D10888.PDF

Rev. sci. tech. Off. int. Epiz., 2011, 30 (2), 555-569
The use of modelling to evaluate and adapt
strategies for animal disease control
C. Saegerman*, S.R. Porter & M.-F. Humblet
Faculty of Veterinary Medicine, Department of Infectious and Parasitic Diseases, Research Unit of
Epidemiology and Risk Analysis Applied to Veterinary Sciences (UREAR), University of Liège, Boulevard
de Colonster 20, B42, B-4000 Liège, Belgium
*Corresponding author: [email protected]
Summary
Disease is often associated with debilitating clinical signs, disorders or
production losses in animals and/or humans, leading to severe socio-economic
repercussions. This explains the high priority that national health authorities and
international organisations give to selecting control strategies for and the
eradication of specific diseases. When a control strategy is selected and
implemented, an effective method of evaluating its efficacy is through modelling.
To illustrate the usefulness of models in evaluating control strategies, the
authors describe several examples in detail, including three examples of
classification and regression tree modelling to evaluate and improve the early
detection of disease: West Nile fever in equids, bovine spongiform
encephalopathy (BSE) and multifactorial diseases, such as colony collapse
disorder (CCD) in the United States. Also examined are regression modelling to
evaluate skin test practices and the efficacy of an awareness campaign for
bovine tuberculosis (bTB); mechanistic modelling to monitor the progress of a
control strategy for BSE; and statistical nationwide modelling to analyse the
spatio-temporal dynamics of bTB and search for potential risk factors that could
be used to target surveillance measures more effectively.
In the accurate application of models, an interdisciplinary rather than a
multidisciplinary approach is required, with the fewest assumptions possible.
Keywords
Animal disease – Control strategy – Epidemiology – Evaluation – Modelling.
Introduction
Since disease is often associated with debilitating clinical
signs, disorders or production losses in animals and/or
humans, causing severe socio-economic repercussions,
national health authorities and international organisations
naturally give a high priority to selecting strategies to
control and eventually eradicate that specific disease.
Choosing a strategic option to deal with a specific disease
depends on several factors:
– the true prevalence of the disease in the animal
reservoir(s)
– the socio-economic context
– the animal health surveillance system
– the policy set by the relevant health authorities.
The overall control strategy can range from a systematic
vaccination strategy (e.g. of replacement stock and mature
animals) in heavily infected countries, to selective
vaccination (e.g. of replacement stock only) and eventually
to a control strategy (testing and slaughtering, associated
with a total ban on vaccination). At the same time, an
adequate veterinary infrastructure must be established,
enabling epidemiological surveillance and the control of
animal movements (e.g. to prevent the risk of disease entry
through importation).
In addition, to gain and maintain disease-free status for an
entire country is often not achievable, especially in the case
of diseases that are difficult to control at national
boundaries. For this reason, it is recommended that
either a regional approach should be taken to
disease control, involving inter-country cooperation, or
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compartmentalisation within a country. In the latter, each
compartment includes animals that are clearly recognised
as part of a unique sub-population, which has no or
limited epidemiological links with other sub-populations
at risk. The measures taken to identify this sub-population
should be documented in detail (for traceability) and must
take into account the epidemiological characteristics of the
disease (21, 27).
When a specific control strategy is selected and
implemented, it is then necessary to evaluate its efficacy.
This strategy can simply be evaluated by the follow-up
of some frequency indicator, such as the prevalence rate or
the incidence rate, which is more linked to the risk
of infection, but also by the alternative use of modelling.
A model is a simplified representation of a real-world
situation or process that occurs in the population (17). It is
also a logical description and interpretation of a theory,
describing observed behaviour, and simplified by ignoring
details (29). Models are usually classified as one of the
following:
– descriptive (a simplified description of observations to
aid understanding)
– predictive (defining the past behaviour of a system in
order to predict the future)
– explicative (to simulate the system while considering
some hypotheses about elementary mechanisms) (3).
Despite remarkable progress in the biology of contagious
diseases, which has provided numerous tools to quantify
the distribution of pathogens within populations,
veterinarians and physicians have long remained reluctant
to apply models. Today, the emergence and persistence of
numerous infectious diseases have prompted many
practical and theoretical questions which cannot be
approached without modelling the natural and\or
controlled dynamics of infections within the populations
concerned (20).
Numerous models exist to evaluate control strategies (e.g.
7). The aim of this paper is not to present an exhaustive list
but to detail some examples.
– The first model is a classification and regression tree
(CART) to evaluate the effectiveness of West Nile fever
(WNF) and bovine spongiform encephalopathy (BSE)
surveillance (18, 24).
– The second model is a variant of the first. Classification
and regression tree modelling is used to better understand
the relative importance of and interrelations among
different risk variables of a complex (multifactorial)
disorder. This approach allows evaluation or adaptation of
the control strategy (32).
– The third is a model dedicated to the evaluation of skin
testing by veterinarians as part of the surveillance of bovine
Rev. sci. tech. Off. int. Epiz., 30 (2)
tuberculosis (bTB). Two options are proposed: performing
a global and regional evaluation or, alternatively,
monitoring the effect of an awareness campaign (11, 12).
– The fourth is a mechanistic model to monitor
the progress of a control strategy for BSE (25).
– The fifth is a molecular model to analyse the spatiotemporal dynamics of bTB, in search of potential risk
factors that could be used to better target surveillance
measures (10).
Modelling the clinical pattern
of a disease to ensure
its early detection worldwide
Ensuring an early warning of emerging or reemerging animal diseases is a key parameter of any control
strategy (15). One interesting method is to use a
CART analysis. The CART analysis is a non-linear and nonparametric model, fitted by binary recursive partitioning of
multidimensional co-variate space (1, 24, 28). Using
CART 6.0 software (Salford Systems, San Diego, United
States), the analysis successively splits the data set
into increasingly homogeneous subsets until it is stratified
and meets specified criteria. The Gini index is normally
used as the splitting method, and a ten-fold crossvalidation is used to test the predictive capacity of the
trees obtained. The CART analysis performs crossvalidation by growing maximal trees on subsets of data,
then calculating error rates based on unused portions of
the data set.
To accomplish this, CART divides the data set into
ten randomly selected and roughly equal parts, with each
‘part’ containing a similar distribution of data from the
populations of interest (i.e. confirmed versus suspected
cases). The analysis then uses the first nine parts
of the data, constructs the largest possible tree, and uses
the remaining 1/10th of the data to obtain initial estimates
of the error rate of the selected sub-tree. The process is
repeated, using different combinations of the
nine remaining data subsets and a different 1/10th data
subset to test the resulting tree. This process is repeated
until each 1/10th data subset has been used to test a tree
that was grown using a 9/10ths data subset. The results of
the ten mini-tests are then combined to calculate error
rates for trees of each possible size; these error rates are
applied to prune the tree that was grown using the
entire data set. The consequence of this complex process is
a set of fairly reliable estimates of the independent
predictive accuracy of the tree, even when some data for
independent variables are incomplete and/or
comparatively scarce.
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For each node in a CART-generated tree, the ‘primary
splitter’ is the variable that best splits the node, maximising
the purity of the resulting nodes. When the primary
splitting variable is missing for an individual observation,
that observation is not discarded but a surrogate splitting
variable is sought instead. A surrogate splitter is a variable
in which the pattern within the data set, relative to the
outcome variable, is similar to that of the primary splitter.
Thus, the program uses the best available information to
compensate for missing values. In data sets of reasonable
quality, this approach allows all observations to be used,
which is a significant advantage over more traditional
multivariate regression modelling, in which missing
observations for any predictor variables are often
discarded. Further details about CART are presented in
previously published articles (e.g. 24, 28). Two practical
examples are developed below to explain the CART
procedure.
In summary, the peak of WNF occurrence was observed in
September, whatever the country, and was linked to vector
activity in temperate climates. A significant difference
between Italy and France was observed in the delay
between initial clinical signs and veterinary consultation.
This difference is thought to be due to a lack of awareness
of the disease at the time of the study and/or to the absence
of a centralised epidemiological surveillance system. No
clinical sign was significantly associated with WNF. Despite
similar clinical presentations in the three countries, the
occurrence of hyperthermia was more frequently reported
in France. French owners living in endemic areas may be
more attentive and seek warning clinical signs. The CART
analysis demonstrated the major importance of
geographical locality and month when reaching a diagnosis
and emphasised the differences in predominant clinical
signs, depending on the period of detection of the
suspected case (whether WNF was epizootic or not)
(Table I).
Early detection of West Nile fever
Despite several previous studies on the subject, WNF
remains a challenging disease and an important veterinary
public health issue. Modelling the clinical pattern of a
disease worldwide to ensure its early detection is a key
parameter of passive surveillance. Both developed and
developing countries can apply such surveillance because
it is not onerous. Awareness of the potential emergence of
WNF should be promoted. A centralised passive animal
surveillance system that reports both suspected and
confirmed WNF cases, in a standardised manner, should
be implemented and well organised in at-risk countries, to
allow early detection. Communication between countries
and between veterinary and public authorities is essential
for efficient WNF control, in view of the currently unstable
epidemiological situation. Spatio-temporal modelling
could be of significant help, both in assessing the risk of
emergence and in detecting high-risk areas, to allow
complementary active surveillance.
West Nile fever (WNF) is a worldwide viral zoonotic
infection caused by a mosquito-borne Flavivirus of the
Flaviviridae family. Recently, WNF has become a major
veterinary public health concern. Horses are particularly
sensitive to WNF, with approximately 10% of infected
animals presenting with neurological disorders, as
compared to 1% of humans (16). Thus, improving the
detection of WNF in equids is highly pertinent to public
health.
A retrospective study was recently performed, describing
risk and protection factors for the development of clinical
WNF in equids; comparing clinical presentation in three
European countries: France, Italy and Hungary; and
creating CARTs to facilitate clinical diagnosis (by making
clinical impressions objective) and thus improve passive
surveillance of this disease in Europe (18). For the
comparative study, three previously published data sets
were used, which included raw clinical data from:
– 14 confirmed Italian cases from 1998 (2)
– 39 confirmed French cases from 2004 (14)
– 20 confirmed Hungarian cases from 2007 to 2008 (13).
For the retrospective study, equine data were collected
from the French epizootic in 2004 (39 cases and
61 controls). In addition, data from suspected but nonconfirmed cases from 2008 were also included (39 other
controls) to investigate the possible differences in the
recruitment of suspected cases in a non-epizootic period.
The French data were also analysed with CART. The first
analyses were performed on all the French data available
(2004 and 2008; n = 139). A CART analysis was further
performed on data from the 2004 epizootic exclusively
(n = 100).
Early detection of bovine
spongiform encephalopathy
As a result of the favourable BSE epidemiological situation
of most Member States in the European Union (EU), a
reduction of control measures was recently suggested, by
reducing testing procedures (35). However, in such a
context, reporting suspected cases of clinically infected
cattle is the most common method for detecting sporadic
cases of BSE. Improving clinical diagnosis and decisionmaking remains crucial.
Comparisons were made between the clinical patterns
(consisting of 25 signs) of all 30 BSE cases confirmed in
Belgium before October 2002, and of 272 suspected cases
that were subsequently determined to be negative, through
histological and immunohistochemical testing, and
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Table I
Power of the different clinical signs splitters obtained after classification and regression tree analysis for West Nile fever (maximum
score = 100) (18)
CART I
Predictor variable
Score
CART II
Predictor variable
Score
Paresis
100.00
Hyperaesthesia
100.00
Weakness
81.37
Recumbency
93.60
Recumbency
75.45
Anorexia
81.41
Hyperthermia
63.59
Prostration
58.88
Paralysis
61.61
Cranial nerve deficit
57.87
Prostration
60.27
Hyperthermia
52.13
Cranial nerve deficit
46.13
Trembling, tetany, fasciculations
51.05
Trembling, tetany, fasciculations
42.15
Paralysis
49.65
Change in behaviour
40.81
Paresis
39.14
Anorexia
34.64
Change in behaviour
32.60
Hyperaesthesia
34.27
Weakness
25.80
Ataxia
13.05
Ataxia
17.76
CART I is the classification and regression tree analysis that takes into account all the suspected French cases from 2004 and 2008
(n = 139; sensitivity of tree = 82.1%; specificity of tree = 78.0%)
CART II is the classification and regression tree analysis that takes into account suspected French cases from the 2004 epizootic exclusively
(n = 100; sensitivity of tree = 74.4%; specificity of tree = 88.5%)
scrapie-associated fibres (24). Some seasonality was
observed in reporting suspected cases, with more cases
being reported during winter, when the animals were kept
indoors. The median duration of illness was 30 days. The
ten most relevant signs of BSE, in order of importance,
were:
– kicking in the milking parlour
– hypersensitivity to touch and/or sound
– head shyness
– a panic-stricken response
– reluctance to enter the milking parlour
– abnormal ear movements or carriage
– increased alertness behaviour
– reduced milk yield
– teeth grinding
– temperament change.
Ataxia did not appear to be a specific sign of BSE. A CART
was constructed, using the following four features:
– age of the animal
– year of birth
– number of relevant BSE signs noted
– number of clinical signs, typical of listeriosis, noted.
The model presented 100% sensitivity and 85% specificity
(Fig. 1). The originality of this approach lies in the fact
that, first, it offers an explorative and interactive tool and,
secondly, the results and conclusions arrived at are
independent of BSE prevalence, through the use of odds
ratios. The second feature is especially appealing for rare
events. A similar decision tree, allowing ‘highly suspect
BSE cases’ to be distinguished from all other suspected BSE
cases, could be applied in other countries, with or without
the use of rapid tests. The continued addition of clinical
data would permit further improvement to the model tree,
even if the clinical BSE pattern changed through time. The
same methodology can also be applied to other diseases,
e.g. scrapie.
Modelling
a multifactorial disease
to improve understanding
Classification and regression tree analysis is also useful
for modelling multifactorial diseases, such as colony
collapse disorder (CCD) in the United States. This is a
syndrome whose defining trait is the rapid loss of adult
worker honey bees, Apis mellifera L. (31). It is thought to
be responsible for overwintering losses experienced by
beekeepers in the United States since the winter of 2006 to
2007 (33). Using the same data set developed to perform a
monofactorial analysis (31), the authors conducted a CART
analysis in an attempt to better understand the relative
importance of and interrelations among different risk
variables in CCD. Fifty-five exploratory variables were
used to construct two CART models. One model included
the cost of misclassifying a CCD-diagnosed colony as a
non-CCD colony, and one model did not. The model tree
that permitted the misclassification presented a sensitivity
of 85% and a specificity of 74% (Fig. 2).
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BSE: bovine spongiform encephalopathy
LIS: listeriosis
Score: number of clinical signs that are present
Fig. 1
Classification and regression tree modelling for clinically suspected bovine spongiform encephalopathy cases in Belgium (24)
Although factors measuring colony stress (e.g. adult bee
physiological measures, such as fluctuating asymmetry or
mass of head) were important discriminating values, six of
the 19 variables studied that showed the greatest
discriminatory value were pesticide levels in different hive
matrices. Notably, levels of coumaphos (a varroa miticide
commonly used by beekeepers) in brood presented the
highest discriminatory value and were at their highest in
healthy bee colonies. The CART analysis provided
evidence that CCD is probably the result of several factors
acting in concert, making afflicted colonies more
susceptible to disease. This analysis highlighted several
areas requiring further attention, including the effect of
sub-lethal pesticide exposure on pathogen prevalence and
the role of variability of bee tolerance to pesticides in
colony survivorship. In this example, CART modelling
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CCD: colony collapse disorder
Fluctuating asymmetry: random differences in the shape or size of a bilaterally
symmetrical character, which can be an indicator of individual fitness because organisms exposed
to stress during early development show less symmetry than unstressed organisms (31)
ppb: parts per billion
Fig. 2
Classification and regression tree of the risk factors for colony collapse disorder colonies
There is a cost of 2 points for misclassifying a colony diagnosed with colony collapse disorder (CCD) as a non-CCD colony
The sensitivity and the specificity of the tree are 85% and 74% respectively (32)
permitted a better understanding of an animal health
disorder, allowed the evaluation of a control strategy and
suggested a new area deserving further investigation.
Modelling the compliance
of skin test procedures
in the surveillance
of bovine tuberculosis
and monitoring the effect
of an awareness campaign
Bovine tuberculosis remains of great concern in the world,
even in countries where eradication programmes have
been implemented. An original and useful methodology to
model the comparison of skin test practices in different
regions/countries was performed, using an anonymous
postal questionnaire sent to veterinary bovine practitioners
(10). The participation of the veterinarians was voluntary
and the representativeness was statistically tested,
permitting inferences to be drawn about the whole
veterinary population. International experts in bTB were
asked to fill in the questionnaire and specify the standard
(ideal), acceptable and unacceptable answers. A scoring
scale was then constructed. For each question, a score of
zero was recorded for the standard answer, a score of one
for an acceptable answer, and a score of two for an
unacceptable answer. Furthermore, these experts were
asked to weight the questionnaire’s items, according to
their possible influence on the risk of not detecting reactors
(Table II).
The performance criteria (n = 30) were classified into five
categories:
– materials
– injection procedure
– measurement of the response
– particular aspects that applied when animals were
purchased (e.g. mandatory tests on purchase in several
countries)
– other epidemiological aspects (e.g. skin testing of
animals suffering from chronic pneumonia, which was
resistant to classical treatment).
A global score was calculated for each participating
veterinarian. The situation between the two regions of a
country was compared before and after weighting the
scores, using two scenarios in each case: with imputation
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Table II
Single intradermal tuberculin test scoring table developed through expert opinion (n = 5) and total of points obtained for each
criterion (n = 11 experts) (12)
0 (Standard)
Scores
1 (Acceptable)
1. Tuberculin conservation methods (in general)
Out of the light, 3°C to 8°C
–
Other answers
70
2. Tuberculin conservation in vehicle
Icebox 4°C
–
Other answers
25
3. Mean tuberculin conservation delay in the vehicle
before use
1 day
3 to 5 days
>5 days
47
4. Percentage of use of tuberculin doses
90% to 100%
80% to 89%
<80%
12
5. Tool of injection
Manual syringe
Dermojet, automatic syringe
–
21
6. Use of a syringe previously filled with a tuberculin carpule
No
Yes
–
17
7. Use of a dermojet previously filled with a tuberculin solution No
Yes
–
17
8. Cleansing/disinfection of SIT material
Cleansing and disinfection
Cleansing or disinfection
Neither cleansing
nor disinfection
28
9. Frequency of cleansing/disinfection of SIT material
After each herd
Once a week
Less than once a week
25
10. Frequency of needle replacement (syringe)
After each herd; if broken
Once a week
Others
20
11. Frequency of dermojet revision
Yearly
If defective
Others
22
12. Use of avian tuberculin
Never
Occasionally*
Often
36
13. Site of injection
Neck
–
Caudal fold, other
51
Questionnaire items
2 (Not acceptable)
Points
A. Material
B. Injection
14. Shaving the site of injection
–
Yes
No
22
15. Clipping the site of injection
Yes
No
–
24
16. Use of scissors to clip the hair at the site of injection
Yes
No
–
44
17. Checking for the absence of swelling or lesion
before injection
Yes
–
No
45
18. Evaluation of the skin fold before injection
Spring cutimeter
or slide caliper
Palpation or
visual observation
–
48
19. Post-injection verification (formation of a pea-like
swollen area)
Yes
–
No
75
20. Type of reading of the response
Quantitative and qualitative
Qualitative; palpation
Visual observation
94
21. Mean delay of reading the response
72 h
–
–
58
C. Reading the response
22. Isolation of a test reactor and/or suspect
Yes
–
No
23
23. Delay in warning the Veterinary Authority
Immediately
12 h to 24 h
>24 h
33
24. Systematic checking of the animal’s identification
when skin-tested at purchase
Yes
–
No
42
25. Isolation at purchase, until reading of the response
Yes
–
No
19
26. Systematic SIT at purchase
Yes
–
No
53
27. Repetition of SIT if test suspect at purchase
Yes
No
–
42
28. Minimum age of calves for carrying out skin test
6 weeks
<6 weeks
>6 weeks
23
29. SIT if steroidal anti-inflammatory treatment
No***
–
Yes
42
30. SIT if chronic pneumonia (resistant to classical treatment)
Yes
–
No
37
D. SIT at purchase
**
E. Others
SIT: Single intradermal tuberculin test
* A veterinarian might occasionally use avian tuberculin at a Veterinary Authority’s request, based on the environmental epidemiological context
** If sent to the abattoir
*** The veterinarian is not always advised by the farmer that an animal has been treated
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(each missing value was replaced by a score of two
corresponding to the worst-case scenario, assuming that
the absence of an answer meant that the veterinarian was
masking an unacceptable answer) and without imputation
for the missing values. In the first scenario, the comparison
between the distributions of global scores was assessed by
a negative binomial regression (because of extra-binomial
variability). In the second scenario, the average scores were
assessed by means of a bootstrapped quantile regression
distribution, an iterative method allowing the estimation of
the parameters of interest on the basis of sampling with
remittance (a handing-over sampling).
In this study, veterinary practitioners participated at a rate
of 20%, which can be considered acceptable (5). A
significant correlation was found between the number of
answers and the number of veterinarians per province
(Pearson’s correlation coefficient), so the participation rate
was considered representative for the different provinces of
the country. Missing data were homogeneously and
proportionally split between the two regions. The
respondent’s region was selected as the comparative factor
in this assessment.
Before weighting the scores, no significant difference was
observed, whichever scenario or category of items was
used. After weighting the scores, significant differences
were observed between the two regions for three criteria:
– materials
– reading the response
– other epidemiological aspects.
The weighting of scores allowed accurate identification of
the differences between regions and should be promoted.
It seems necessary to harmonise tuberculin testing
practices at the country level. No veterinarian returned a
null score. The production of a new veterinary manual
summarising recommendations for ‘good skin test
practices’ should be suggested to animal health authorities.
The study could also be repeated in the same country in
the future, to check that practices have improved, as well
as in other countries (a multi-centric study) to evaluate the
suitability of the proposed assessment methodology and
thus improve confidence in animal trade worldwide.
In addition, the same methodology was recently used to
evaluate a campaign to alert veterinarians to the
importance of correctly performing the tuberculosis skin
test (11). The participants in the study were asked to
complete the questionnaire in duplicate; one questionnaire
on their practices before the campaign, and the other on
their practices after the campaign. A comparison of both
situations was carried out (pre- and post-awareness
campaign), as well as a comparison with another country,
arbitrarily selected as a reference for the methodology. The
representative participation of veterinarians involved in the
study was statistically tested, allowing inferences to be
Rev. sci. tech. Off. int. Epiz., 30 (2)
made about the whole veterinary population. A significant
difference was noticed between the mean global scores
before and after the awareness campaign.
These results show the usefulness of an appropriate
awareness campaign on tuberculosis skin testing aimed at
veterinarians. The study also highlights a new, structured
auto-assessment approach to veterinary practices.
Moreover, both studies (evaluating compliance with best
practices for tuberculosis skin testing and evaluating the
results of an awareness campaign) could easily be adapted
to other diseases and control strategies, including meat
inspection (research is continuing in this field). This
example demonstrates the value of continuous evaluation
of control strategies by modelling. The approach allows the
identification and location of non-compliant procedures,
which can then be targeted for correction. It also highlights
the role that can be played by an independent body, such
as a university (e.g. the Faculty of Veterinary Medicine), in
performing such studies on a statistical basis, in support of
the appropriate Veterinary Authority.
Mechanistic modelling of age
distribution at the moment of
disease detection to monitor
trends for diseases that have
a long incubation period
Bovine spongiform encephalopathy is a zoonotic disease
that caused the emergence of new variant Creuzfeldt-Jakob
disease in the mid-1990s (36).
It is very important to analyse the trend of the BSE
epidemic. A simple mechanistic model was constructed in
Belgium to simulate this trend. One hundred and eighteen
cases of BSE were notified in Belgium before 1 January
2004. The authors analysed trends in the age of the animal
at the time of detection and attempted to use this
parameter as a robust predictor for the current status of the
BSE epidemic in the country (25). The following indicator
variables were considered:
– date of birth
– breed
– date and mode of detection
– number and age of animals slaughtered and rendered
each month.
Age at detection in terms of date of birth was a very poor
epidemiological indicator. However, age at detection in
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terms of detection was a very good epidemiological
indicator. Typically, the average age at the time of detection
increases at the start of an epidemic, tends to stabilise at its
height and starts to increase again when the epidemic
curve declines. The difference between the start and the
end of an epidemic lies in the distribution of the ages at
detection: a characteristic of the end of an epidemic is a
reduction in the number of younger animals. The authors
concluded that the increasing age of BSE cases at the time
of detection was due to the depletion of cases (no new
infections; thus, detected cases were becoming older) and
that this was a reliable indicator of the onset of a decrease
in the epidemic curve in Belgium. With this simulation, the
authors showed how the age distribution at the time of
detection closely follows the epidemic curve and used
British data to illustrate this point (Fig. 3). The age
distribution at the time of detection may be used to
monitor the trend in situations where absolute numbers of
cases cannot be determined accurately.
More recently, a comparable exercise was performed
at the European level using age-period-cohort and
a) Distribution of the incubation time of BSE cases (35)
reproduction ratio modelling applied to 2001 to
2007 surveillance data (4). This second effort confirmed
the first study and a strong decline in BSE
risk was observed for all countries that applied control
measures during the 1990s, starting at different points in
various countries. Results were compared with the
type and date of BSE control measures implemented
between 1990 and 2001 in each country. These results
showed that a ban on feeding meat-and-bone meal
(MBM) to cattle alone was not sufficient to eliminate BSE.
The epidemic began to fade out shortly after
complementary measures (e.g. the removal of specified risk
material and controls on cross-contamination and
sterilisation of MBM) were implemented, targeted at
controlling the risk linked to MBM. Given the long
incubation period of BSE, it is still too early to estimate the
additional effect of banning the feeding of animal protein
to all farm animals, which was introduced in 2001. These
results provide new insights into risk assessment of BSE for
cattle and humans, which will be especially useful in the
context of the possible relaxing of BSE surveillance and
control measures (4).
b) Distribution of infections based on three scenarios of the
epidemic curve (100,000 infections during a period of 20 years)
0.25
Probability
0.20
0.15
0.10
0.05
0.0
1
2
3
4
5
6
7
8
9
Incubation period (years)
10
11
12
13
c) Mean age at detection of BSE cases resulting from (a) and (b)
(details of calculations are available in the appendix of ref. 26)
Scenario 1: complete epidemic curve (beginning, peak and tailing off)
Scenario 2: tailing off of the epidemic curve
Scenario 3: beginning of the epidemic curve
Fig. 3
Modelling trends of the mean age at detection of bovine spongiform encephalopathy (BSE) cases as a function of three scenarios
of the epidemic curve (25)
564
Statistical nationwide
modelling to analyse the
spatio-temporal dynamics
of bovine tuberculosis, in
search of potential risk factors
that could be used to better
target surveillance measures
Bovine TB is one of the seven neglected zoonotic
diseases worldwide (37) and its epidemiological situation
remains worrying in many countries (38).
Whilst the dynamics of bTB transmission are not yet fully
understood, risk factors for bTB – including a variety of
parameters, such as: wildlife, contacts between animals,
animal movements, animal density, etc. – have
been identified around the world (reviewed in 9). The
failure to eradicate bTB due to Mycobacterium bovis in
cattle could be explained by such factors as
inadequate control measures, agro-environmental
conditions, wildlife reservoirs and the movement of
infected animals (6). In countries that are officially free
from bTB, including several Member States of the EU, a
reduction in control measures, by no longer testing at the
purchase of the animal and reducing herd testing, was
recently suggested, partly because bTB control
programmes are economically stringent. Nevertheless, to
date, no nationwide study has investigated the potential
risk factors for bTB based on the molecular
characterisation of strains. A database including all M. bovis
strains isolated between 1995 and 2006 in Belgium was
compiled thanks to three molecular techniques applied in
parallel:
– spoligotyping
– restriction fragment length polymorphism
– mycobacterial interspersed repetitive unit-variablenumber tandem-repeat (10).
This database was first used to analyse the bTB spatiotemporal dynamics in Belgium during the period of
interest. After a complete revision of the literature on bTB
risk factors, the potential risk factors to be investigated in
Belgium were selected. A total of 49 parameters, named
predictors derived from several databases, were included
in the statistical model and considered as potential risk
factors for bTB (Box 1). All predictors were compiled into
a unique database. For the analysis of bTB dynamics, the
temporal unit was a year, and the spatial unit was defined
as follows: the territory was divided into 5 km × 5 km cells,
identified by their X and Y Lambert coordinates.
Rev. sci. tech. Off. int. Epiz., 30 (2)
Box 1
List of the 49 predictors for bovine tuberculosis (bTB)
included in the model (8, 10)
History of bTB (persistence of bTB)
Logarithm of the distance to the centre of the infected pixel
Density of cattle per pixel in 2000
Density of cattle per pixel in 2001
Density of cattle per pixel in 2002
Density of cattle per pixel in 2003
Density of cattle per pixel in 2004
Density of cattle per pixel in 2005
Density of cattle per pixel in 2006
Total number of movements into a pixel during the previous year
Total number of movements from an infected area during the previous
year
Proportion of movements that originated from infected pixels during the
previous year
Total number of movements into a pixel during the current year
Total number of movements from an infected area during the current year
Proportion of movements that originated from infected pixels during the
current year
Density of mouflons in a pixel (mean of all years)
Density of fallow deer in a pixel (mean of all years)
Density of red deer in a pixel (mean of all years)
Density of roe deer in a pixel (mean of all years)
Density of wild boar in a pixel (mean of all years)
Surface of the pixel covered by crops
Surface of the pixel covered by forests
Surface of the pixel covered by other types of vegetation
Surface of the pixel covered by pastures
Surface of the pixel covered by urban zones
Surface of the pixel covered by wet zone areas
Length of forest-pasture edge (m)
Mean middle-infra-red temperature (°C)
Middle-infra-red temperature annual amplitude (°C)
Middle-infra-red temperature bi-annual amplitude (°C)
Minimum middle-infra-red temperature (°C)
Maximum middle-infra-red temperature (°C)
Middle-infra-red temperature phase of annual cycle (months)
Middle-infra-red temperature phase of bi-annual cycle (months)
Mean land surface temperature (°C)
Land surface temperature annual amplitude (°C)
Land surface temperature bi-annual amplitude (°C)
Minimum land surface temperature (°C)
Maximum land surface temperature (°C)
Land surface temperature phase of annual cycle (months)
Land surface temperature phase of bi-annual cycle (months)
Mean normalised difference vegetation index
Normalised difference vegetation index annual amplitude
Normalised difference vegetation index bi-annual amplitude
Minimum normalised difference vegetation index
Maximum normalised difference vegetation index
Normalised difference vegetation index phase of annual cycle
Normalised difference vegetation index phase of bi-annual cycle
Altitude (m)
565
Rev. sci. tech. Off. int. Epiz., 30 (2)
The following predictors were then re-sampled at the 5 km
resolution:
– distance to the centre of an infected cell (short-distance
spread)
predictors were added to the model, using a standard-entry
stepwise procedure. The model was restricted to variables
with the highest predictive power, and only those
presenting more than 1% of log-likelihood change after
removal were retained.
– wild population densities
– land use
– land cover
– climatic data (remotely sensed data for several
bioclimatic indicators) (8)
– cattle movements and density.
Land-cover data included in the model were expressed as
the percentage of a cell occupied by the different types of
vegetation (equivalent to ha/km2). Bovine TB persistence
was included in the model as follows: a score was allocated
to each cell, for each year of the period of interest,
according to the presence/absence of bTB outbreaks. No
bTB outbreak corresponded to a score of zero, while a
score of one meant the presence of bTB. When the
presence of bTB was confirmed, the M. bovis strain was
specified. Two records were registered for each movement
of cattle: the location that the animal had moved off and
the location it had moved onto. The treatment of cattle
movement data involved pairing of ‘on’ and ‘off’
movements, which provided three categories of variables
for each cell:
– the total number of inward movements
– the total number of movements from infected areas
– the proportion of movements from infected areas.
First to be tested was the influence of movements that
occurred the year before a bTB outbreak. The impact of
movements taking place during the year of occurrence of a
bTB outbreak in a given cell was also investigated. The
whole process led to the construction of a unique database,
covering 1995 to 2006 and including all information on
the 49 predictors, per cell and per year.
A step-by-step multiple logistic regression was applied to
the model to analyse the relationship between the
occurrence of bTB and the 49 predictors. Gilbert and
collaborators originally built up this regression model to
study the impact of animal movements on bTB
transmission in Great Britain (6). The model was further
adapted to Belgium, and the results of molecular typing
were also included. Wald’s statistics quantified the
contribution of each predictor to the model.
The following predictors were first entered in the model:
disease persistence (i.e. antecedent of bTB) and shortdistance spread. A positive relationship between these two
predictors and the presence of bTB was highlighted. Other
The best predictors were systematically tested with other
families of predictors (wildlife, climatic, land cover), in a
descending approach. At each step of the process, the
predictor with the lowest z value was discarded. The final
step consisted in testing together all predictors showing a
positive or negative relationship with the presence of bTB.
In fact, the 49 predictors could not initially be tested
together at the same time because some of them were
correlated. A predictor was considered as being a
significant risk factor when it presented a positive
association with P < 0.05. Two statistical approaches were
used. The first approach included all M. bovis strains
identified in Belgium between 1995 and 2006, while the
second approach focused on strains belonging to the
predominant spoligotype SB0162 (34). R software was
used to carry out the whole statistical process (19).
In summary, several risk factors for bTB in Belgium were
identified for the first time, thanks to this study. The first
approach identified a bTB antecedent in the herd
(P < 0.001), distance from an outbreak (neighbouring
effect) (P < 0.001) and density of cattle (P < 0.001) as
significant risk factors. The second approach highlighted
the proportion of movements from an infected area during
the current year as a main risk factor (P = 0.007). It is thus
essential that vigilance over the control of movements and
skin testing at purchase is not relaxed. Wildlife do not
seem to represent a risk in Belgium to date, but
epidemiological surveillance is crucial in light of the
situation in neighbouring countries, such as France and
the United Kingdom. Studies focusing more specifically on
the role of environment and climate in the persistence of
M. bovis should also be undertaken. In addition, the results
of this study suggest a difference in behaviour for the
spoligotype prevailing in Belgium (SB0162), underlining
the importance of molecular epidemiology to investigate
potential strain-related differences of virulence. Such an
evidence-based approach is easily transposed to another
disease and allows the evaluation and adaptation of control
strategies at a nationwide level.
Conclusion
Since animal diseases place a significant burden on animal
health, human health and global economies, national
health authorities and international organisations should
give priority to specific diseases and select the appropriate
control strategies to eventually eradicate them. Once a
control strategy has been selected and implemented, it is
566
Rev. sci. tech. Off. int. Epiz., 30 (2)
important to evaluate its efficacy and adapt the strategy
accordingly, where necessary. In this paper, the authors
have detailed several examples to illustrate the usefulness
of modelling in evaluating a control strategy. Modelling can
be performed according to evidence-based medicine (30),
preferably by a scientific and independent body (e.g. a
university Faculty of Veterinary Medicine in support of the
appropriate animal health authority), especially when an
anonymous questionnaire is necessary to improve
knowledge of the situation in the field.
Today, the emergence and persistence of numerous
infectious diseases give rise to practical and theoretical
questions, which cannot be approached without modelling
(20). When such modelling is completed, after
modification of some parameter(s), it is also possible to
estimate the outcome of various strategies, allowing
informed decision-making. Extensive knowledge of the
pathogenesis, epidemiology and factors influencing the
probability of infection for a specific disease (e.g. risk
factors) are crucial to develop an accurate model to
evaluate the control strategy or to identify sub-populations
at risk and conduct a targeted survey. An interdisciplinary
rather than a multidisciplinary approach is required, which
makes the fewest assumptions possible.
permit the evaluation and adaptation of animal disease
control strategies (23). Modelling also highlights a new,
structured auto-assessment approach to veterinary
practices (e.g. 11). In addition to the assessment of
effective control strategies, consideration should be given
to developing an approach to disease modelling that
integrates both economic and social aspects (acceptability,
feasibility, stability) (22).
Acknowledgements
There were no potential conflicts of interest for any of the
authors. The tuberculosis studies were funded by the
Federal Public Service of Health, Food Chain Safety and
Environment, Brussels, Belgium (contract RF 6182). The
West Nile fever and bovine spongiform encephalopathy
studies were funded by the University of Liege, Belgium
(contract D-08/02), and the Federal Agency for the Safety
of the Food Chain, Brussels, Belgium, respectively. The
authors would also like to thank the anonymous bovine
and equine veterinary practitioners for their participation.
Sincere thanks go to all the experts who took part in the
development of the scoring system and the ranking of the
tuberculosis parameters included in the questionnaire.
While some reluctance remains among veterinarians and
physicians, modelling is one of the key investments in
Veterinary Services for the future. Veterinary education
should include the development of such modelling to
L’utilisation des modèles pour évaluer et modifier
les stratégies de lutte contre les maladies animales
C. Saegerman, S.R. Porter & M.-F. Humblet
Résumé
Chez l’homme comme chez l’animal, la présence d’une maladie est souvent
associée à des signes cliniques d’affaiblissement, à des troubles des fonctions
physiologiques ou à des pertes de production, avec des conséquences socioéconomiques considérables. La sélection de stratégies de lutte appropriées et
l’éradication de certaines maladies particulières sont donc une priorité tant pour
les autorités sanitaires nationales que pour les organisations internationales.
Dès lors qu’une stratégie de lutte a été sélectionnée et mise en œuvre, il peut
s’avérer utile de faire appel à la modélisation pour en évaluer l’efficacité. Afin
d’illustrer cette contribution des modèles, les auteurs décrivent en détail
plusieurs exemples, dont trois portent sur la modélisation de classes et sur les
arbres de régression utilisés pour évaluer et améliorer la détection précoce de
maladies : en l’occurrence, la fièvre de West Nile chez les équidés,
l’encéphalopathie spongiforme bovine (ESB) et les maladies multifactorielles
567
Rev. sci. tech. Off. int. Epiz., 30 (2)
telles que le syndrome d’effondrement des colonies d’abeilles aux États-Unis. Ils
examinent également les modèles de régression utilisés pour évaluer les
pratiques en matière de tests cutanés et l’efficacité d’une campagne de
sensibilisation à la tuberculose bovine ; la modélisation mécaniste visant à suivre
l’état d’avancement d’une stratégie de lutte contre l’ESB ; et enfin la
modélisation statistique à l’échelle nationale visant à analyser la dynamique
spatio-temporelle de la tuberculose bovine et à identifier les facteurs de risque
susceptibles de faire l’objet de mesures de surveillance ciblées plus efficaces.
Pour une utilisation appropriée des modèles, il convient d’adopter une approche
interdisciplinaire plutôt que pluridisciplinaire et de recourir à un nombre aussi
limité que possible d’hypothèses de départ.
Mots-clés
Épidémiologie – Évaluation – Maladie animale – Modélisation – Stratégie de lutte.
Utilización de modelos para evaluar
y adaptar estrategias de control zoosanitario
C. Saegerman, S.R. Porter & M.-F. Humblet
Resumen
Las enfermedades se acompañan a menudo de signos clínicos debilitantes,
trastornos o pérdidas de productividad en los animales y/o el ser humano, hecho
que tiene graves consecuencias socioeconómicas. Ello explica la elevada
prioridad que las autoridades sanitarias nacionales y las organizaciones
internacionales otorgan a la selección de estrategias de lucha y a la
erradicación de determinadas dolencias. Al elegir y aplicar una estrategia de
control, un método eficaz para evaluar su eficacia pasa por la elaboración de
modelos. Para poner de relieve la utilidad de los modelos a la hora de evaluar
estrategias de control, los autores exponen varios ejemplos en detalle, entre
ellos tres ejemplos de modelos de árboles de clasificación y regresión para
evaluar y mejorar la detección precoz de las enfermedades: la fiebre del Nilo
Occidental en équidos, la encefalopatía espongiforme bovina (EEB) y
enfermedades multifactoriales, tal como el síndrome de desabejamiento o
colapso de las colmenas en los Estados Unidos. Además se examinan modelos
de regresión para evaluar la práctica de pruebas intradérmicas y la eficacia de
una campaña de sensibilización sobre la tuberculosis bovina (TB); modelos
mecánicos para seguir los resultados de una estrategia de lucha contra la EEB;
y modelos estadísticos nacionales para analizar la dinámica espacio-temporal
de la TB y descubrir posibles factores de riesgo que podrían utilizarse para elegir
más eficaz y selectivamente las medidas de vigilancia. La aplicación rigurosa de
modelos exige un planteamiento interdisciplinar, más que multidisciplinar, con la
asunción del menor número posible de premisas de partida.
Palabras clave
Elaboración de modelos – Enfermedad animal – Epidemiología – Estrategia de control –
Evaluación
568
Rev. sci. tech. Off. int. Epiz., 30 (2)
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