Conceptualising the technical relationship of animal disease surveillance to intervention and mitigation as a basis for economic analysis

Evaluating the Integration of One Health in Surveillance Systems for Antimicrobial Use and Resistance: A Conceptual Framework

It is now widely acknowledged that surveillance of antimicrobial resistance (AMR) must adopt a "One Health" (OH) approach to successfully address the significant threats this global public health issue poses to humans, animals, and the environment. While many protocols exist for the evaluation of surveillance, the specific aspect of the integration of a OH approach into surveillance systems for AMR and antimicrobial Use (AMU), suffers from a lack of common and accepted guidelines and metrics for its monitoring and evaluation functions. This article presents a conceptual framework to evaluate the integration of OH in surveillance systems for AMR and AMU, named the Integrated Surveillance System Evaluation framework (ISSE framework). The ISSE framework aims to assist stakeholders and researchers who design an overall evaluation plan to select the relevant evaluation questions and tools. The framework was developed in partnership with the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). It consists of five evaluation components, which consider the capacity of the system to: [1] integrate a OH approach, [2] produce OH information and expertise, [3] generate actionable knowledge, [4] influence decision-making, and [5] positively impact outcomes. For each component, a set of evaluation questions is defined, and links to other available evaluation tools are shown. The ISSE framework helps evaluators to systematically assess the different OH aspects of a surveillance system, to gain comprehensive information on the performance and value of these integrated efforts, and to use the evaluation results to refine and improve the surveillance of AMR and AMU globally.

Seroprevalence of Toxoplasma gondii infection in sows and finishers from conventional and organic herds in Denmark: Implications for potential future serological surveillance

Pigs are one of several host species for Toxoplasma gondii parasites, and consumption of infected pork may lead to toxoplasmosis in humans. We estimated seroprevalence in sows and finishers from conventional and organic herds in Denmark and discussed the strategies for reducing the risk from pork. We collected 447 blood samples from 59 herds, and additional meat-juice samples from 212 of the same pigs. Using a T. gondii IgG commercial ELISA test, we found 2% (95% CI = 0.4%-5%) apparent seroprevalence of T. gondii in conventional finishers, 11% (95% CI = 6%-17%) in organic finishers, 19% (95% CI = 11%-30%) in conventional sows and 60% (95% CI = 47%-72%) in organic sows. The odds of an animal testing positive for T. gondii was 16 times higher (95% CI = 4.6-74.3) in organic compared to conventional herds. The odds were 22 times higher (95% CI = 6.5-88.3) if the animal was a sow compared to a finisher. Meat-juice ELISA values were significantly correlated with plasma results (P < 0.001), but on average 64% of the blood-plasma ELISA values. Lowering the recommended cut-off from 20 to 13 percent positive values of the positive control for meat-juice ELISA, resulted in the meat-juice ELISA identifying 93% of the plasma positives as positive and 99% of the plasma negatives as negative. The time taken to detect one or more infected pigs from a T. gondii positive herd at slaughter was estimated using abattoir data on pigs (17,195,996) and batches (165,569) delivered to Danish abattoirs in 2018. The time to detection was affected by the seroprevalence, frequency at which the pigs were delivered, the number of samples tested per batch delivery and the batch sizes. Time to detection was long in conventional finisher herds due to low prevalence, and in sow herds because of intermittent delivery of a low number of sows. In organic finisher herds, time to detection was short due to medium prevalence and frequent delivery of a high number of finishers. Conventional finisher herds may be classified as low-risk, organic finisher herds as medium-risk, and conventional and organic sow herds as high-risk herds. Risk-mitigation strategies at processing plants (freezing or curing) or at the consumer level (heat treatment) for meat originating from high-risk herds, surveillance of medium-risk herds, and auditing for controlled housing (high biosecurity) in low-risk herds may be cost-effective alternatives to serological surveillance of all Danish pig herds.

Quantifying the hidden costs of imperfect detection for early detection surveillance

The global spread of pathogens poses an increasing threat to health, ecosystems and agriculture worldwide. As early detection of new incursions is key to effective control, new diagnostic tests that can detect pathogen presence shortly after initial infection hold great potential for detection of infection in individual hosts. However, these tests may be too expensive to be implemented at the sampling intensities required for early detection of a new epidemic at the population level. To evaluate the trade-off between earlier and/or more reliable detection and higher deployment costs, we need to consider the impacts of test performance, test cost and pathogen epidemiology. Regarding test performance, the period before new infections can be first detected and the probability of detecting them are of particular importance. We propose a generic framework that can be easily used to evaluate a variety of different detection methods and identify important characteristics of the pathogen and the detection method to consider when planning early detection surveillance. We demonstrate the application of our method using the plant pathogen Phytophthora ramorum in the UK, and find that visual inspec-tion for this pathogen is a more cost-effective strategy for early detection surveillance than an early detection diagnostic test.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Risk-based surveillance for meat-borne parasites

There is a plethora of meat-borne hazards - including parasites - for which there may be a need for surveillance. However, veterinary services worldwide need to decide how to use their scarce resources and prioritise among the perceived hazards. Moreover, to remain competitive, food business operators - irrespective of whether they are farmers or abattoir operators - are preoccupied with maintaining a profit and minimizing costs. Still, customers and trade partners expect that meat products placed on the market are safe to consume and should not bear any risks of causing disease. Risk-based surveillance systems may offer a solution to this challenge by applying risk analysis principles; first to set priorities, and secondly to allocate resources effectively and efficiently. The latter is done through a focus on the cost-effectiveness ratio in sampling and prioritisation. Risk-based surveillance was originally introduced into veterinary public health in 2006. Since then, experience has been gathered, and the methodology has been further developed. Guidelines and tools have been developed, which can be used to set up appropriate surveillance programmes. In this paper, the basic principles are described, and by use of a surveillance design tool called SURVTOOLS (https://survtools.org/), examples are given covering three meat-borne parasites for which risk-based surveillance is 1) either in place in the European Union (EU) (Trichinella spp.), 2) to be officially implemented in December 2019 (Taenia saginata) or 3) only carried out by one abattoir company in the EU as there is no official EU requirement (Toxoplasma gondii). Moreover, advantages, requirements and limitations of risk-based surveillance for meat-borne parasites are discussed.

One Health in Action: Operational Aspects of an Integrated Surveillance System for Zoonoses in Western Kenya

Surveillance of diseases in Kenya and elsewhere in East Africa is currently carried out by both human and animal health sectors. However, a recent evaluation highlighted the lack of integration between these sectors, leading to disease under-reporting and inefficiencies. This project aimed to develop an integrated and cost-effective surveillance and reporting system for 15 zoonotic diseases piloted in the counties of Bungoma, Busia, and Kakamega in western Kenya. Specifically, in this paper we describe the operational aspects of such a surveillance system. Interviews were carried out with key informants, and this was followed by field visits to identify sentinel sites and liaise with relevant stakeholders. Based on this information, a sampling strategy comprising 12 sentinel sites, 4 in each county, was developed. Each sentinel site comprised of a livestock market, 1-2 neighboring slaughter houses/slabs, and a hospital in the vicinity; each of the 12 sites, comprising 12 × 3 = 36 sampling locations, was visited every 4 weeks for 20 cycles. At each site, animal or patient sampling included a clinical examination and collection of blood, feces, and nasal swabs; in slaughtered animals, mesenteric lymph nodes, hydatid cysts, and flukes were also collected. At the end of each field visit, data on staff involved and challenges encountered were recorded, while biological samples were processed and tested for 15 zoonotic diseases in the field laboratory in Busia, Kenya. Public engagement sessions were held at each sentinel site to share preliminary results and provide feedback to both stakeholders and study participants. A livestock market visit lasted just over 3 h, and the most common challenge was the frequent refusals of animal owners to participate in the study. At the slaughterhouses, visits lasted just under 4 h, and challenges included poorly engaged meat inspectors or slaughter processes that were too quick for sampling. Finally, the hospital visits lasted around 4 h, and the most frequent challenges included low patients turn-out, frequent staff turn-over leading to poor institutional memory, and difficulty in obtaining patient stool samples. Our experiences have highlighted the importance of engaging with local stakeholders in the field, while also providing timely feedback through public engagement sessions, to ensure on-going compliance.

Valuing Health Surveillance as an Information System: Interdisciplinary Insights

The economic evaluation of health surveillance systems and of health information is a methodological challenge, as for information systems in general. Main present threads are considering cost-effectiveness solutions, minimizing costs for a given technically required output, or cost-benefit analysis, balancing costs with economic benefits of duly informed public interventions. The latter option, following a linear command-and-control perspective, implies considering a main causal link between information, decision, action, and health benefits. Yet, valuing information, taking into account its nature and multiple sources, the modalities of its processing cycle, from production to diffusion, decentralized use and gradual building of a shared information capital, constitutes a promising challenge. This work proposes an interdisciplinary insight on the value of health surveillance to get a renewed theoretical framework integrating information and informatics theory and information economics. The reflection is based on a typological approach of value, basically distinguishing between use and non-use values. Through this structured discussion, the main idea is to expand the boundaries of surveillance evaluation, to focus on changes and trends, on the dynamic and networked structure of information systems, on the contribution of diverse data, and on the added value of combining qualitative and quantitative information. Distancing itself from the command-and-control model, this reflection considers the behavioral fundaments of many health risks, as well as the decentralized, progressive and deliberative dimension of decision-making in risk management. The framework also draws on lessons learnt from recent applications within and outside of health sector, as in surveillance of antimicrobial resistance, inter-laboratory networks, the use of big data or web sources, the diffusion of technological products and large-scale financial risks. Finally, the paper poses the bases to think the challenge of a workable approach to economic evaluation of health surveillance through a better understanding of health information value. It aims to avoid over-simplifying the range of health information benefits across society while keeping evaluation within the boundaries of what may be ascribed to the assessed information system.

Complex System Approaches for Animal Health Surveillance

Many new and highly variable data are currently being produced by the many participants in farmed animal productions systems. These data hold the promise of new information with potential value for animal health surveillance. The current analytical paradigm for dealing with these new data is to implement syndromic surveillance systems, which focus mainly on univariate event detection methods applied to individual time series, with the goal of identifying epidemics in the population. This approach is relatively limited in the scope and not well-suited for extracting much of the additional information that is contained within these data. These approaches have value and should not be abandoned. However, an additional, new analytical paradigm will be needed if surveillance and disease control agencies wish to extract additional information from these data. We propose a more holistic analytical approach borrowed from complex system science that considers animal disease to be a product of the complex interactions between the many individuals, organizations and other factors that are involved in, or influence food production systems. We will discuss the characteristics of farmed animal food production systems that make them complex adaptive systems and propose practical applications of methods borrowed from complex system science to help animal health surveillance practitioners extract additional information from these new data.

Evidence needed for antimicrobial resistance surveillance systems

One Health surveillance for antimicrobial resistance has been promoted by the scientific community and by international organizations for more than a decade. In this article, we highlight issues that need to be addressed to improve the understanding of the effectiveness of One Health surveillance for antimicrobial resistance. We also outline the evidence needed to support countries planning to increase the level of integration of their surveillance system. Based on experience in Canada and other countries, we argue that more effort is needed to understand and measure the added value of One Health for antimicrobial resistance surveillance and to identify the most effective integration strategies. To date, guidelines for the development of One Health surveillance have focused mainly on the types of data that should be integrated. However, it may be necessary to apply the concept of One Health to surveillance tasks beyond data integration to realize the full value of the approach. Integration can be enhanced across different surveillance activities (data collection, analysis, interpretation and dissemination), taking account of the different skills and perspectives of experts and stakeholders involved. More research is needed to investigate the mechanisms through which a One Health approach to surveillance can increase the performance of antimicrobial resistance surveillance and, ultimately, improve health outcomes.

Using multi-criteria risk ranking methodology to select case studies for a generic risk assessment framework for exotic disease incursion and spread through Europe

We present a novel approach of using the multi-criteria pathogen prioritisation methodology as a basis for selecting the most appropriate case studies for a generic risk assessment framework. The approach uses selective criteria to rank exotic animal health pathogens according to the likelihood of introduction and the impact of an outbreak if it occurred in the European Union (EU). Pathogens were evaluated based on their impact on production at the EU level and international trade. A subsequent analysis included criteria of relevance to quantitative risk assessment case study selection, such as the availability of data for parameterisation, the need for further research and the desire for the case studies to cover different routes of transmission. The framework demonstrated is flexible with the ability to adjust both the criteria and their weightings to the user's requirements. A web based tool has been developed using the RStudio shiny apps software, to facilitate this.

Evaluation of the cost-effectiveness of bovine brucellosis surveillance in a disease-free country using stochastic scenario tree modelling

Surveillance systems of exotic infectious diseases aim to ensure transparency about the country-specific animal disease situation (i.e. demonstrate disease freedom) and to identify any introductions. In a context of decreasing resources, evaluation of surveillance efficiency is essential to help stakeholders make relevant decisions about prioritization of measures and funding allocation. This study evaluated the efficiency (sensitivity related to cost) of the French bovine brucellosis surveillance system using stochastic scenario tree models. Cattle herds were categorized into three risk groups based on the annual number of purchases, given that trading is considered as the main route of brucellosis introduction in cattle herds. The sensitivity in detecting the disease and the costs of the current surveillance system, which includes clinical (abortion) surveillance, programmed serological testing and introduction controls, were compared to those of 19 alternative surveillance scenarios. Surveillance costs included veterinary fees and laboratory analyses. The sensitivity over a year of the current surveillance system was predicted to be 91±7% at a design prevalence of 0.01% for a total cost of 14.9±1.8 million €. Several alternative surveillance scenarios, based on clinical surveillance and random or risk-based serological screening in a sample (20%) of the population, were predicted to be at least as sensitive but for a lower cost. Such changes would reduce whole surveillance costs by 20 to 61% annually, and the costs for farmers only would be decreased from about 12.0 million € presently to 5.3–9.0 million € (i.e. 25–56% decrease). Besides, fostering the evolution of the surveillance system in one of these directions would be in agreement with the European regulations and farmers perceptions on brucellosis risk and surveillance.

Cost-effectiveness evaluation of bovine tuberculosis surveillance in wildlife in France (Sylvatub system) using scenario trees

Bovine tuberculosis (bTB) is a common disease in cattle and wildlife, with health, zoonotic and economic implications. Infected wild animals, and particularly reservoirs, could hinder eradication of bTB from cattle populations, which could have an important impact on international cattle trade. Therefore, surveillance of bTB in wildlife is of particular importance to better understand the epidemiological role of wild species and to adapt the control measures. In France, a bTB surveillance system for free-ranging wildlife, the Sylvatub system, has been implemented since 2011. It relies on three surveillance components (SSCs) (passive surveillance on hunted animals (EC-SSC), passive surveillance on dead or dying animals (SAGIR-SSC) and active surveillance (PSURV-SSC)). The effectiveness of the Sylvatub system was previously assessed, through the estimation of its sensitivity (i.e. the probability of detecting at least one case of bTB infection by each SSC, specie and risk-level area). However, to globally assess the performance of a surveillance system, the measure of its sensitivity is not sufficient, as other factors such as economic or socio-economic factors could influence the effectiveness. We report here an estimation of the costs of the surveillance activities of the Sylvatub system, and of the cost-effectiveness of each surveillance component, by specie and risk-level, based on scenario tree modelling with the same tree structure as used for the sensitivity evaluation. The cost-effectiveness of the Sylvatub surveillance is better in higher-risk departments, due in particular to the higher probability of detecting the infection (sensitivity). Moreover, EC-SSC, which has the highest unit cost, is more efficient than the surveillance enhanced by the SAGIR-SSC, due to its better sensitivity. The calculation of the cost-effectiveness ratio shows that PSURV-SSC remains the most cost-effective surveillance component of the Sylvatub system, despite its high cost in terms of coordination, sample collection and laboratory analysis.

Economics of zoonoses surveillance in a 'One Health' context: an assessment of Campylobacter surveillance in Switzerland

Cross-sectorial surveillance and general collaboration between the animal and the public health sectors are increasingly recognized as needed to better manage the impacts of zoonoses. From 2009, the Swiss established a Campylobacter mitigation system that includes human and poultry surveillance data-sharing within a multi-sectorial platform, in a 'One Health' approach. The objective of this study was to explore the economics of this cross-sectorial approach, including surveillance and triggered interventions. Costs and benefits of the One Health and of the uni-sectorial approach to Campylobacter surveillance were identified using an economic assessment framework developed earlier. Cost information of surveillance activities and interventions was gathered and disability-adjusted life years (DALYs) associated with the disease estimated for 2008 and 2013. In the first 5 years of this One Health approach to Campylobacter mitigation, surveillance contributed with information mainly used to perform risk assessments, monitor trends and shape research efforts on Campylobacter. There was an increase in costs associated with the mitigation activities following integration, due mainly to the allocation of additional resources to research and implementation of poultry surveillance. The overall burden of campylobacteriosis increased by 3·4-8·8% to 1751-2852 DALYs in 2013. In the timing of the analysis, added value associated with this cross-sectorial approach to surveillance of Campylobacter in the country was likely generated through non-measurable benefits such as intellectual capital and social capital.

Effectiveness and Cost Efficiency of Different Surveillance Components for Proving Freedom and Early Detection of Disease: Bluetongue Serotype 8 in Cattle as Case Study for Belgium, France and the Netherlands

Quick detection and recovery of country's freedom status remain a constant challenge in animal health surveillance. The efficacy and cost efficiency of different surveillance components in proving the absence of infection or (early) detection of bluetongue serotype 8 in cattle populations within different countries (the Netherlands, France, Belgium) using surveillance data from years 2006 and 2007 were investigated using an adapted scenario tree model approach. First, surveillance components (sentinel, yearly cross-sectional and passive clinical reporting) within each country were evaluated in terms of efficacy for substantiating freedom of infection. Yearly cross-sectional survey and passive clinical reporting performed well within each country with sensitivity of detection values ranging around 0.99. The sentinel component had a sensitivity of detection around 0.7. Secondly, how effective the components were for (early) detection of bluetongue serotype 8 and whether syndromic surveillance on reproductive performance, milk production and mortality data available from the Netherlands and Belgium could be of added value were evaluated. Epidemic curves were used to estimate the timeliness of detection. Sensitivity analysis revealed that expected within-herd prevalence and number of herds processed were the most influential parameters for proving freedom and early detection. Looking at the assumed direct costs, although total costs were low for sentinel and passive clinical surveillance components, passive clinical surveillance together with syndromic surveillance (based on reproductive performance data) turned out most cost-efficient for the detection of bluetongue serotype 8. To conclude, for emerging or re-emerging vectorborne disease that behaves such as bluetongue serotype 8, it is recommended to use passive clinical and syndromic surveillance as early detection systems for maximum cost efficiency and sensitivity. Once an infection is detected and eradicated, cross-sectional screening for substantiating freedom of infection and sentinel for monitoring the disease evolution are recommended.

Economic Assessment of Zoonoses Surveillance in a 'One Health' Context: A Conceptual Framework

Collaboration between animal and public health sectors has been highlighted as a means to improve the management of zoonotic threats. This includes surveillance systems for zoonoses, where enhanced cross-sectoral integration and sharing of information are seen as key to improved public health outcomes. Yet, there is a lack of evidence on the economic returns of such collaboration, particularly in the development and implementation of surveillance programmes. The economic assessment of surveillance in this context needs to be underpinned by the understanding of the links between zoonotic disease surveillance in animal populations and the wider public health disease mitigation process and how these relations impact on the costs and benefits of the surveillance activities. This study presents a conceptual framework of these links as a basis for the economic assessment of cross-sectoral zoonoses surveillance with the aim of supporting the prioritization of resource allocation to surveillance. In the proposed framework, monetary, non-monetary and intermediate or intangible cost components and benefit streams of three conceptually distinct stages of zoonotic disease mitigation are identified. In each stage, as the final disease mitigation objective varies so does the use of surveillance information generated in the animal populations for public health decision-making. Consequently, the associated cost components and benefit streams also change. Building on the proposed framework and taking into account these links, practical steps for its application are presented and future challenges are discussed.

African Swine Fever in Uganda: Qualitative Evaluation of Three Surveillance Methods with Implications for Other Resource-Poor Settings

Animal diseases impact negatively on households and on national economies. In low-income countries, this pertains especially to socio-economic effects on household level. To control animal diseases and mitigate their impact, it is necessary to understand the epidemiology of the disease in its local context. Such understanding, gained through disease surveillance, is often lacking in resource-poor settings. Alternative surveillance methods have been developed to overcome some of the hurdles obstructing surveillance. The objective of this study was to evaluate and qualitatively compare three methods for surveillance of acute infectious diseases using African swine fever in northern Uganda as an example. Report-driven outbreak investigations, participatory rural appraisals (PRAs), and a household survey using a smartphone application were evaluated. All three methods had good disease-detecting capacity, and each of them detected many more outbreaks compared to those reported to the World Organization for Animal Health during the same time period. Apparent mortality rates were similar for the three methods although highest for the report-driven outbreak investigations, followed by the PRAs, and then the household survey. The three methods have different characteristics and the method of choice will depend on the surveillance objective. The optimal situation might be achieved by a combination of the methods: outbreak detection via smartphone-based real-time surveillance, outbreak investigation for collection of biological samples, and a PRA for a better understanding of the epidemiology of the specific outbreak. All three methods require initial investments and continuous efforts. The sustainability of the surveillance system should, therefore, be carefully evaluated before making such investments.

A conceptual framework for economic optimization of an animal health surveillance portfolio

Decision making on hazard surveillance in livestock product chains is a multi-hazard, multi-stakeholder, and multi-criteria process that includes a variety of decision alternatives. The multi-hazard aspect means that the allocation of the scarce resource for surveillance should be optimized from the point of view of a surveillance portfolio (SP) rather than a single hazard. In this paper, we present a novel conceptual approach for economic optimization of a SP to address the resource allocation problem for a surveillance organization from a theoretical perspective. This approach uses multi-criteria techniques to evaluate the performances of different settings of a SP, taking cost-benefit aspects of surveillance and stakeholders’ preferences into account. The credibility of the approach has also been checked for conceptual validity, data needs and operational validity; the application potentials of the approach are also discussed.

Reconciling surveillance systems with limited resources: an evaluation of passive surveillance for rabies in an endemic setting

Surveillance systems for rabies in endemic regions are often subject to severe constraints in terms of resources. The World Organisation for Animal Health (OIE) and the World Health Organisation (WHO) propose the use of an active surveillance system to substantiate claims of disease freedom, including rabies. However, many countries do not have the resources to establish active surveillance systems for rabies and the testing of dead dogs poses logistical challenges. This paper explores the potential of using a scenario tree model parameterised with data collected via questionnaires and interviews to estimate the sensitivity of passive surveillance, assessing its potential as a viable low-cost alternative to active surveillance systems. The results of this explorative study illustrated that given a large enough sample size, in this case the entire population of Colombo City, the sensitivity of passive surveillance can be 100% even at a low disease prevalence (0.1%), despite the low sensitivity of individual surveillance components (mean values in the range 4.077×10(-5)-1.834×10(-3) at 1% prevalence). In addition, logistic regression was used to identify factors associated with increased recognition of rabies in dogs and reporting of rabies suspect dogs. Increased recognition was observed amongst dog owners (OR 3.8 (CI, 1.3-10.8)), people previously bitten by dogs (OR 5.9 (CI, 2.2-15.9)) and people who believed they had seen suspect dogs in the past (OR 4.7 (CI, 1.8-12.9)). Increased likelihood of reporting suspect dogs was observed amongst dog owners (OR 5.3 (CI, 1.1-25)). Further work is required to validate the data collection tool and the assumptions made in the model with respect to sample size in order to develop a robust methodology for evaluating passive rabies surveillance.

One Health surveillance - More than a buzz word?

One Health surveillance describes the systematic collection, validation, analysis, interpretation of data and dissemination of information collected on humans, animals and the environment to inform decisions for more effective, evidence- and system-based health interventions. During the second International Conference on Animal Health Surveillance (ICAHS) in Havana, Cuba, a panel discussion was organised to discuss the relevance of One Health in the context of surveillance. A number of success stories were presented which generally focused on the obvious interfaces between human and veterinary medicine such as zoonoses and food safety. Activities aimed at strengthening inter-sectoral networking through technical collaboration, conferences, workshops and consultations have resulted in recommendations to advance the One Health concept. There are also several One Health educational programmes offered as Masters programmes. Continuing challenges to One Health surveillance were identified at both technical as well as organisational level. It was acknowledged that the public health sector and the environmental sector could be engaged more in One Health activities. Legal issues, hurdles to data sharing, unclear responsibilities and structural barriers between ministries prevent integrated action. Policy makers in the health sector often perceive One Health as a veterinary-driven initiative that is not particularly relevant to their priority problems. Whilst some funding schemes allow for the employment of scientists and technicians for research projects, the development of a sustainable One Health workforce has yet to be broadly demonstrated. Funding opportunities do not explicitly promote the development of One Health surveillance systems. In addition, organisational, legal and administrative barriers may prevent operational implementation. Strategies and communication across sectors need to be aligned. Whilst at the technical or local level the formal separation can be bridged, separate funding sources and budgets can jeopardise the overall strategy, especially if funding cuts are later required. To overcome such challenges, a strong business case for One Health surveillance is needed. This should include the costs and benefits of One Health activities or projects including consequences of different strategies as well as risks. Integrated training should also be further promoted. Future ICAHS conferences should continue to provide a platform for discussing surveillance in the One Health context and to provide a forum for surveillance professionals from all relevant sectors to interact.

A rationale to unify measurements of effectiveness for animal health surveillance

Surveillance systems produce data which, once analysed and interpreted, support decisions regarding disease management. While several performance measures for surveillance are in use, no theoretical framework has been proposed yet with a rationale for defining and estimating effectiveness measures of surveillance systems in a generic way. An effective surveillance system is a system whose data collection, analysis and interpretation processes lead to decisions that are appropriate given the true disease status of the target population. Accordingly, we developed a framework accounting for sampling, testing and data interpretation processes, to depict in a probabilistic way the direction and magnitude of the discrepancy between "decisions that would be made if the true state of a population was known" and the "decisions that are actually made upon the analysis and interpretation of surveillance data". The proposed framework provides a theoretical basis for standardised quantitative evaluation of the effectiveness of surveillance systems. We illustrate such approaches using hypothetical surveillance systems aimed at monitoring the prevalence of an endemic disease and at detecting an emerging disease as early as possible and with an empirical case study on a passive surveillance system aiming at detecting cases of Highly Pathogenic Avian Influenza cases in Vietnamese poultry.

Systems approaches to animal disease surveillance and resource allocation: methodological frameworks for behavioral analysis

While demands for animal disease surveillance systems are growing, there has been little applied research that has examined the interactions between resource allocation, cost-effectiveness, and behavioral considerations of actors throughout the livestock supply chain in a surveillance system context. These interactions are important as feedbacks between surveillance decisions and disease evolution may be modulated by their contextual drivers, influencing the cost-effectiveness of a given surveillance system. This paper identifies a number of key behavioral aspects involved in animal health surveillance systems and reviews some novel methodologies for their analysis. A generic framework for analysis is discussed, with exemplar results provided to demonstrate the utility of such an approach in guiding better disease control and surveillance decisions.

Assessing the expenditure distribution of animal health surveillance: the case of Great Britain

Animal health surveillance in Great Britain (GB) is conducted through public and private initiatives, yet there is no consolidated information on these activities and their outcomes. We developed an inventory of livestock health surveillance programmes in GB to identify gaps in resource use and potential synergies that could be exploited. The inventory contained details of 36 livestock surveillance activities active in 2011. Data were collected by questionnaire and interviews. Livestock health surveillance funding was found to be unevenly distributed between species: the vast majority (approximately 94 per cent) was spent on cattle diseases (tuberculosis surveillance accounted for most of this expenditure), with 2 per cent on pigs, 2 per cent on sheep/goats, 1 per cent on poultry, and 1 per cent on antimicrobial resistance surveillance across all species. Consequently, surveillance efforts in GB appears heavily skewed towards regions with high cattle densities, particularly high-prevalence tuberculosis areas such as the southwest. The contribution of private schemes to surveillance funding was hard to quantify due to limited access to data, but was estimated to be about 10 per cent. There is scope to better understand the benefits of surveillance, enhance data sharing, clarify costs and identify who pays and who gains. Health surveillance should be considered within the sharing of responsibilities for disease control.

Potential use of school absenteeism record for disease surveillance in developing countries, case study in rural Cambodia

BACKGROUND

Disease surveillance allows prospective monitoring of patterns in disease incidence in the general community, specific institutions (e.g. hospitals, elderly care homes), and other important population subgroups. Surveillance activities are now routinely conducted in many developed countries and in certain easy-to-reach areas of the developing ones. However due to limited health resources, population in rural area that consisted of the most the vulnerable groups are not under surveillance. Cheaper alternative ways for disease surveillance were needed in resource-limited settings.

METHODS AND FINDINGS

In this study, a syndromic surveillance system using disease specific absenteeism rates was established in 47 pre-schools with 1,417 students 3-6 y of age in a rural area of Kampot province, Cambodia. School absenteeism data were collected via short message service. Data collected between 1st January and 31st December 2012 was used for system evaluation for future potential use in larger scale. The system appeared to be feasible and acceptable in the rural study setting. Moderate correlation was found between rates of school absenteeism due to illness and the reference data on rates of attendance at health centers in persons <16 y (maximum cross-correlation coefficient = 0.231 at lag = -1 week).

CONCLUSIONS

School absenteeism data is pre-existing, easily accessible and requires minimum time and resources after initial development, and our results suggest that this system may be able to provide complementary data for disease surveillance, especially in resource limited settings where there is very little information on illnesses in the community and traditional surveillance systems are difficult to implement. An important next step is to validate the syndromic data with other forms of surveillance including laboratory data.

Application of loop analysis for the qualitative assessment of surveillance and control in veterinary epidemiology

Background

Systems for animal disease mitigation involve both surveillance activities and interventions to control the disease. They are complex organizations that are described by partial or imprecise data, making it difficult to evaluate them or make decisions to improve them. A mathematical method, called loop analysis, can be used to model qualitatively the structure and the behavior of dynamic systems; it relies on the study of the sign of the interactions between the components of the system. This method, currently widely used by ecologists, has to our knowledge never been applied in the context of animal disease mitigation systems. The objective of the study was to assess whether loop analysis could be applied to this new context. We first developed a generic model that restricted the applicability of the method to event-based surveillance systems of endemic diseases, excluding the emergence and eradication phases. Then we chose the mitigation system of highly pathogenic avian influenza (HPAI) H5N1 in Cambodia as an example of such system to study the application of loop analysis to a real disease mitigation system.

Results

Breaking down the generic model, we constructed a 6-variables model to represent the HPAI H5N1 mitigation system in Cambodia. This construction work improved our understanding of this system, highlighting the link between surveillance and control which is unclear in traditional representations of this system. Then we analyzed the effect of the perturbations to this HPAI H5N1 mitigation system that we interpreted in terms of investment in a given compartment. This study suggested that increasing intervention at a local level can optimize the system’s efficiency. Indeed, this perturbation both decreases surveillance and intervention costs and reduces the disease’s occurrence.

Conclusion

Loop analysis can be applied to disease mitigation systems. Its main strength is that it is easy to design, focusing on the signs of the interactions. It is a simple and flexible tool that could be used as a precursor to large-scale quantitative studies, to support reflection about disease mitigation systems structure and functioning.

Cost analysis of various low pathogenic avian influenza surveillance systems in the Dutch egg layer sector

BACKGROUND

As low pathogenic avian influenza viruses can mutate into high pathogenic viruses the Dutch poultry sector implemented a surveillance system for low pathogenic avian influenza (LPAI) based on blood samples. It has been suggested that egg yolk samples could be sampled instead of blood samples to survey egg layer farms. To support future decision making about AI surveillance economic criteria are important. Therefore a cost analysis is performed on systems that use either blood or eggs as sampled material.

METHODOLOGY/PRINCIPAL FINDINGS

The effectiveness of surveillance using egg or blood samples was evaluated using scenario tree models. Then an economic model was developed that calculates the total costs for eight surveillance systems that have equal effectiveness. The model considers costs for sampling, sample preparation, sample transport, testing, communication of test results and for the confirmation test on false positive results. The surveillance systems varied in sampled material (eggs or blood), sampling location (farm or packing station) and location of sample preparation (laboratory or packing station). It is shown that a hypothetical system in which eggs are sampled at the packing station and samples prepared in a laboratory had the lowest total costs (i.e. € 273,393) a year. Compared to this a hypothetical system in which eggs are sampled at the farm and samples prepared at a laboratory, and the currently implemented system in which blood is sampled at the farm and samples prepared at a laboratory have 6% and 39% higher costs respectively.

CONCLUSIONS/SIGNIFICANCE

This study shows that surveillance for avian influenza on egg yolk samples can be done at lower costs than surveillance based on blood samples. The model can be used in future comparison of surveillance systems for different pathogens and hazards.