Modelling the effectiveness and risks of vaccination strategies to control classical swine fever epidemics

Policies to prevent zoonotic spillover: a systematic scoping review of evaluative evidence

BACKGROUND

Emerging infectious diseases of zoonotic origin present a critical threat to global population health. As accelerating globalisation makes epidemics and pandemics more difficult to contain, there is a need for effective preventive interventions that reduce the risk of zoonotic spillover events. Public policies can play a key role in preventing spillover events. The aim of this review is to identify and describe evaluations of public policies that target the determinants of zoonotic spillover. Our approach is informed by a One Health perspective, acknowledging the inter-connectedness of human, animal and environmental health.

METHODS

In this systematic scoping review, we searched Medline, SCOPUS, Web of Science and Global Health in May 2021 using search terms combining animal health and the animal-human interface, public policy, prevention and zoonoses. We screened titles and abstracts, extracted data and reported our process in line with PRISMA-ScR guidelines. We also searched relevant organisations' websites for evaluations published in the grey literature. All evaluations of public policies aiming to prevent zoonotic spillover events were eligible for inclusion. We summarised key data from each study, mapping policies along the spillover pathway.

RESULTS

Our review found 95 publications evaluating 111 policies. We identified 27 unique policy options including habitat protection; trade regulations; border control and quarantine procedures; farm and market biosecurity measures; public information campaigns; and vaccination programmes, as well as multi-component programmes. These were implemented by many sectors, highlighting the cross-sectoral nature of zoonotic spillover prevention. Reports emphasised the importance of surveillance data in both guiding prevention efforts and enabling policy evaluation, as well as the importance of industry and private sector actors in implementing many of these policies. Thoughtful engagement with stakeholders ranging from subsistence hunters and farmers to industrial animal agriculture operations is key for policy success in this area.

CONCLUSION

This review outlines the state of the evaluative evidence around policies to prevent zoonotic spillover in order to guide policy decision-making and focus research efforts. Since we found that most of the existing policy evaluations target 'downstream' determinants, additional research could focus on evaluating policies targeting 'upstream' determinants of zoonotic spillover, such as land use change, and policies impacting infection intensity and pathogen shedding in animal populations, such as those targeting animal welfare.

Approaches for disease prioritization and decision-making in animal health, 2000-2021: a structured scoping review

This scoping review identifies and describes the methods used to prioritize diseases for resource allocation across disease control, surveillance, and research and the methods used generally in decision-making on animal health policy. Three electronic databases (Medline/PubMed, Embase, and CAB Abstracts) were searched for articles from 2000 to 2021. Searches identified 6, 395 articles after de-duplication, with an additional 64 articles added manually. A total of 6, 460 articles were imported to online document review management software (sysrev.com) for screening. Based on inclusion and exclusion criteria, 532 articles passed the first screening, and after a second round of screening, 336 articles were recommended for full review. A total of 40 articles were removed after data extraction. Another 11 articles were added, having been obtained from cross-citations of already identified articles, providing a total of 307 articles to be considered in the scoping review. The results show that the main methods used for disease prioritization were based on economic analysis, multi-criteria evaluation, risk assessment, simple ranking, spatial risk mapping, and simulation modeling. Disease prioritization was performed to aid in decision-making related to various categories: (1) disease control, prevention, or eradication strategies, (2) general organizational strategy, (3) identification of high-risk areas or populations, (4) assessment of risk of disease introduction or occurrence, (5) disease surveillance, and (6) research priority setting. Of the articles included in data extraction, 50.5% had a national focus, 12.3% were local, 11.9% were regional, 6.5% were sub-national, and 3.9% were global. In 15.2% of the articles, the geographic focus was not specified. The scoping review revealed the lack of comprehensive, integrated, and mutually compatible approaches to disease prioritization and decision support tools for animal health. We recommend that future studies should focus on creating comprehensive and harmonized frameworks describing methods for disease prioritization and decision-making tools in animal health.

Fuzzy model for quantitative assessment of the epidemic risk of African Swine Fever within Australia

African Swine Fever (ASF) has spread rapidly across different continents since 2007 and caused huge biosecurity threats and economic losses. Establishing an effective risk assessment model is of great importance for ASF prevention, especially for those ASF-free countries such as Australia. With a vast territory and an economy heavily relying on primary industry, Australia faces a threat from the spread of ASF. Although ordinary quarantine measures have been well-performed throughout Australia, there is still a need to develop an effective risk assessment model to understand the spread of ASF due to the strong transmission ability of ASF. In this paper, via a comprehensive literature review, and analyzing the transmission factors of ASF, we provide a fuzzy model to assess the epidemic risk of Australian states and territories, under the assumption that ASF has entered Australia. As demonstrated in this work, although the pandemic risk of ASF in Australia is relatively low, there is a risk of irregular and scattered outbreaks, with Victoria (VIC) and New South Wales (NSW) - Australia Capital Territory (NSW-ACT) showed the highest risk. The reliability of this model was also systematically tested by a conjoint analysis model. To our knowledge, this is the first study to comprehensively analyze the ASF epidemic risk in a country using fuzzy modeling. This work can provide an understanding of the risk ASF transmission within Australia based on the fuzzy modeling, the same methodology can also provide insights and useful information for the establishment of fuzzy models to perform the ASF risk assessment for other countries.

An exploration of within-herd dynamics of a transboundary livestock disease: A foot and mouth disease case study

Transboundary livestock diseases are a high priority for policy makers because of the serious economic burdens associated with infection. In order to make well informed preparedness and response plans, policy makers often utilize mathematical models to understand possible outcomes of different control strategies and outbreak scenarios. Many of these models focus on the transmission between herds and the overall trajectory of the outbreak. While the course of infection within herds has not been the focus of the majority of models, a thorough understanding of within-herd dynamics can provide valuable insight into a disease system by providing information on herd-level biological properties of the infection, which can be used to inform decision making in both endemic and outbreak settings and to inform larger between-herd models. In this study, we develop three stochastic simulation models to study within-herd foot and mouth disease dynamics and the implications of different empirical data-based assumptions about the timing of the onset of infectiousness and clinical signs. We also study the influence of herd size and the proportion of the herd that is initially infected on the outcome of the infection. We find that increasing herd size increases the duration of infectiousness and that the size of the herd plays a more significant role in determining this duration than the number of initially infected cattle in that herd. We also find that the assumptions made regarding the onset of infectiousness and clinical signs, which are based on contradictory empirical findings, can result in the predictions about when infection would be detectable differing by several days. Therefore, the disease progression used to characterize the course of infection in a single bovine host could have significant implications for determining when herds can be detected and subsequently controlled; the timing of which could influence the overall predicted trajectory of outbreaks.

Risk Factors and Spatiotemporal Analysis of Classical Swine Fever in Ecuador

Classical swine fever (CSF) is one of the most important re-emergent swine diseases worldwide. Despite concerted control efforts in the Andean countries, the disease remains endemic in several areas, limiting production and trade opportunities. In this study, we aimed to determine the risk factors and spatiotemporal implications associated with CSF in Ecuador. We analysed passive surveillance and vaccination campaign datasets from 2014 to 2020; Then, we structured a herd-level case-control study using a logistic and spatiotemporal Bayesian model. The results showed that the risk factors that increased the odds of CSF occurrence were the following: swill feeding (OR 8.53), time until notification (OR 2.44), introduction of new pigs during last month (OR 2.01) and lack of vaccination against CSF (OR 1.82). The spatiotemporal model showed that vaccination reduces the risk by 33%. According to the priority index, the intervention should focus on Morona Santiago and Los Rios provinces. In conclusion, the results highlight the complexity of the CSF control programs, the importance to improve the overall surveillance system and the need to inform decision-makers and stakeholders.

Estimation of the Lethality Rate, Recovery Rate, and Case Fatality Ratio of Classical Swine Fever in Japanese Wild Boar: An Analysis of the Epidemics From September 2018 to March 2019

Understanding the morbidity and lethality of diseases is necessary to evaluate the effectiveness of countermeasure against the epidemics (e.g., vaccination). To estimate them, detailed data on host population dynamics are required; however, estimating the population size for wildlife is often difficult. We aimed to elucidate the morbidity and lethality of classical swine fever (CSF) currently highly prevalent in the wild boar population in Japan. To this end, we estimated lethality rate, recovery rate, and case fatality ratio (CFR) of CSF without detailed data on the population estimates of wild boar. A mathematical model was constructed to describe the CSF dynamics and population dynamics of wild boar. We fitted the model to the (i) results of the reverse transcription polymerase chain reaction (RT-PCR) test for the CSFV gene and the (ii) results of the enzyme-linked immunosorbent assay (ELISA) test for the antibody against CSFV in sampled wild boar. In the 280 wild boar sampled from September 2018 to March 2019 in the major CSF-affected area in Japan, the lethality rate and recovery rate of CSF per week were estimated as 0.165 (95% confidence interval: 0.081–0.250) and 0.004 (0–0.009), respectively. While the estimate of lethality rate of CSF was similar with the estimates in previous studies, the recovery rate was lower than those reported previously. CFR was estimated as 0.959 (0.904–0.981) using our estimate of recovery rate. This study is the first to estimate lethality rate of CSF from the dynamics of CSF epidemics in the wild boar population. Since the value of CFR is sensitive to the value of recovery rate, the accuracy in the estimate of recovery rate is a key for the accurate estimation of CFR. A long-term transmission experiment of moderately virulent strains may lead to more accurate estimation of the recovery rate and CFR of CSF.

Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission

Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology-based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We describe a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (i) individual exposures and (ii) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the regional relative risk-a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions. In theory, these regions can range from individual rooms to large geographic areas. In this paper, we apply the regional relative risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km, demonstrating that in many common cases minimal input information is required to use the metric. Also, we demonstrate that the model predictions are consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as transmission within indoor environments. This work provides context for (i) the initial stages of an airborne disease outbreak and (ii) larger-scale disease spread, including unexpected low-probability disease "sparks" that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks. IMPORTANCE Airborne infectious disease transmission events occur over a wide range of spatial scales and can be important to disease outbreaks. We describe a new physics- and biology-based theory for the important case in which individuals are infected by a single airborne particle (even though numerous infectious particles can be emitted into the air and inhaled). Based on this theory, we propose a new epidemiological metric, regional relative risk, that compares the risk between two geographic regions (in theory, regions can range from individual rooms to large areas). Our modeling of transmission events predicts that for many scenarios of interest, minimal information is required to use this metric for locations 50 m to 20 km downwind. This prediction is consistent with data from prior disease outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as indoor environments. Our results may be applicable to many airborne diseases a priori, as these results depend on the physics of airborne particulate dispersion.

Spatial model of foot-and-mouth disease outbreak in an endemic area of Thailand

Foot-and-mouth disease (FDM) is a disease of cloven-hoofed animals with high costs in animal welfare and animal production. Up to now, transmission between farms in FMD-endemic areas has been given little attention. Between farm transmission can be quantified by distance independent transmission parameters and a spatial transmission kernel indicating the rate of transmission of an infected farm to susceptible farms depending on the distance. The spatial transmission kernel and distance-independent transmission parameters were estimated from data of an FMD outbreak in Lamphaya Klang subdistrict in Thailand between 2016 and 2017. The spatial between-farm transmission rate in Lamphaya Klang subdistrict was higher compared with the spatial between-farm transmission rate from FMDV in epidemic areas. The result can be explained by the larger size of the within-farm outbreak in the endemic area due to no culling. The inclusion of distance-independent transmission parameters improved the model fit, which suggests the presence of transmission sources from outside the area and spread within the area independent of the distance between farms. The remaining distance-dependent transmission was mainly local and could be due to over-the-fence transmission or other forms of contact between nearby farms. Farm size on the kernel positively affects the transmission rate, by increasing both infectivity and susceptibility with increasing farm size. The results showed that both distance-dependent transmission and distance-independent transmission were contributed to FMDV transmission in Lamphaya Klang outbreak. These transmission parameters help to gain knowledge about FMD transmission dynamic in the endemic area.

Transboundary Animal Diseases, an Overview of 17 Diseases with Potential for Global Spread and Serious Consequences

Animals provide food and other critical resources to most of the global population. As such, diseases of animals can cause dire consequences, especially disease with high rates of morbidity or mortality. Transboundary animal diseases (TADs) are highly contagious or transmissible, epidemic diseases, with the potential to spread rapidly across the globe and the potential to cause substantial socioeconomic and public health consequences. Transboundary animal diseases can threaten the global food supply, reduce the availability of non-food animal products, or cause the loss of human productivity or life. Further, TADs result in socioeconomic consequences from costs of control or preventative measures, and from trade restrictions. A greater understanding of the transmission, spread, and pathogenesis of these diseases is required. Further work is also needed to improve the efficacy and cost of both diagnostics and vaccines. This review aims to give a broad overview of 17 TADs, providing researchers and veterinarians with a current, succinct resource of salient details regarding these significant diseases. For each disease, we provide a synopsis of the disease and its status, species and geographic areas affected, a summary of in vitro or in vivo research models, and when available, information regarding prevention or treatment.

Assessment of the control measures of the category A diseases of Animal Health Law: Classical Swine Fever

EFSA received a mandate from the European Commission to assess the effectiveness of some of the control measures against diseases included in the Category A list according to Regulation (EU) 2016/429 on transmissible animal diseases ('Animal Health Law'). This opinion belongs to a series of opinions where these control measures will be assessed, with this opinion covering the assessment of control measures for Classical swine fever (CSF). In this opinion, EFSA and the AHAW Panel of experts review the effectiveness of: (i) clinical and laboratory sampling procedures, (ii) monitoring period and (iii) the minimum radii of the protection and surveillance zones, and the minimum length of time the measures should be applied in these zones. The general methodology used for this series of opinions has been published elsewhere; nonetheless, details of the model used for answering these questions are presented in this opinion as well as the transmission kernels used for the assessment of the minimum radius of the protection and surveillance zones. Several scenarios for which these control measures had to be assessed were designed and agreed prior to the start of the assessment. Here, several recommendations are given on how to increase the effectiveness of some of the sampling procedures. Based on the average length of the period between virus introduction and the reporting of a CSF suspicion, the monitoring period was assessed as non-effective. In a similar way, it was recommended that the length of the measures in the protection and surveillance zones were increased from 15 to 25 days in the protection zone and from 30 to 40 days in the surveillance zone. Finally, the analysis of existing Kernels for CSF suggested that the radius of the protection and the surveillance zones comprise 99% of the infections from an affected establishment if transmission occurred. Recommendations provided for each of the scenarios assessed aim to support the European Commission in the drafting of further pieces of legislation, as well as for plausible ad hoc requests in relation to CSF.

Mechanistic modelling of African swine fever: A systematic review

The spread of African swine fever (ASF) poses a grave threat to the global swine industry. Without an available vaccine, understanding transmission dynamics is essential for designing effective prevention, surveillance, and intervention strategies. These dynamics can often be unraveled through mechanistic modelling. To examine the assumptions on transmission and objectives of the mechanistic models of ASF, a systematic review of the scientific literature was conducted. Articles were examined across multiple epidemiological and model characteristics, with filiation between models determined through the creation of a neighbor-joined tree using phylogenetic software. Thirty-four articles qualified for inclusion, with four main modelling objectives identified: estimating transmission parameters (11 studies), assessing determinants of transmission (7), examining consequences of hypothetical outbreaks (5), assessing alternative control strategies (11). Population-based (17), metapopulation (5), and individual-based (12) model frameworks were represented, with population-based and metapopulation models predominantly used among domestic pigs, and individual-based models predominantly represented among wild boar. The majority of models (25) were parameterized to the genotype II isolates currently circulating in Europe and Asia. Estimated transmission parameters varied widely among ASFV strains, locations, and transmission scale. Similarly, parameter assumptions between models varied extensively. Uncertainties on epidemiological and ecological parameters were usually accounted for to assess the impact of parameter values on the modelled infection trajectory. To date, almost all models are host specific, being developed for either domestic pigs or wild boar despite the fact that spillover events between domestic pigs and wild boar are evidenced to play an important role in ASF outbreaks. Consequently, the development of more models incorporating such transmission routes is crucial. A variety of codified and hypothetical control strategies were compared however they were all a priori defined interventions. Future models, built to identify the optimal contributions across many control methods for achieving specific outcomes should provide more useful information for policy-makers. Further, control strategies were examined in competition with each other, which is opposed to how they would actually be synergistically implemented. While comparing strategies is beneficial for identifying a rank-order efficacy of control methods, this structure does not necessarily determine the most effective combination of all available strategies. In order for ASFV models to effectively support decision-making in controlling ASFV globally, these modelling limitations need to be addressed.

Scientific Opinion on the assessment of the control measures of the category A diseases of Animal Health Law: African Swine Fever

EFSA received a mandate from the European Commission to assess the effectiveness of some of the control measures against diseases included in the Category A list according to Regulation (EU) 2016/429 on transmissible animal diseases ('Animal Health Law'). This opinion belongs to a series of opinions where these control measures will be assessed, with this opinion covering the assessment of control measures for African Swine Fever (ASF). In this opinion, EFSA and the AHAW Panel of experts reviewed the effectiveness of: (i) clinical and laboratory sampling procedures, (ii) monitoring period and (iii) the minimum radius of the protection and surveillance zone, and the minimum length of time the measures should be applied in these zones. The general methodology used for this series of opinions has been published elsewhere; nonetheless, specific details of the model used for the assessment of the laboratory sampling procedures for ASF are presented here. Here, also, the transmission kernels used for the assessment of the minimum radius of the protection and surveillance zones are shown. Several scenarios for which these control measures had to be assessed were designed and agreed prior to the start of the assessment. In summary, several sampling procedures as described in the diagnostic manual for ASF were considered ineffective and a suggestion to exclude, or to substitute with more effective procedures was made. The monitoring period was assessed as non-effective for several scenarios and a longer monitoring period was suggested to ensure detection of potentially infected herds. It was demonstrated that the surveillance zone comprises 95% of the infections from an affected establishment, and therefore is considered effective. Recommendations provided for each of the scenarios assessed aim to support the European Commission in the drafting of further pieces of legislation, as well as for plausible ad hoc requests in relation to ASF.

Modelling infectious viral diseases in swine populations: a state of the art

Mathematical modelling is nowadays a pivotal tool for infectious diseases studies, completing regular biological investigations. The rapid growth of computer technology allowed for development of computational tools to address biological issues that could not be unravelled in the past. The global understanding of viral disease dynamics requires to account for all interactions at all levels, from within-host to between-herd, to have all the keys for development of control measures. A literature review was performed to disentangle modelling frameworks according to their major objectives and methodologies. One hundred and seventeen articles published between 1994 and 2020 were found to meet our inclusion criteria, which were defined to target papers representative of studies dealing with models of viral infection dynamics in pigs. A first descriptive analysis, using bibliometric indexes, permitted to identify keywords strongly related to the study scopes. Modelling studies were focused on particular infectious agents, with a shared objective: to better understand the viral dynamics for appropriate control measure adaptation. In a second step, selected papers were analysed to disentangle the modelling structures according to the objectives of the studies. The system representation was highly dependent on the nature of the pathogens. Enzootic viruses, such as swine influenza or porcine reproductive and respiratory syndrome, were generally investigated at the herd scale to analyse the impact of husbandry practices and prophylactic measures on infection dynamics. Epizootic agents (classical swine fever, foot-and-mouth disease or African swine fever viruses) were mostly studied using spatio-temporal simulation tools, to investigate the efficiency of surveillance and control protocols, which are predetermined for regulated diseases. A huge effort was made on model parameterization through the development of specific studies and methodologies insuring the robustness of parameter values to feed simulation tools. Integrative modelling frameworks, from within-host to spatio-temporal models, is clearly on the way. This would allow to capture the complexity of individual biological variabilities and to assess their consequences on the whole system at the population level. This would offer the opportunity to test and evaluate in silico the efficiency of possible control measures targeting specific epidemiological units, from hosts to herds, either individually or through their contact networks. Such decision support tools represent a strength for stakeholders to help mitigating infectious diseases dynamics and limiting economic consequences.

Inferences about the transmission of lumpy skin disease virus between herds from outbreaks in Albania in 2016

Lumpy skin disease has recently emerged as a major threat to cattle populations outside of Africa, where it is endemic. In 2015 the first ever European outbreaks occurred in Greece, which were followed by spread across much of the Balkans in 2016. Here we use a simple mathematical model for the transmission of lumpy skin disease virus (LSDV) between herds to explore factors influencing its spread by fitting it to data on outbreaks in Albania in 2016. We show that most transmission occurs over short distances (<5 km), but with an appreciable probability of transmission at longer distances. We also show that there is evidence for seasonal variation in the force of infection associated with temperature, possibly through its influence on the relative abundance of the stable fly, Stomoxys calcitrans. These two results together are consistent with LSDV being transmitted by the bites of blood-feeding insects, though further work is required to incriminate specific species as vectors. Finally, we show that vaccination has a significant impact on spread and estimate the vaccine effectiveness to be 76%.

Estimation of infection risk on pig farms in infected wild boar areas-Epidemiological analysis for the reemergence of classical swine fever in Japan in 2018

In September 2018, classical swine fever (CSF) reemerged in Japan after 26 years' absence. The first case was detected at a pig farm in Gifu Prefecture, in the center of Japan, and the disease spread to both domestic pigs and wild boar (Sus scrofa). The spread of CSF in wild boar is extremely difficult to control and is thus a great threat to domestic pig farms, and understanding the transmission risk from wild boar to domestic pigs is essential to implement effective control measures that will prevent domestic pig infection. Therefore, this study elucidates the transmission risk from wild boar to domestic pigs by introducing a transmission kernel that is dependent on the distance between infected wild boar and pig farms, and then estimating the risk area of infection from wild boar by describing the transmission probability. The study used epidemiological data from Gifu Prefecture in the period from September 2018 to March 2019, including a total of 171 1-km grid cells where an infected wild boar was detected and pig farm data from 13 infected and 34 uninfected farms. The estimated infection risk area within 28 days matched well with the observed data. The risk area widened gradually during the epidemic, and at the end of March, the risk area extended over a range of approximately 75 km from east to west and 40 km from north to south (almost 3000 km²). Ten out of the 13 infected farms and four out of the 34 uninfected farms were located within the high-risk area (>60 % infection probability). In contrast, one infected farm and 18 uninfected farms were located within the low-risk area (<5 % infection probability). When several infected grid cells were detected within 5 km of a pig farm, the risk of infection from wild boar within 28 days was more than 5 %. This analysis provides an estimate of the potential spatial range over which CSF virus can spread between wild boar and domestic pig farms, and can be used to inform the early detection of CSF-suspected pigs and the strengthening of biosecurity measures that will effectively prevent and control the disease based on the infection risk level.

Herd-level infectious disease surveillance of livestock populations using aggregate samples

All sectors of livestock production are in the process of shifting from small populations on many farms to large populations on fewer farms. A concurrent shift has occurred in the number of livestock moved across political boundaries. The unintended consequence of these changes has been the appearance of multifactorial diseases that are resistant to traditional methods of prevention and control. The need to understand complex animal health conditions mandates a shift toward the collection of longitudinal animal health data. Historically, collection of such data has frustrated and challenged animal health specialists. A promising trend in the evolution toward more efficient and effective livestock disease surveillance is the increased use of aggregate samples, e.g. bulk tank milk and oral fluid specimens. These sample types provide the means to monitor disease, estimate herd prevalence, and evaluate spatiotemporal trends in disease distribution. Thus, this article provides an overview of the use of bulk tank milk and pen-based oral fluids in the surveillance of livestock populations for infectious diseases.

Control fast or control smart: When should invading pathogens be controlled?

The intuitive response to an invading pathogen is to start disease management as rapidly as possible, since this would be expected to minimise the future impacts of disease. However, since more spread data become available as an outbreak unfolds, processes underpinning pathogen transmission can almost always be characterised more precisely later in epidemics. This allows the future progression of any outbreak to be forecast more accurately, and so enables control interventions to be targeted more precisely. There is also the chance that the outbreak might die out without any intervention whatsoever, making prophylactic control unnecessary. Optimal decision-making involves continuously balancing these potential benefits of waiting against the possible costs of further spread. We introduce a generic, extensible data-driven algorithm based on parameter estimation and outbreak simulation for making decisions in real-time concerning when and how to control an invading pathogen. The Control Smart Algorithm (CSA) resolves the trade-off between the competing advantages of controlling as soon as possible and controlling later when more information has become available. We show-using a generic mathematical model representing the transmission of a pathogen of agricultural animals or plants through a population of farms or fields-how the CSA allows the timing and level of deployment of vaccination or chemical control to be optimised. In particular, the algorithm outperforms simpler strategies such as intervening when the outbreak size reaches a pre-specified threshold, or controlling when the outbreak has persisted for a threshold length of time. This remains the case even if the simpler methods are fully optimised in advance. Our work highlights the potential benefits of giving careful consideration to the question of when to start disease management during emerging outbreaks, and provides a concrete framework to allow policy-makers to make this decision.

Estimation of the transmission dynamics of African swine fever virus within a swine house

The spread of African swine fever virus (ASFV) threatens to reach further parts of Europe. In countries with a large swine production, an outbreak of ASF may result in devastating economic consequences for the swine industry. Simulation models can assist decision makers setting up contingency plans. This creates a need for estimation of parameters. This study presents a new analysis of a previously published study. A full likelihood framework is presented including the impact of model assumptions on the estimated transmission parameters. As animals were only tested every other day, an interpretation was introduced to cover the weighted infectiousness on unobserved days for the individual animals (WIU). Based on our model and the set of assumptions, the within- and between-pen transmission parameters were estimated to βw = 1·05 (95% CI 0·62–1·72), βb = 0·46 (95% CI 0·17–1·00), respectively, and the WIU = 1·00 (95% CI 0–1). Furthermore, we simulated the spread of ASFV within a pig house using a modified SEIR-model to establish the time from infection of one animal until ASFV is detected in the herd. Based on a chosen detection limit of 2·55% equivalent to 10 dead pigs out of 360, the disease would be detected 13–19 days after introduction.

The use of vaccines to control pathogen spread in pig populations

Vaccine efficacy has often been studied from the viewpoint of individual direct clinical protection. For several vaccines, a decrease in pathogen shedding in vaccinated animals has also been documented, which suggests that transmission between individuals has the potential to be reduced. In addition, vaccination induces an immune response in the host potentially decreasing susceptibility to infection in comparison with immunologically naïve animals. As a collective result of individual vaccinations, vaccine programmes generally have a wider impact on pathogen diffusion at the population scale. Beyond the individual protection conferred by mass vaccination campaigns, the indirect protection of non-immune individuals in contact with vaccinated ones also contributes to controlling pathogen spread at the population scale; a phenomenon known as herd immunity.

Pathogen spread within pig populations is strongly related to the required vaccine coverage at the population level and to pathogen characteristics in terms of diffusion (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {R}_0 $$\end{document}R0). Before setting up vaccination programmes, it is therefore necessary to have quantitative knowledge on vaccine efficacy as regards transmission reduction. These data can be obtained by carrying out experimental studies or observational protocols in real conditions. These quantitative data have mainly been estimated for major infectious diseases which have now been eradicated. A great gap in knowledge has however been identified for enzootic diseases which are daily impacting the swine sector as well as for the source of variation responsible for a decrease in vaccine efficacy as compared to assessments obtained in experimental conditions.

Data-Driven Risk Assessment from Small Scale Epidemics: Estimation and Model Choice for Spatio-Temporal Data with Application to a Classical Swine Fever Outbreak

Livestock epidemics have the potential to give rise to significant economic, welfare, and social costs. Incursions of emerging and re-emerging pathogens may lead to small and repeated outbreaks. Analysis of the resulting data is statistically challenging but can inform disease preparedness reducing potential future losses. We present a framework for spatial risk assessment of disease incursions based on data from small localized historic outbreaks. We focus on between-farm spread of livestock pathogens and illustrate our methods by application to data on the small outbreak of Classical Swine Fever (CSF) that occurred in 2000 in East Anglia, UK. We apply models based on continuous time semi-Markov processes, using data-augmentation Markov Chain Monte Carlo techniques within a Bayesian framework to infer disease dynamics and detection from incompletely observed outbreaks. The spatial transmission kernel describing pathogen spread between farms, and the distribution of times between infection and detection, is estimated alongside unobserved exposure times. Our results demonstrate inference is reliable even for relatively small outbreaks when the data-generating model is known. However, associated risk assessments depend strongly on the form of the fitted transmission kernel. Therefore, for real applications, methods are needed to select the most appropriate model in light of the data. We assess standard Deviance Information Criteria (DIC) model selection tools and recently introduced latent residual methods of model assessment, in selecting the functional form of the spatial transmission kernel. These methods are applied to the CSF data, and tested in simulated scenarios which represent field data, but assume the data generation mechanism is known. Analysis of simulated scenarios shows that latent residual methods enable reliable selection of the transmission kernel even for small outbreaks whereas the DIC is less reliable. Moreover, compared with DIC, model choice based on latent residual assessment correlated better with predicted risk.

Vulnerability of the British swine industry to classical swine fever

Classical swine fever (CSF) is a notifiable, highly contagious viral disease of swine which results in severe welfare and economic consequences in affected countries. To improve preparedness, it is critical to have some understanding of how CSF would spread should it be introduced. Based on the data recorded during the 2000 epidemic of CSF in Great Britain (GB), a spatially explicit, premises-based model was developed to explore the risk of CSF spread in GB. We found that large outbreaks of CSF would be rare and generated from a limited number of areas in GB. Despite the consistently low vulnerability of the British swine industry to large CSF outbreaks, we identified concerns with respect to the role played by the non-commercial sector of the industry. The model further revealed how various epidemiological features may influence the spread of CSF in GB, highlighting the importance of between-farm biosecurity in preventing widespread dissemination of the virus. Knowledge of factors affecting the risk of spread are key components for surveillance planning and resource allocation, and this work provides a valuable stepping stone in guiding policy on CSF surveillance and control in GB.

Control of African swine fever epidemics in industrialized swine populations

African swine fever (ASF) is a notifiable infectious disease with a high impact on swine health. The disease is endemic in certain regions in the Baltic countries and has spread to Poland constituting a risk of ASF spread toward Western Europe. Therefore, as part of contingency planning, it is important to explore strategies that can effectively control an epidemic of ASF. In this study, the epidemiological and economic effects of strategies to control the spread of ASF between domestic swine herds were examined using a published model (DTU-DADS-ASF). The control strategies were the basic EU and national strategy (Basic), the basic strategy plus pre-emptive depopulation of neighboring swine herds, and intensive surveillance of herds in the control zones, including testing live or dead animals. Virus spread via wild boar was not modelled. Under the basic control strategy, the median epidemic duration was predicted to be 21days (5th and 95th percentiles; 1-55days), the median number of infected herds was predicted to be 3 herds (1-8), and the total costs were predicted to be €326 million (€256-€442 million). Adding pre-emptive depopulation or intensive surveillance by testing live animals resulted in marginal improvements to the control of the epidemics. However, adding testing of dead animals in the protection and surveillance zones was predicted to be the optimal control scenario for an ASF epidemic in industrialized swine populations without contact to wild boar. This optimal scenario reduced the epidemic duration to 9days (1-38) and the total costs to €294 million (€257-€392 million). Export losses were the driving force of the total costs of the epidemics.

Simulating the epidemiological and economic effects of an African swine fever epidemic in industrialized swine populations

African swine fever (ASF) is a notifiable infectious disease with a considerable impact on animal health and is currently one of the most important emerging diseases of domestic pigs. ASF was introduced into Georgia in 2007 and subsequently spread to the Russian Federation and several Eastern European countries. Consequently, there is a non-negligible risk of ASF spread towards Western Europe. Therefore it is important to develop tools to improve our understanding of the spread and control of ASF for contingency planning. A stochastic and dynamic spatial spread model (DTU-DADS) was adjusted to simulate the spread of ASF virus between domestic swine herds exemplified by the Danish swine population. ASF was simulated to spread via animal movement, low- or medium-risk contacts and local spread. Each epidemic was initiated in a randomly selected herd - either in a nucleus herd, a sow herd, a randomly selected herd or in multiple herds simultaneously. A sensitivity analysis was conducted on input parameters. Given the inputs and assumptions of the model, epidemics of ASF in Denmark are predicted to be small, affecting about 14 herds in the worst-case scenario. The duration of an epidemic is predicted to vary from 1 to 76days. Substantial economic damages are predicted, with median direct costs and export losses of €12 and €349 million, respectively, when epidemics were initiated in multiple herds. Each infectious herd resulted in 0 to 2 new infected herds varying from 0 to 5 new infected herds, depending on the index herd type.

Simulation of Spread of African Swine Fever, Including the Effects of Residues from Dead Animals

To study the spread of African swine fever (ASF) within a pig unit and the impact of unit size on ASF spread, a simulation model was created. In the model, an animal can be in one of the following stages: susceptible, latent, subclinical, clinical, or recovered. Animals can be infectious during the subclinical stage and are fully infectious during the clinical stage. ASF virus (ASFV) infection through residues of dead animals in the slurries was also modeled in an exponentially fading-out pattern. Low and high transmission rates for ASFV were tested in the model. Robustness analysis was carried out in order to study the impact of uncertain parameters on model predictions. The results showed that the disease may fade out within the pig unit without a major outbreak. Furthermore, they showed that spread of ASFV is dependent on the infectiousness of subclinical animals and the residues of dead animals, the transmission rate of the virus, and importantly the unit size. Moreover, increasing the duration of the latent or the subclinical stages resulted in longer time to disease fade out. The proposed model is a simple and robust tool simulating the spread of ASFV within a pig house taking into account dynamics of ASFV spread and the unit size. The tool can be implemented in simulation models of ASFV spread between herds.

Using national movement databases to help inform responses to swine disease outbreaks in Scotland: the impact of uncertainty around incursion time

Modelling is an important component of contingency planning and control of disease outbreaks. Dynamic network models are considered more useful than static models because they capture important dynamic patterns of farm behaviour as evidenced through animal movements. This study evaluates the usefulness of a dynamic network model of swine fever to predict pre-detection spread via movements of pigs, when there may be considerable uncertainty surrounding the time of incursion of infection. It explores the utility and limitations of animal movement data to inform such models and as such, provides some insight into the impact of improving traceability through real-time animal movement reporting and the use of electronic animal movement databases. The study concludes that the type of premises and uncertainty of the time of disease incursion will affect model accuracy and highlights the need for improvements in these areas.

Classical swine fever virus marker vaccine strain CP7_E2alf: genetic stability in vitro and in vivo

Recently, CP7_E2alf (SuvaxynCSF Marker), a live marker vaccine against classical swine fever virus, was licensed through the European Medicines Agency. For application of such a genetically engineered virus under field conditions, knowledge about its genetic stability is essential. Here, we report on stability studies that were conducted to assess and compare the mutation rate of CP7_E2alf in vitro and in vivo. Sequence analyses upon passaging confirmed the high stability of CP7_E2alf, and no recombination events were observed in the experimental setup. The data obtained in this study confirm the genetic stability of CP7_E2alf as an important safety component.

Controlling highly pathogenic avian influenza outbreaks: An epidemiological and economic model analysis

Outbreaks of highly pathogenic avian influenza (HPAI) can cause large losses for the poultry sector and for animal disease controlling authorities, as well as risks for animal and human welfare. In the current simulation approach epidemiological and economic models are combined to compare different strategies to control highly pathogenic avian influenza in Dutch poultry flocks. Evaluated control strategies are the minimum EU strategy (i.e., culling of infected flocks, transport regulations, tracing and screening of contact flocks, establishment of protection and surveillance zones), and additional control strategies comprising pre-emptive culling of all susceptible poultry flocks in an area around infected flocks (1 km, 3 km and 10 km) and emergency vaccination of all flocks except broilers around infected flocks (3 km). Simulation results indicate that the EU strategy is not sufficient to eradicate an epidemic in high density poultry areas. From an epidemiological point of view, this strategy is the least effective, while pre-emptive culling in 10 km radius is the most effective of the studied strategies. But these two strategies incur the highest costs due to long duration (EU strategy) and large-scale culling (pre-emptive culling in 10 km radius). Other analysed pre-emptive culling strategies (i.e., in 1 km and 3 km radius) are more effective than the analysed emergency vaccination strategy (in 3 km radius) in terms of duration and size of the epidemics, despite the assumed optimistic vaccination capacity of 20 farms per day. However, the total costs of these strategies differ only marginally. Extending the capacity for culling substantially reduces the duration, size and costs of the epidemic. This study demonstrates the strength of combining epidemiological and economic model analysis to gain insight in a range of consequences and thus to serve as a decision support tool in the control of HPAI epidemics.

Efficacy of CSF vaccine CP7_E2alf in piglets with maternally derived antibodies

There is a need for live DIVA (differentiating infected from vaccinated animals) vaccines against classical swine fever (CSF). The aim of this study was to investigate whether vaccination with the chimeric pestivirus vaccine CP7_E2alf is efficacious to protect young piglets born from vaccinated sows, thus with maternally derived antibodies (MDAs). Groups of 10 piglets each, with or without MDAs, were vaccinated either intramuscularly (IM), at an age of 3 or 6 weeks, or orally (OR), at an age of 6 weeks. Five piglets of each group were challenged with CSFV strain Koslov and protection against clinical disease, virus shedding and transmission were studied. Vaccination with CP7_E2alf, both in the presence of MDA's and in piglets without MDA's, protected against severe clinical signs, but virus shedding from most inoculated piglets and transmission to contact pigs was observed. However, virus transmission in the vaccinated piglets was significantly reduced as compared to non-vaccinated piglets, although the reproduction ratio's R calculated from the results in the vaccinated pigs from our study were not yet significantly below 1. The efficacy of vaccination with CP7_E2alf in the presence of MDAs (R IMvac=0.8, R ORvac=0.4) seemed to be slightly less as compared to vaccination in the absence of MDAs (R IMvac=0.2, R ORvac=0). On a population level, the results suggest that the CP7_E2alf vaccine is an effective tool in the control and eradication of CSF and, moreover, can be applied for both IM and oral use for young age groups, with MDAs having a limited effect on the efficacy.

Agroterrorism targeting livestock: a review with a focus on early detection systems

Agroterrorism targeting livestock can be described as the intentional introduction of an animal disease agent against livestock with the purpose of causing economic damage, disrupting socioeconomic stability of a country, and creating panic and distress. This type of terrorism can be alluring to terrorists because animal disease agents are easily available. This review addresses the vulnerabilities of the livestock industry to agroterrorism. However, we also show that early detection systems have recently been developed for agroterrorism and deliberate spread of animal pathogens in livestock, including an agroterrorism intelligence cycle, syndromic surveillance programs, and computer-based clinical decision support systems that can be used for early detection of notifiable animal diseases. The development of DIVA-vaccines in the past 10 to 15 years has created, in principle, an excellent response instrument to counter intentional animal disease outbreaks. These developments have made our animal agriculture less vulnerable to agroterrorism. But we cannot relax; there are still many challenges, in particular with respect to integration of first line of defense, law enforcement, and early detection systems for animal diseases.

How commercial and non-commercial swine producers move pigs in Scotland: a detailed descriptive analysis

BACKGROUND

The impact of non-commercial producers on disease spread via livestock movement is related to their level of interaction with other commercial actors within the industry. Although understanding these relationships is crucial in order to identify likely routes of disease incursion and transmission prior to disease detection, there has been little research in this area due to the difficulties of capturing movements of small producers with sufficient resolution. Here, we used the Scottish Livestock Electronic Identification and Traceability (ScotEID) database to describe the movement patterns of different pig production systems which may affect the risk of disease spread within the swine industry. In particular, we focused on the role of small pig producers.

RESULTS

Between January 2012 and May 2013, 23,169 batches of pigs were recorded moving animals between 2382 known unique premises. Although the majority of movements (61%) were to a slaughterhouse, the non-commercial and the commercial sectors of the Scottish swine industry coexist, with on- and off-movement of animals occurring relatively frequently. For instance, 13% and 4% of non-slaughter movements from professional producers were sent to a non-assured commercial producer or to a small producer, respectively; whereas 43% and 22% of movements from non-assured commercial farms were sent to a professional or a small producer, respectively. We further identified differences between producer types in several animal movement characteristics which are known to increase the risk of disease spread. Particularly, the distance travelled and the use of haulage were found to be significantly different between producers.

CONCLUSIONS

These results showed that commercial producers are not isolated from the non-commercial sector of the Scottish swine industry and may frequently interact, either directly or indirectly. The observed patterns in the frequency of movements, the type of producers involved, the distance travelled and the use of haulage companies provide insights into the structure of the Scottish swine industry, but also highlight different features that may increase the risk of infectious diseases spread in both Scotland and the UK. Such knowledge is critical for developing more robust biosecurity and surveillance plans and better preparing Scotland against incursions of emerging swine diseases.

The influence of between-farm distance and farm size on the spread of classical swine fever during the 1997-1998 epidemic in The Netherlands

As the size of livestock farms in The Netherlands is on the increase for economic reasons, an important question is how disease introduction risks and risks of onward transmission scale with farm size (i.e. with the number of animals on the farm). Here we use the epidemic data of the 1997-1998 epidemic of Classical Swine Fever (CSF) Virus in The Netherlands to address this question for CSF risks. This dataset is one of the most powerful ones statistically as in this epidemic a total of 428 pig farms where infected, with the majority of farm sizes ranging between 27 and 1750 pigs, including piglets. We have extended the earlier models for the transmission risk as a function of between-farm distance, by adding two factors. These factors describe the effect of farm size on the susceptibility of a 'receiving' farm and on the infectivity of a 'sending' farm (or 'source' farm), respectively. Using the best-fitting model, we show that the size of a farm has a significant influence on both farm-level susceptibility and infectivity for CSF. Although larger farms are both more susceptible to CSF and, when infected, more infectious to other farms than smaller farms, the increase is less than linear. The higher the farm size, the smaller the effect of increments of farm size on the susceptibility and infectivity of a farm. Because of changes in the Dutch pig farming characteristics, a straightforward extrapolation of the observed farm size dependencies from 1997/1998 to present times would not be justified. However, based on our results one may expect that also for the current pig farming characteristics in The Netherlands, farm susceptibility and infectivity depend non-linearly on farm size, with some saturation effect for relatively large farm sizes.

The potential of antiviral agents to control classical swine fever: a modelling study

Classical swine fever (CSF) represents a continuous threat to pig populations that are free of disease without vaccination. When CSF virus is introduced, the minimal control strategy imposed by the EU is often insufficient to mitigate the epidemic. Additional measures such as preemptive culling encounter ethical objections, whereas emergency vaccination leads to prolonged export restrictions. Antiviral agents, however, provide instantaneous protection without inducing an antibody response. The use of antiviral agents to contain CSF epidemics is studied with a model describing within- and between-herd virus transmission. Epidemics are simulated in a densely populated livestock area in The Netherlands, with farms of varying sizes and pig types (finishers, piglets and sows). Our results show that vaccination and/or antiviral treatment in a 2 km radius around an infected herd is more effective than preemptive culling in a 1 km radius. However, the instantaneous but temporary protection provided by antiviral treatment is slightly less effective than the delayed but long-lasting protection offered by vaccination. Therefore, the most effective control strategy is to vaccinate animals when allowed (finishers and piglets) and to treat with antiviral agents when vaccination is prohibited (sows). As independent control measure, antiviral treatment in a 1 km radius presents an elevated risk of epidemics running out of control. A 2 km control radius largely eliminates this risk.

Efficacy of chimeric Pestivirus vaccine candidates against classical swine fever: protection and DIVA characteristics

Currently no live DIVA (Differentiating Infected from Vaccinated Animals) vaccines against classical swine fever (CSF) are available. The aim of this study was to investigate whether chimeric pestivirus vaccine candidates (CP7_E2alf, Flc11 and Flc9) are able to protect pigs against clinical signs, and to reduce virus shedding and virus transmission, after a challenge with CSF virus (CSFV), 7 or 14 days after a single intramuscular vaccination. In these vaccine candidates, either the E2 or the E(rns) encoding genome region of a bovine viral diarrhoea virus strain were combined with a cDNA copy of CSFV or vice versa. Furthermore, currently available serological DIVA tests were evaluated. The vaccine candidates were compared to the C-strain. All vaccine candidates protected against clinical signs. No transmission to contact pigs was detected in the groups vaccinated with C-strain, CP7_E2alf and Flc11. Limited transmission occurred in the groups vaccinated with Flc9. All vaccine candidates would be suitable to stop on-going transmission of CSFV. For Flc11, no reliable differentiation was possible with the current E(rns)-based DIVA test. For CP7_E2alf, the distribution of the inhibition percentages was such that up to 5% false positive results may be obtained in a large vaccinated population. For Flc9 vaccinated pigs, the E2 ELISA performed very well, with an expected 0.04% false positive results in a large vaccinated population. Both CP7_E2alf and Flc9 are promising candidates to be used as live attenuated marker vaccines against CSF, with protection the best feature of CP7_E2alf, and the DIVA principle the best feature of Flc9.

Scaling from challenge experiments to the field: Quantifying the impact of vaccination on the transmission of bluetongue virus serotype 8

Bluetongue (BT) is an economically important disease of ruminants caused by bluetongue virus (BTV) and transmitted by Culicoides biting midges. The most practical and effective way to protect susceptible animals against BTV is by vaccination. Data from challenge studies in calves and sheep conducted by Intervet International b.v., in particular, presence of viral RNA in the blood of challenged animals, were used to estimate vaccine efficacy. The results of the challenge studies for calves indicated that vaccination is likely to reduce the basic reproduction number (R(0)) for BTV in cattle to below one (i.e. prevent major outbreaks within a holding) and that this reduction is robust to uncertainty in the model parameters. Sensitivity analysis showed that the whether or not vaccination is predicted to reduce R(0) to below one depended on the following assumptions: (i) whether "doubtful" results from the challenge studies are treated as negative or positive; (ii) whether or not the probability of transmission from host to vector is reduced by vaccination; and (iii) whether the extrinsic incubation period follows a realistic gamma distribution or the more commonly used exponential distribution. For sheep, all but one of the vaccinated animals were protected and, consequently, vaccination will consistently reduce R(0) in sheep to below one. Using a stochastic spatial model for the spread of BTV in Great Britain (GB), vaccination was predicted to reduce both the incidence of disease and spatial spread in simulated BTV outbreaks in GB, in both reactive vaccination strategies and when an incursion occurred into a previously vaccinated population.

Vaccination against foot-and-mouth disease I: epidemiological consequences

An epidemic of foot-and-mouth disease (FMD) can have devastating effects on animal welfare, economic revenues, the export position and society as a whole, as occurred during the 2001 FMD epidemic in the Netherlands. Following the preemptive culling of 260,000 animals during this outbreak, the Dutch government adopted emergency vaccination as preferred control policy. However, a vaccination-to-live strategy has not been applied before, posing unprecedented challenges for effectively controlling the epidemic, regaining FMD-free status and minimizing economic losses. These three topics are covered in an interdisciplinary model analysis. In this first part we evaluate whether and how emergency vaccination can be effectively applied to control FMD epidemics in the Netherlands. For this purpose we develop a stochastic individual-based model that describes FMD virus transmission between animals and between herds, taking heterogeneity between host species (cattle, sheep and pigs) into account. Our results in a densely populated livestock area with >4 farms/km(2) show that emergency ring vaccination can halt the epidemic as rapidly as preemptive ring culling, while the total number of farms to be culled is reduced by a factor of four. To achieve this reduction a larger control radius around detected farms and a corresponding adequate vaccination capacity is needed. Although sufficient for the majority of simulated epidemics with a 2 km vaccination zone, the vaccination capacity available in the Netherlands can be exhausted by pig farms that are on average ten times larger than cattle herds. Excluding pig farms from vaccination slightly increases the epidemic, but more than halves the number of animals to be vaccinated. Hobby flocks - modelled as small-sized sheep flocks - do not play a significant role in propagating the epidemic, and need not be targeted during the control phase. In a more sparsely populated livestock area in the Netherlands with about 2 farms/km(2) the minimal control strategy of culling only detected farms seems sufficient to control an epidemic.

Comparison of pre-emptive and reactive strategies to control an incursion of bluetongue virus serotype 1 to Great Britain by vaccination

Bluetongue (BT) is a disease of ruminants caused by bluetongue virus (BTV), which is spread between its hosts by Culicoides midges. Vaccination is the most effective way to protect susceptible animals against BTV and was used reactively to control the recent northern European outbreak. To assess the consequences of using vaccination pre-emptively we used a stochastic, spatially explicit model to compare reactive and pre-emptive vaccination strategies against an incursion of BTV serotype 1 (BTV-1) into Great Britain. Both pre-emptive and reactive vaccination significantly reduced the number of affected farms and limited host morbidity and mortality. In addition, vaccinating prior to the introduction of disease reduced the probability of an outbreak occurring. Of the strategies simulated, widespread reactive vaccination resulted in the lowest levels of morbidity. The predicted effects of vaccination were found to be sensitive to vaccine efficacy but not to the choice of transmission kernel.

Challenge of pigs with classical swine fever viruses after C-strain vaccination reveals remarkably rapid protection and insights into early immunity

Pre-emptive culling is becoming increasingly questioned as a means of controlling animal diseases, including classical swine fever (CSF). This has prompted discussions on the use of emergency vaccination to control future CSF outbreaks in domestic pigs. Despite a long history of safe use in endemic areas, there is a paucity of data on aspects important to emergency strategies, such as how rapidly CSFV vaccines would protect against transmission, and if this protection is equivalent for all viral genotypes, including highly divergent genotype 3 strains. To evaluate these questions, pigs were vaccinated with the Riemser® C-strain vaccine at 1, 3 and 5 days prior to challenge with genotype 2.1 and 3.3 challenge strains. The vaccine provided equivalent protection against clinical disease caused by for the two challenge strains and, as expected, protection was complete at 5 days post-vaccination. Substantial protection was achieved after 3 days, which was sufficient to prevent transmission of the 3.3 strain to animals in direct contact. Even by one day post-vaccination approximately half the animals were partially protected, and were able to control the infection, indicating that a reduction of the infectious potential is achieved very rapidly after vaccination. There was a close temporal correlation between T cell IFN-γ responses and protection. Interestingly, compared to responses of animals challenged 5 days after vaccination, challenge of animals 3 or 1 days post-vaccination resulted in impaired vaccine-induced T cell responses. This, together with the failure to detect a T cell IFN-γ response in unprotected and unvaccinated animals, indicates that virulent CSFV can inhibit the potent antiviral host defences primed by C-strain in the early period post vaccination.

Modelling effectiveness of herd level vaccination against Q fever in dairy cattle

Q fever is a worldwide zoonosis caused by the bacterium Coxiella burnetii. The control of this infection in cattle is crucial: infected ruminants can indeed encounter reproductive disorders and represent the most important source of human infection. In the field, vaccination is currently advised in infected herds but the comparative effectiveness of different vaccination protocols has never been explored: the duration of the vaccination programme and the category of animals to be vaccinated have to be determined. Our objective was to compare, by simulation, the effectiveness over 10 years of three different vaccination strategies in a recently infected dairy cattle herd.A stochastic individual-based epidemic model coupled with a model of herd demography was developed to simulate three temporal outputs (shedder prevalence, environmental bacterial load and number of abortions) and to calculate the extinction rate of the infection. For all strategies, the temporal outputs were predicted to strongly decrease with time at least in the first years of vaccination. However, vaccinating only three years was predicted inadequate to stabilize these dynamic outputs at a low level. Vaccination of both cows and heifers was predicted as being slightly more effective than vaccinating heifers only. Although the simulated extinction rate of the infection was high for both scenarios, the outputs decreased slower when only heifers were vaccinated.Our findings shed new light on vaccination effectiveness related to Q fever. Moreover, the model can be further modified for simulating and assessing various Q fever control strategies such as environmental and hygienic measures.

Using mortality data for early detection of Classical Swine Fever in The Netherlands

Early detection of the introduction of an infectious livestock disease is of great importance to limit the potential extent of an outbreak. Classical Swine Fever (CSF) often causes non-specific clinical signs, which can take considerable time to be detected. Currently, the disease can be detected by three main routes, that are all triggered by clinical signs. To improve the early detection of CSF an additional program, based on mortality data, aims to routinely perform PCR tests on ear notch samples from herds with a high(er) mortality. To assess the effectiveness of this new early detection system, we have developed a stochastic model that describes the virus transmission within a pig herd, the development of disease in infected animals and the different early detection programs. As virus transmission and mortality (by CSF and by other causes) are different for finishing pigs, piglets and sows, a distinction is made between these pig categories. The model is applied to an extensive database that contains all unique pig herds in The Netherlands, their herd sizes and their mortality reports over the CSF-free period 2001-2005. Results from the simulations suggest that the new early detection system is not effective in piglet sections, due to the high mortality from non-CSF causes, nor in sow sections, due to the low CSF-mortality. In finishing herds, the model predicts that the new early detection system can improve the detection time by two days, from 38 (27-53) days to 36 (24-51) days after virus introduction, when assuming a moderately virulent virus strain causing a 50% CSF mortality. For this result up to 5 ear notch samples per herd from 8 (0-13) finishing herds must be tested every workday. Detecting a source herd two days earlier could considerably reduce the number of initially infected herds. However, considering the variation in outcome and the uncertainty in some model assumptions, this two-day gain in detection time is too small to demonstrate a substantial effect of the new early detection system based on mortality data. But when the alertness of herd-owners and veterinarians diminishes during long CSF-free periods, the new early detection system might gain in effectiveness.

Reviewing model application to support animal health decision making

Animal health is of societal importance as it affects human welfare, and anthropogenic interests shape decision making to assure animal health. Scientific advice to support decision making is manifold. Modelling, as one piece of the scientific toolbox, is appreciated for its ability to describe and structure data, to give insight in complex processes and to predict future outcome. In this paper we study the application of scientific modelling to support practical animal health decisions. We reviewed the 35 animal health related scientific opinions adopted by the Animal Health and Animal Welfare Panel of the European Food Safety Authority (EFSA). Thirteen of these documents were based on the application of models. The review took two viewpoints, the decision maker's need and the modeller's approach. In the reviewed material three types of modelling questions were addressed by four specific model types. The correspondence between tasks and models underpinned the importance of the modelling question in triggering the modelling approach. End point quantifications were the dominating request from decision makers, implying that prediction of risk is a major need. However, due to knowledge gaps corresponding modelling studies often shed away from providing exact numbers. Instead, comparative scenario analyses were performed, furthering the understanding of the decision problem and effects of alternative management options. In conclusion, the most adequate scientific support for decision making - including available modelling capacity - might be expected if the required advice is clearly stated.

The role of movement restrictions and pre-emptive destruction in the emergency control strategy against CSF outbreaks in domestic pigs

Classical swine fever (CSF) outbreaks in domestic pig herds lead to the implementation of standard control measures according to legislative regulations. Ideal outbreak control entails the swift and efficient culling of all pigs on premises detected positive for CSF virus. Often all pig holdings around the detected cases are pre-emptively destroyed to exclude transmission into the neighbourhood. In addition to these measures, zones are defined in which surveillance and protection measures are intensified to prevent further distant disease spread. In particular, all movements are prohibited within standstill areas. Standstill also excludes the transport of fattened pigs to slaughter. Historical outbreaks provide evidence of the success of this control strategy. However, the extent to which the individual strategy elements contribute to this success is unknown. Therefore, we applied a spatially and temporally explicit epidemic model to the problem. Its rule-based formulation is tailored to a one-by-one model implementation of existing control concepts. Using a comparative model analysis the individual contributions of single measures to overall control success were revealed. From the results of the model we concluded that movement restrictions had the dominant impact on strategy performance suggesting a reversal of the current conceptual thinking. Additional measures such as pre-emptive culling only became relevant under imperfect compliance with movement restrictions. The importance of movement restrictions for the overall control success illustrates the need for explicit consideration of this measure when contingency strategies are being amended (e.g. emergency vaccination) and associated risks assessed.

A modeling framework to describe the transmission of bluetongue virus within and between farms in Great Britain

BACKGROUND

Recently much attention has been given to developing national-scale micro-simulation models for livestock diseases that can be used to predict spread and assess the impact of control measures. The focus of these models has been on directly transmitted infections with little attention given to vector-borne diseases such as bluetongue, a viral disease of ruminants transmitted by Culicoides biting midges. Yet BT has emerged over the past decade as one of the most important diseases of livestock.

METHODOLOGY/PRINCIPAL FINDINGS

We developed a stochastic, spatially-explicit, farm-level model to describe the spread of bluetongue virus (BTV) within and between farms. Transmission between farms was modeled by a generic kernel, which includes both animal and vector movements. Once a farm acquired infection, the within-farm dynamics were simulated based on the number of cattle and sheep kept on the farm and on local temperatures. Parameter estimates were derived from the published literature and using data from the outbreak of bluetongue in northern Europe in 2006. The model was validated using data on the spread of BTV in Great Britain during 2007. The sensitivity of model predictions to the shape of the transmission kernel was assessed.

CONCLUSIONS/SIGNIFICANCE

The model is able to replicate the dynamics of BTV in Great Britain. Although uncertainty remains over the precise shape of the transmission kernel and certain aspects of the vector, the modeling approach we develop constitutes an ideal framework in which to incorporate these aspects as more and better data become available. Moreover, the model provides a tool with which to examine scenarios for the spread and control of BTV in Great Britain.