Pol, T.D., van der

Saatkamp, H.W.

English

Name not available

First conceptual considerations of economic optimisation of monitoring and surveillance systems for animal health and food safety

Wageningen Yield 

Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
 
FIRST CONCEPTUAL CONSIDERATIONS OF ECONOMIC OPTIMISATION OF 
MONITORING AND SURVEILLANCE SYSTEMS FOR ANIMAL HEALTH AND 
FOOD SAFETY 
 
T.D. van der Pol and H.W. Saatkamp, Business Economics Group, Wageningen University  
 
Introduction and background 
The mission of food safety authorities (FSAs) is to assure to the safety of food and 
animal health. A vast amount of biological agents, chemical contaminants and residues can 
adversely affect animal and human health. The socio-economic impacts of the presence of 
some hazards in food production chains are substantial. FSAs aim to minimize these impacts 
by legislation enforcement and assessing risks, as well as risk management. Their tasks centre 
on prevention, monitoring and control. This research focuses on monitoring only.  
Monitoring and surveillance systems (MOSSs)1 serve various objectives including 
early detection (e.g. of an animal disease or medical residue), detection of threshold 
violations, free status substantiation, determining hazard characteristics, providing baseline 
prevalence estimates and monitoring trend development. Many actors are involved, such as 
individual farmers, laboratories, veterinary services, product boards and authorities. FSAs 
have monitoring responsibilities for a large hazard portfolio. 
Monitoring and surveillance is costly and it is simply impossible and undesirable to 
monitor any hazard at any stage at any time. Consequently, an economic optimisation 
problem exists for FSAs. The performance of both single and portfolio MOSSs must be 
maximized under budget constraints. 
 To address this optimisation problem a PhD project was initiated. The problems are 
formulated from the perspective of the Dutch and German FSAs. The project is executed 
within the large German-Dutch project Safeguard. The aim of this paper is to describe the 
global set-up of the research and to present conceptual considerations of the optimisation 
problems.   
 
Research definitions 
 Terminology surrounding monitoring and surveillance is inconsistently used, both in 
the literature as in practice. It is not the purpose of this research to enter in semantic 
discussions; it suffices to provide some definitions as they are used in this research.  
Monitoring and surveillance are used interchangeably. Nevertheless, it is worthwhile 
to note that some authors make distinctions. According to Noordhuizen et al. (2001) 
monitoring is the process of “collecting data about animal health, disease and their 
determinants” and surveillance may be seen as “an extension of monitoring” whereby 
information is linked to thresholds. Similar remarks can be made for other terminology such 
as programme, system, component, instrument and methodology. 
  The term MOSS is arbitrarily adopted to indicate the total set of system components 
(cf. Doherr and Audigé, 2001; Martin et al., 2007). Examples of system components are e.g. 
clinical surveillance, blood sampling at a chain stage, ante-mortem inspection, post-mortem 
inspection, a sentinel network and entomological surveillance. To specify in detail what a 
component comprises the term instrument will further be used instead of system component.  
 A MOSS summarises the total set of employed system components for one or several 
hazards. The term ‘single MOSS’ is used to indicate the total set of system components 
employed for the monitoring of one specific hazard, or hazard class when used in the context 
                                                 
1
 Terminology is generally not consistently used; the term MOSS is adopted to indicate the total set of system 
components. This is explained in the research definitions’ section. 
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Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
of generic model formulation. Some monitoring activities are shared per definition; in that 
case they are separated as much as possible and treated as if only used for the particular 
hazard. ‘Portfolio MOSS’ refers to the monitoring of a specific set of hazards.  
 A generally accepted mathematical definition of risk is probabilistic variability of 
events to happen whereas uncertainty refers to unknown variability. Given the context it is 
generally more appropriate to “define uncertainty as imperfect knowledge, and risk as 
uncertain consequences” (Hardaker, 1997).  
 
Approach and scope 
 The research consists of four stages: the development of a conceptual framework, the 
formulation of single MOSS models and analysis of results, the development a tool for 
decision support and MOSS portfolio optimisation. 
 To enable model building a thorough understanding of the decision problems is 
essential. A conceptualisation should include the formulation of single MOSS classes for 
which generic performance criteria, indicators and models can be formulated. Other elements 
are stakeholder analyses and analysis of inputs. 
Generic combined epidemiological-economic single MOSS optimisation models are 
developed and tested for specific hazards. The next step is to interpret these results to come to 
decision support. After single MOSS optimisation the problem of portfolio MOSS 
optimisation will be addressed. 
The research scope is limited to single and portfolio MOSS prioritised in legal sense 
for which FSA cq. product boards have responsibilities. Various biological and chemical 
hazards are considered.  
 
Research outline 
Figure 1 shows a stepwise approach to arrive at decision support for the budget 
allocation problems. In this section the building blocks are explained in a general way to 
clarify the rationale of the research. In the next section a brief overview is given of some of 
the relevant aspects of the budget allocation problems.  
Ultimate research objective is to provide information on optimal budget allocation on 
monitoring and surveillance which is of use for FSAs. Single and portfolio MOSS are 
considered. Decision support will at least consist of: suggestions for cost-effective monitoring 
protocols, analysis of performance sensitivity for budget changes and insights in the impact of 
performance perception on optimal MOSS design and configuration. 
 To enable this models are required since for most hazards there is too large a number 
of monitoring options to intuitively solve the problem. Combined epidemiological/economic 
modelling is applied to capture features of particular hazards. Consequently, a first category 
of parameter inputs comes from the hazard itself; few examples are infectivity and recovery 
rates, potential target group parameters and expected prevalence. Also, contact structures of 
the involved chains are important to investigate how a hazard spreads throughout the 
population.  
Diverse other inputs are needed. Potential and current system components and the total 
set of available instruments must be analysed. Parameters on test performance must be 
obtained, as well as the economic aspects of the MOSS instruments. The available budget for 
the MOSS must be integrated as well as distributional defaults. The legal environment puts 
constraints on the feasible solution space. However, the models will be run with and without 
these constraints in order to be able to explore short-term possibilities as well as possibilities 
for longer-term MOSS configurations.  
 
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Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
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 The models aim at demonstrating performance of different MOSS configurations. For 
this, dependent variables are obtained springing from the raison d’être of the MOSS which 
can be summarised in performance criteria (e.g. early detection). Performance indicators can 
e.g. be the number of infected herds at the end of the high-risk period and the confidence the 
average time to detect a hazard introduction.   
 With all the general parameter inputs, choice variables and performance variables a 
model can be constructed. It is not the intention to build a ‘from scratch’ model for any 
hazard. The holistic approach implies a forward and backward arrow in Figure 1 between 
inputs, performance definition and models. Suitable generic standard model structures can be 
adopted from the literature. These models can be calibrated for specific single hazards. A 
similar approach will be developed for portfolios. 
 Once performance indicator outcomes are obtained a subjective valuation of these 
outcomes must be made taking account for subjective preferences. Several methods are 
thinkable to further reduce the number of feasible solutions. By either deducting hard utility 
assumptions or with softer decision rules capturing shared performance perceptions final cost-
effective solutions can be found. 
 
Important issues to ensure cost-effective monitoring and surveillance 
In this section a brief overview is given of some of the relevant topics important for 
determining the cost-effectiveness of a MOSS. 
 
Sampling design 
 Overall cost-effectiveness of sampling depends on technical test performance 
measures (sensitivity, specificity, predictive values) and related costs. Choosing the optimal 
Figure 1 The building blocks of the research to arrive at decision support. 
 
11
Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
sampling size and sampling frequency is important. Literature on this topic is abundant. 
Cameron and Baldock (1998) note that disease prevalence is often clustered within herds and 
show optimal herd and individual sample size to substantiate disease freedom. Wagner and 
Salman (2004) propose a formula for herd level prevalence estimates when tests are not 
perfect. 
Diverse ‘targeted’ or ‘risk-based’ sampling strategies have been proposed. Alban et al. 
(2008) e.g. find that Trichinella spp. could be monitored more efficiently when aimed at out-
door reared pigs. Tavornpanisch et al. (2006) identify that cows in higher lactation have a 
higher probability of positive testing on paratuberculosis. Schwermer et al. (2009) analyse the 
value of earlier surveys to calculate new sample sizes, based on importation risk and the 
proportion of surviving animals within the subpopulations. Target groups may also be defined 
spatially. Salmonella monitoring may e.g. be targeted at farms in a high-density area of 
slaughter-pig farms (Benschop et al., 2009). 
 
Legal environment and instruments 
In many parts of the world monitoring and surveillance is formalised in a 
comprehensive legal framework. The OIE has listed notifiable diseases (OIE, 2009a). In the 
OIE Terrestrial Manual prescribed and alternative diagnostic tests and vaccines are specified 
for some listed diseases (OIE, 2009b). The Codex Alimentarius Committee prescribes 
maximum residue limits and test performance requirements. On European level, the EFSA 
has an important role in assessing and communicating risks associated with food production 
chains. Legislation is set in EC Directives and further specified in national programmes. On 
the national level, the ministries of agriculture and health as well as product boards have 
responsibilities regarding the formulation of legislation. 
 
Performance indicators and modelling 
 With the percentiles of the number of infected herds at the end of the high-risk period 
as performance indicator Klinkenberg et al. (2005) calculate effectiveness and costs of some 
alternative surveillance programmes for CSF with a state-transition model. Similarly, Fischer 
et al. (2005) use a stochastic SLIR model to determine median time-to-detect and median 
number of infected farms for M. Bovis outbreaks under different surveillance strategies. Graat 
et al. (2001) use the basic reproduction rate between herds (Rh) as performance indicator to 
determine minimum surveillance requirements for BHV1. Martin et al. (2007) propose a 
scenario-tree approach with the inclusion of different data sources to estimate total system 
sensitivity to substantiate disease freedom.  
The above examples show different modelling approaches and performance indicators 
to determine the effectiveness of a MOSS. Some of the authors also explicitly include 
economic aspects. Modelling approaches and performance indicators vary not only within 
hazard classes. Coffey et al. (2009) e.g. assess human exposure risks to mycotoxins in dairy 
milk with a simulation model.  
 
Decision rules 
 The model outputs are probability distributions for multiple PIs. Trade-offs may 
become visible between PI outcomes. Ideally, MOSS alternatives could be ranked with full 
information about utility functions of decision makers. Since this will generally not be the 
case, decision rules are needed which specify the generally shared perceptions about the value 
of the PI outcomes.   
Portfolio investment under uncertainty has been subject of much research in finance. 
Markowitz (1952) has introduced mean-variance analysis. The theory on stochastic 
dominance and asymmetric risk measures has evolved (e.g. Bawa, 1975).  
12
Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
The budget allocation problem of food-safety authorities is similar in nature. Budget is 
allocated on the basis of expectations about the benefits of monitoring activities. Also, food 
safety authorities show risk-averse attitudes. This is expressed in the amount of budget spent 
to limit risks: a risk premium is paid. An example is the observed unbalance in surveillance 
costs for BSE compared with the additional risks when some of the measures would be 
relaxed (cf. Adkin et al., 2009; Benedictus et al., 2009).  
 
Conclusion and discussion 
 The cost-effective optimisation of MOSSs is complex. A stepwise approach is 
considered to determine cost-effective MOSSs; both the problem of single and portfolio 
MOSS optimisation are investigated.  
 The holistic approach comprises hazard categorisation, performance definition and 
generic model formulation. It is presumed that it is possible to formulate a limited number of 
hazard categories which can be made specific.  
 Much attention will be paid to the systematic interpretation of model outcomes.   
 
Literature 
 
Adkins, A. et al. (2009) Estimating the impact on the food chain of changing bovine spongiform encephalopathy 
(BSE)  control measures: The BSE Control Model. Preventive Veterinary Medicine, in press 
Alban, L. et al. (2008) Towards a risk-based surveillance for Trichinella spp. in Danish pig production. 
Preventive Veterinary Medicine 87, pp. 340-357 
Bawa, V.S. (1975) Safety-first, stochastic dominance, and optimal portfolio choice. The Journal of Financial 
and Quantitative Analysis 13 (2) 1978, pp. 255-271 
Benedictus, A, Hogeveen, H. and Berends, B.R. (2009) The price of the precautionary principle: cost-
effectiveness of BSE intervention strategies in the Netherlands. Preventive Veterinary Medicine 89, pp.212-222 
Benschop, J. et al. (2009) Informing surveillance programmes by investigating spatial dependency of subclinical 
Salmonella infection. Epidemiology and Infection 137 (9), pp. 1348-1359 
Cameron, A.R. and Baldock, C. (1998) Two-stage sampling in surveys to substantiate freedom of disease. 
Preventive Veterinary Medicine 34, pp. 19-30 
Coffey, R.; Cummins, E. and Ward, S. (2009) Exposure assessment of mycotoxins in dairy milk. Food control, 
pp. 239-249  
Doherr, M.G. and Audigé, L. (2001) Monitoring and surveillance for rare health-related events: a review from 
the veterinary perspective. Philosophical Transactions- Royal Society 356, pp.1097-1106  
Fischer et al. (2005) Evaluation of surveillance strategies for bovine tuberculosis (Mycobacterium m bovis) 
using an individual based epidemiological model. Preventive Veterinary Medicine 67, pp. 283-301 
Graat., E.A.M. et al. (2001) Modelling the effect of surveillance programmes on spread of bovine herpesvirus 1 
between certified cattle herds. Veterinary Microbiology 79, pp. 193-208 
Hardaker, J.B.; Huirne, R.B.M. and Anderson, J.R. (1997) Coping with risk in agriculture, Wallingford: CAB 
International, 274p. 
Klinkenberg, D. et al. (2005) The effectiveness of classical swine fever surveillance programmes in the 
Netherlands. Preventive Veterinary Medicine 67, pp. 19-37 
Martin, P.A.J.; Cameron, A.R.; Greiner, M. (2007) Demonstrating freedom from disease using multiple complex 
data sources 1: A new methodology based on scenario trees. Preventive Veterinary Medicine 79, pp. 71-79 
OIE, 2009a Terrestrial Animal Health Code, http://www.oie.int/eng/normes/mcode/ 
OIE, 2009b Manual of Diagnostic Tests and Vaccines for Terrestrial Animals, 
http://www.oie.int/fr/normes/mmanual/A_index.htm 
Markowitz, H. (1952) Portfolio selection. The Journal of Finance 7 (1), pp.77-91 
Noordhuizen, J.P.T.M. et al. (2001) Application of quantitative methods in veterinary epidemiology, Lucknow: 
international book distributing co., 429p. 
Schwermer, S.; Reding, I.; Hadorn, D.C. (2009) Risk-based sample size calculation for consecutive surveys to 
document freefom from animal diseases. Preventive Veterinary Medicine 92, pp. 366-372 
Tavornpanich, S. et al. (2006) Evaluation of cost-effectiveness of targeted sampling methods for detection of 
Mycobacterium avium subsp paratuberculosis infection in dairy herds. American Journal of Veterinary Research 
67 (5), pp. 821-828 
Wagner, B. and Salman, M.D. (2004) Strategies for two-stage sampling designs for estimating herd-level 
prevalence. Preventive Veterinary Medicine 66, pp. 1-17 
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