circulation within the
population. = Weaning PCR negative pigs.*
*Gillespie(2003), Dufresne (2004)
First milestone in PRRSv control:
To Achieve Sow Herd Stability
Using dx test as monitoring tool
 Goal of testing
 Accuracy and Confidence level
 Cost of the sampling and testing
 Frequency of the sampling Vs size sampling
 Characteristics of the test (Sp,Se)
Using ELISA IDEXX Herd Check
 Commercial kit
(Standard)
 Reliable and well
implemented across
D-Labs.
 Costless vs PCR
Keep in mind:
 Measure Exposure, NO
protection.
 Consistent absent of
anamnesic Ab response in
present of complete
protection vs disease ( ab ≠
protection)
 Unable to differentiate Field
exposure vs Vaccine
 Seropositive pigs become
seronegative overtime or
following repeated
vaccination
Murtaugh (2005)
Measuring sow herd stabilization
Serum Profiles.
• ELISA (IDEXX Herd Check) measures exposure.
–
–
–
–
Population test
SP value average (>0.4 +)
SP value Standard Variation StD
% Positive
Reduce Resident virus circulation
within Sow Herd & Weaning negative
pigs.
Gradual (%)
Understanding the serological picture with
ELISA Idexx
Comportamiento Serologico Hembras de Reemplazo Inoculo Vivo (LVI)
1.0000
100%
90%
0.6000
70%
60%
0.4000
50%
0.2000
40%
30%
0.0000
D77/L
D77/K
D77/J
D77/I
D77/H
D77/G
D77/F
D77/E
D77/D
D77/C
D77/B
D77/A
D28/L
D28/K
D28/J
D28/I
D28/H
D28/G
D28/F
D28/E
D28/D
D28/C
D28/B
D28/A
-0.2000
% Positivos
80%
D0/L
D0/K
D0/J
D0/I
D0/H
D0/G
D0/F
D0/E
D0/D
D0/C
D0/B
D0/A
Desviacion Estandar
0.8000
-0.4000
10%
0%
DS
Replacement naive gilts batches
LVI in GDU (n=35)
20%
% Pos
Poly. (DS)
Days in GDU
D0
Standard Dev
0.004
D28
D77
0.652 0.476
Angulo, Private practice, 2003
Understanding the serological picture with
ELISA Idexx
Seguimiento perfiles serologico hato
3
100%
90%
2,5
80%
70%
Post
2
sp
60%
1,5
50%
40%
1
30%
20%
0,5
10%
0
0%
2003
n=35 (Parity structure)
Sow herd serum profile
monitoring along the line with
MLV mass vaccinations.
2004
Apr 05
Prom s/p
Okt 05
Dev. Stand.
% Positivos
2003
2004
Abril-05
Octubre -05
SP Avg
2.393a
0.848b
0.575b
0.422b
Stand Dev
1.015
0.622
0.989
0.319
% Positive
100
70
57
57
Angulo, IPVS proceedings, 2006
Applying quality tools in the analysis:
Box Plot.
NWA Quality
Analyst
Software
Oneway Analysis of SP PRRS Hato By Fecha
4
SP PR R S H ato
 Data exploration tool to
analyze and find trends
and relationships
identifying unique
characteristics of the
data analyzed.
Facilitating its description
and interpretation.
3
2
1
0
agost 07
JMP
Software
Feb-0 8
Fecha
Quantiles
Level
Minimum
10%
25%
Media n
75%
90%
Maximum
agost 07
0
0.114795
0.440744
0.944229
1.658523
2.356496
2.896979
Feb-0 8
0
0.395297
0.944963
1.743764
2.620429
3.028118
4.027211
Std Err Mean
Means and Std Deviations
Daniel (2004). Biostats, 4th ed.
Level
Mean
Std Dev
agost 07
Number
60
1.11777
0.785800
0.10145
Low e r 95 %
0.9148
Upper 95%
1.3208
Feb-0 8
60
1.74107
0.978845
0.12637
1.4882
1.9939
Using Box Plot in Serology Profiles
 Information about:
Shape, Dispersion
and Center of the
data.
Boxplot of 1, 2, 3
Largest Value
4
Outlier
75th Percentile
3
Data
50th
Median
» Central Tendency
Percentile
2
» Dispersion stats
Mean
25th1Percentile
» Skew
» Outlying
0
Smallest Value
1
2
Interval coefficient
3
Measurements
» Quick look at expected
values
MINITAB, 2012
Sow Herd PRRSv serology
Variable
Samples Mean
Std. Dev.
Target
Hato reproductor Vx PRRS Ma
Pre Vx PRRS
PRRS_MARZO_08
70
1.03543 1.18679
0.4
PRRS_HR_AGO08
70
0.369097 0.417897
0.4
s/p
0 0.5 1 1.5 2 2.5 3 3.5 4
Based on Median and Quartiles
Target
Specs
PRRSv Serology after PRRSv program
implementation
Oneway Analysis of SP PRRS Sitio 5 hato By Mes
6
Sitio 5 hato
SP PR R S
5
4
3
2
1
0
7-Jun
Dic 07
Mes
Quantiles
Level
Minimum
10%
25%
Median
75%
90%
Maximum
7-Jun
0.046
0.409
0.9145
1.932
3.848
4.7236
5.617
Dic 07
0.186
0.413
0.689
1.311
1.748
1.9088
2.375
Means and Std Deviations
Level
Number
Mean
Std Dev
Std Err Mean
Low er 95%
Upper 95%
7-Jun
37
2.38370
1.62982
0.26794
1.8403
2.9271
Dic 07
35
1.26434
0.60027
0.10146
1.0581
1.4705
PRRSv Serology: Outbreak Recovery
picture (4,500 sows)
Variable
Mean
Std. Dev.
Minimum MaximumConf Level
0.740294
0.027
2.988
95
S_J_I_NOV_04
1.019
S_J_I_ENE_05
1.69907 1.5696
0.054
4.296
95
S_J_I_MAR_05
2.17
0.414104
0.996
2.771
95
S_J_I_JUL_05
1.13127 0.602007
0.193
2.204
95
S_J_I_NOV_05
0.628467 0.469602
1.745
95
0
0 0.5 11.5 22.5 3 3.5 4 4.5
Based on Median and Quartiles
Target
Specs
PRRSv Serology: Outbreak picture
Variable
Mean
Std. Dev .
Minimum Maximum Conf Level
48% post
SP_ABR05
0.627769 0.639422
0
3.045
95
2.11
95
1.805
95
74% post
SP_SEP05
0.853828 0.568553
0.09
57% post
SP_FEB06
0.641371 0.516166
0
71% post
SP_SEPT_06
0.837429 0.581574
0.092
2.112
95
0.899152 0.688371
0.103
2.842
95
7.488
95
79% post
SP_ABR_07
72% post
SP_JUL_07
2.02106 2.13882
0
1
2
Based on Median and Quartiles
3
4
5
6
7
0
8
Target
Specs
Monitoring control strategies
Variable
Mean
Std. Dev . Minimum MaximumConf Lev el
% pos 96.6
SP_DIC03
1.63167 0.810751
0.25
3.07
95
1.64286 0.951137
0.2
3.64
95
1.55743 0.785521
0.25
3.07
95
3.6081
95
1.884
95
% pos 94.2
SP_MAY 04
% pos 94.2
SP_J UL_04
Context:
• 2,000 sows FF
farm.
• Mass Vx every
6 months (04).
• 2006 Mass Vx
every 3 months.
% pos 68.5
SP_F EB_06
1.01923 0.863162
0
% pos 83
SP_J UL_06
0.9045710.465189
0
0.5
1
Based on Median and Quartiles
1.5
2
2.5
3
3.5
0.195
4
Target
Specs
Angulo, AMVEC, 2006
Home take messages
 Tools for measuring sow herd stability are available
 Understanding of diagnostic tests is critical
 Add all measurements to bring the general picture
 Link tools with goals
 Simple tools like Box Plot can add value to the
analysis, interpretation and decision making process.
» Statistic Software or Excel!!
Thanks for your attention