Risk of foot-and-mouth disease associated with proximity in space and time to infected premises and the implications for control policy during the 2001 epidemic in Cumbria

Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

Mathematical models of infectious diseases are increasingly used to inform policy decisions. The advantages of such models are that multiple control options can be rapidly tested and compared, without the risks and costs associated with field experiments. However, for such models to be practically useful tools detailed data (both in terms of populations and epidemiology) are required. In many countries, such as the USA, individual-level demographic information on livestock farms is generally lacking. However, remotely sensed information (such as satellite images and land-use maps) provides the potential to generate these data or produce surrogate populations. In this paper we use land cover data to predict farm locations in the UK and investigate the effect of a precise knowledge of farm locations upon epidemiological predictions in the event of a foot-and-mouth disease epidemic. Our results show that, when highly resolved land cover data are used to predict farm locations, accurate predictions of epidemic sizes, durations and preferred intervention strategies can be obtained. This suggests that land cover data may be used in countries where individual farm-level data are not available, to allow for analyses to be carried out regarding the likely spread of disease in future outbreaks.

Connecting network properties of rapidly disseminating epizoonotics

BACKGROUND

To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure.

METHODS

Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) 'connectivity', a model that integrated bio-physical concepts (the agent's transmission cycle, road topology) into indicators designed to measure networks ('nodes' or infected sites with short- and long-range links), and 2) 'contacts', which focused on infected individuals but did not assess connectivity.

RESULTS

THE CONNECTIVITY MODEL SHOWED FIVE NETWORK PROPERTIES: 1) spatial aggregation of cases (disease clusters), 2) links among similar 'nodes' (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a "20:80" pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads.

CONCLUSIONS

Geo-temporal constructs of Network Theory's nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.

How to make predictions about future infectious disease risks

Formal, quantitative approaches are now widely used to make predictions about the likelihood of an infectious disease outbreak, how the disease will spread, and how to control it. Several well-established methodologies are available, including risk factor analysis, risk modelling and dynamic modelling. Even so, predictive modelling is very much the ‘art of the possible’, which tends to drive research effort towards some areas and away from others which may be at least as important. Building on the undoubted success of quantitative modelling of the epidemiology and control of human and animal diseases such as AIDS, influenza, foot-and-mouth disease and BSE, attention needs to be paid to developing a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behaviour to land use and climate change. At the same time, there is still considerable room for improvement in how quantitative analyses and their outputs are communicated to policy makers and other stakeholders. A starting point would be generally accepted guidelines for ‘good practice’ for the development and the use of predictive models.

Ongoing estimation of the epidemic parameters of a stochastic, spatial, discrete-time model for a 1983-84 avian influenza epidemic

We formulate a stochastic, spatial, discrete-time model of viral "Susceptible, Exposed, Infectious, Recovered" animal epidemics and apply it to an avian influenza epidemic in Pennsylvania in 1983-84. Using weekly data for the number of newly infectious cases collected during the epidemic, we find estimates for the latent period of the virus and the values of two parameters within the transmission kernel of the model. These data are then jackknifed on a progressive weekly basis to show how our estimates can be applied to an ongoing epidemic to generate continually improving values of certain epidemic parameters.

Spread of porcine circovirus associated disease (PCVAD) in Ontario (Canada) swine herds: Part I. Exploratory spatial analysis

Background

The systemic form of porcine circovirus associated disease (PCVAD), also known as postweaning multisystemic wasting syndrome (PMWS) was initially detected in the early 1990s. Starting in 2004, the Canadian swine industry experienced considerable losses due to PCVAD, concurrent with a shift in genotype of porcine circovirus type 2 (PCV2). Objectives of the current study were to explore spatial characteristics of self-reported PCVAD distribution in Ontario between 2004 and 2008, and to investigate the existence and nature of local spread.

Results

The study included 278 swine herds from a large disease-monitoring project that included porcine reproductive and respiratory syndrome (PRRS) virus-positive herds identified by the diagnostic laboratory, and PRRS virus-negative herds directly from the target population. Herds were included if they had growing pigs present on-site and available geographical coordinates for the sampling site. Furthermore, herds were defined as PCVAD-positive if a producer reported an outbreak of circovirus associated disease, or as PCVAD-negative if no outbreak was noted. Spatial trend was investigated using generalized additive models and time to PCVAD outbreak in a herd using Cox's proportional hazard model; spatial and spatio-temporal clustering was explored using K-functions; and location of most likely spatial and spatio-temporal clusters was investigated using scan statistics. Over the study period, the risk of reporting a PCVAD-positive herd tended to be higher in the eastern part of the province after adjustment for herd PRRS status (P = 0.05). This was partly confirmed for spread (Partial P < 0.01). Local spread also appeared to exist, as suggested by the tentative (P = 0.06) existence of spatio-temporal clustering of PCVAD and detection of a spatio-temporal cluster (P = 0.04).

Conclusions

In Ontario, PCVAD has shown a general trend, spreading from east-to-west. We interpret the existence of spatio-temporal clustering as evidence of spatio-temporal aggregation of PCVAD-positive cases above expectations and, together with the existence of spatio-temporal and spatial clusters, as suggestive of apparent local spread of PCVAD. Clustering was detected at small spatial and temporal scales. Other patterns of spread could not be detected; however, survival rates in discrete Ontario zones, as well as a lack of a clear spatial pattern in the most likely spatio-temporal clusters, suggest other between-herd transmission mechanisms.

Spread of porcine circovirus associated disease (PCVAD) in Ontario (Canada) swine herds: Part II. Matched case-control study

Background

The emergence of porcine circovirus associated disease (PCVAD) was associated with high mortality in swine populations worldwide. Studies performed in different regions identified spatial, temporal, and spatio-temporal trends as factors contributing to patterns of the disease spread. Patterns consistent with spatial trend and spatio-temporal clustering were already identified in this dataset. On the basis of these results, we have further investigated the nature of local spread in this report. The primary objective of this study was to evaluate risk factors for incidence cases of reported PCVAD.

Results

A time-matched case-control study was used as a study design approach, and conditional logistic regression as the analytical method. The main exposure of interest was local spread, which was defined as an unidentified mechanism of PCVAD spread between premises located within 3 kilometers of the Euclidean distance. Various modifications of variables indicative of local spread were also evaluated. The dataset contained 278 swine herds from Ontario originally sampled either from diagnostic laboratory submissions or directly from the target population. A PCVAD case was defined on the basis of the producer's recall. Existence of apparent local spread over the entire study period was confirmed (OR = 2.26, 95% CI: 1.06, 4.83), and was further identified to be time-varying in nature - herds experiencing outbreaks in the later part of the epidemic were more likely than control herds to be exposed to neighboring herds experiencing recent PCVAD outbreaks. More importantly, the pattern of local spread was driven by concurrent occurrence of PCVAD on premises under the same ownership (OR_{EXACTwithin ownership} = 25.6, 95% CI: 3.4, +inf; OR_{EXACToutside ownership} = 1.3, 95% CI: 0.45, 3.3). Other significant factors included PRRSv status of a herd (OR_EXACT = 1.9, 95% CI: 1.0, 3.9), after adjusting for geographical location by including the binary effect of the easting coordinate (Easting > 600 km = 1; OR_EXACT = 1.8, 95% CI: 0.5, 5.6).

Conclusions

These results preclude any conclusion regarding the existence of a mechanism of local spread through airborne transmission or indirectly through contaminated fomites or vectors, as simultaneous emergence of PCVAD could also be a result of concurrent change in contributing factors due to other mechanisms within ownerships.

Evaluation of the risk of neighbourhood infection of H7N1 Highly Pathogenic Avian Influenza in Italy using Q statistic

Exposure to the risk of neighbourhood infection was estimated for the H7N1 Highly Pathogenic Avian Influenza (HPAI) epidemic that affected Northern Italy between 1999 and 2000. The two most affected regions (Lombardy and Veneto) were analyzed and the epidemic was divided into three phases. Q statistics were used to evaluate exposure to the risk of neighbourhood infection using two measures. First, a local Q statistic (Qikt) assessed daily exposure for each farm as a function of the number of neighbouring infected farms that were in their infectious period, weighted by the distance between farms. This allowed us to identify the daily time course of risk for each farm and, at any given time, local groups of farms defined by high risk. Second, for each farm a summary statistic of exposure risk within each phase (Qiph) was obtained by summing Qikt over the duration of each phase. This allowed identification of farms defined by persistent, high exposure risk within each phase of the epidemic. Statistical significance was evaluated using conditional Monte Carlo simulation, and significant values of Qiph were mapped to assess the variation of the risk of neighbourhood infection through the phases. Qikt was larger for farms in Lombardy and the reduction of exposed farms was more marked for Veneto. Although the highest value of Qiph was observed in Veneto, in each phase most of the significant values were in Lombardy. In the last phase of the epidemic, a large reduction in the number of farms significantly exposed to the risk of neighbourhood infection was observed in the Veneto region, along with generally low values of Qiph. This may be explained by differences in control measures in the two regions, including pre-emptive slaughtering of farms considered at high risk of infection. The Q statistic allowed us to quantify geographic, time-dynamic variations in exposure to neighbourhood infection, and to generate hypotheses on the efficacy of control measures.

Evaluating the effectiveness of early vaccination in the control and eradication of equine influenza--a modelling approach

In August 2007, Australia which had previously been free of equine influenza, experienced a large outbreak that lasted approximately 4 months before it was eradicated. The outbreak required a significant national response by government and the horse industries. The main components of the response were movement controls, biosecurity measures, risk-based zoning and, subsequently, vaccination to contain the outbreak. Although not initially used, vaccination became a key element in the eradication program, with approximately 140000 horses vaccinated. Vaccination is recognised as a valuable tool for managing EI in endemically infected countries but there is little experience using it in situations where the objective is disease eradication. Vaccination was undoubtedly an important factor in 2007 as it enabled movements of some horses and associated industry activities to recommence. However, its contribution to containment and eradication is less clear. A premises-level equine influenza model, based on an epidemiological analysis of the 2007 outbreak, was developed to evaluate effectiveness of the mitigation strategies used and to investigate whether vaccination, if applied earlier, would have had an effect on the course of the outbreak. The results indicate that early use of strategic vaccination could have significantly reduced the size of the outbreak. The four vaccination strategies evaluated had, by 1 month into the control program, reduced the number of new infections on average by 60% and the size of the infected area by 8-9%. If resources are limited, a 1 km suppressive ring vaccination around infected premises gave the best results, but with greater vaccination capacity, a 3 km ring vaccination was the most effective strategy. The findings suggest that as well as reducing clinical and economic impacts, vaccination when used with biosecurity measures and movement controls could play an important role in the containment and eradication of equine influenza.

Impact of spatial clustering on disease transmission and optimal control

Spatial heterogeneities and spatial separation of hosts are often seen as key factors when developing accurate predictive models of the spread of pathogens. The question we address in this paper is how coarse the resolution of the spatial data can be for a model to be a useful tool for informing control policies. We examine this problem using the specific case of foot-and-mouth disease spreading between farms using the formulation developed during the 2001 epidemic in the United Kingdom. We show that, if our model is carefully parameterized to match epidemic behavior, then using aggregate county-scale data from the United States is sufficient to closely determine optimal control measures (specifically ring culling). This result also holds when the approach is extended to theoretical distributions of farms where the spatial clustering can be manipulated to extremes. We have therefore shown that, although spatial structure can be critically important in allowing us to predict the emergent population-scale behavior from a knowledge of the individual-level dynamics, for this specific applied question, such structure is mostly subsumed in the parameterization allowing us to make policy predictions in the absence of high-quality spatial information. We believe that this approach will be of considerable benefit across a range of disciplines where data are only available at intermediate spatial scales.

Analysis of local spread of equine influenza in the Park Ridge region of Queensland

In 2007, an incursion of equine influenza (EI) occurred in Australia. Accurate maps of property boundaries were used to examine the pattern and mechanism of local spread of EI. This study focussed on a cluster of infected premises (IPs) at Park Ridge, a peri-urban suburb 26 km south of Brisbane, Queensland. The cluster recorded 437 IPs and 81% of these were not contiguous to a previously IP. The mean distance from each new IP to the closest previous IP was 0.85 +/- 1.50 km with a range of 0.01-12.94 km. Eighty-two percent of new IPs were within 1 km of a previous IP. The spatial mean for each week's new IPs showed a consistent trend of movement from east to west throughout the epizootic consistent with the predominant wind patterns. The findings were consistent with the conclusion that EI will routinely spread over 1-2 km via wind-borne aerosol.

Vaccination reduced the incidence of outbreaks of low pathogenicity avian influenza in northern Italy

When outbreaks of avian influenza (AI) occur in poultry populations, the main goal to achieve is the control and eradication of the infection. However, quantitative information on risk factors for AI spread and efficacy of AI control measures such as vaccination in the field is limited. From 2000 to 2005, H5 and H7 low pathogenicity (LP) AI viruses caused four epidemics in poultry in northeastern Italy. Italian veterinary authorities implemented emergency vaccination in the 2000-2001 and 2002-2003 LPAI epidemics and prophylactic vaccination from July 2004. The aim of this study is to quantitatively evaluate the efficacy of AI vaccination in the field, taking into account the different strategies (emergency and prophylactic) implemented. Moreover, risk factors for LPAI spread in domestic poultry were studied. By survival analysis, we observed a two-fold increase in survival probability for vaccinated poultry farms compared to unvaccinated ones. In meat turkeys, vaccination protocols changed in the different epidemics, and a relationship between protection and the number of vaccinations was observed; two or three vaccine administrations protected flocks from LPAI, whilst four administrations did not significantly reduce the risk of infection. In meat turkeys the risk of AI infection increased also with the increase in both farm size and proximity to an infected farm. In general, we observed a lower number of outbreaks and a faster eradication of the infection when LPAI viruses introduced in a preventively vaccinated poultry population. This study provides insights on LPAI vaccination efficacy and on risk factors involved in LPAI infection at farm level. To our knowledge, this is the first study which quantitatively evaluates AI vaccination efficacy and compares different vaccination strategies and protocols using field data.

Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus

Estimating detailed transmission trees that reflect the relationships between infected individuals or populations during a disease outbreak often provides valuable insights into both the nature of disease transmission and the overall dynamics of the underlying epidemiological process. These trees may be based on epidemiological data that relate to the timing of infection and infectiousness, or genetic data that show the genetic relatedness of pathogens isolated from infected individuals. Genetic data are becoming increasingly important in the estimation of transmission trees of viral pathogens due to their inherently high mutation rate. Here, we propose a maximum-likelihood approach that allows epidemiological and genetic data to be combined within the same analysis to infer probable transmission trees. We apply this approach to data from 20 farms infected during the 2001 UK foot-and-mouth disease outbreak, using complete viral genome sequences from each infected farm and information on when farms were first estimated to have developed clinical disease and when livestock on these farms were culled. Incorporating known infection links due to animal movement prior to imposition of the national movement ban results in the reduction of the number of trees from 41472 that are consistent with the genetic data to 1728, of which just 4 represent more than 95% of the total likelihood calculated using a model that accounts for the epidemiological data. These trees differ in several ways from those constructed prior to the availability of genetic data.

Transmission parameters of highly pathogenic avian influenza (H7N1) among industrial poultry farms in northern Italy in 1999–2000

We estimated between-farm transmission parameters of the highly pathogenic avian-influenza (HPAI) epidemic that struck the poultry industry of northern Italy (including turkeys, layer hens, broilers, gamebirds, and waterfowl) from December 1999 through April 2000. We estimated the average number of susceptible farms that were infected with HPAI virus by each infectious farm during a day (beta) with a generalised linear model (GLM). The HPAI's reproductive ratios (R(h); the average number of new infected farms (IFs) that were caused by an infectious farm) were calculated separately for the regions of Lombardy and Veneto, where 382 out of 413 (92.5%) of IFs were located. In both regions, R(h) decreased to approximately 1 during the second month of the epidemic (showing that its containment had been initiated). Subsequently, during the last two months of the epidemic, beta and R(h) were reduced to 0.04/day and 0.6, respectively, in Veneto and to 0.07/day and 0.8 in Lombardy. The reduction of the susceptible population through strict control measures, including pre-emptive slaughter of at-risk poultry flocks, was implemented to a greatest extent in Veneto and this might have been associated with a more rapid control of the epidemic in this region than in Lombardy.

Spatial analysis of the 1999-2000 highly pathogenic avian influenza (H7N1) epidemic in northern Italy

The effect of proximity on infected premises was evaluated during the highly pathogenic avian influenza (HPAI) epidemic that struck northern Italy in 1999-2000 by quantifying the spatial and temporal clustering of cases. The epidemic was caused by an H7N1 subtype of type A influenza virus that originated from a low-pathogenic AI virus that spread among poultry farms in northeastern Italy in 1999 and eventually became virulent by mutation. More than 90% of 413 infected premises were located in Lombardy and Veneto regions; of 382 outbreaks, 60% occurred in the Lombardy region and 40% in the Veneto region. Global and local spatial statistics were used to estimate the location and degree of clustering of cases with respect to the population at risk. Outbreaks were spatially clustered primarily in Lombardy, with a large cluster in Brescia province and another in Mantua province, on the border of Veneto. Time series analysis was used to assess the temporal clustering of outbreaks. Temporal aggregation increased during the first 5 wk and decreased thereafter (probably as a result of eradication measures enforced in the Veneto region). Spatio-temporal clustering was assessed considering the Temporal Risk Window (TRW), the time period during which premises remain infectious and infection can spread to neighboring premises. The clustering pattern was similar to the one detected when considering spatial clustering (i.e., the larger clusters were identified in the Brescia and Mantua provinces of Lombardy). These results highlight the role of proximity in the spread of AI virus and, when considering the TRW, indicate the possible direction of virus spread.

Space–time interaction as an indicator of local spread during the 2001 FMD outbreak in the UK

During the 2001 FMD outbreak in the UK, decisions on the level of implementation of control measures were supported by predictive models. Models were mainly used as macro-level tools to predict the behaviour of the disease in the whole country rather than at the local level. Here we explore the use of the magnitude and characteristics of the space-time interaction as an indicator of local spread and, indirectly, of the effectiveness of control measures aimed at reducing short-range transmission during the course of a major livestock disease epidemic. The spatiotemporal evolution patterns are described in the four main clusters that were observed during the outbreak by means of the hazard rate and space-time K-function (K(s,t)). For each local outbreak, the relative measure D(0)(s,t), derived from K(s,t), which represents the excess risk attributable to the space-time interaction was calculated for consecutive 20-day temporal windows to represent the dynamics of the space-time interaction. The dynamics of the spatiotemporal interaction were very different among the four local clusters, suggesting that the intensity of local spread, and therefore the effectiveness of control measures, markedly differed between local outbreaks. The large heterogeneity observed in the relative impact of being close in time and space to an infected premises suggests that the decision making in relation to control of the outbreak would have benefited from indicators of local spread which could be used to complement global predictive modelling results. Despite its limitations, our results suggest that the real-time analysis of the space-time interaction can be a valuable decision support tool during the course of a livestock disease epidemic.

Analysis of the 1999–2000 highly pathogenic avian influenza (H7N1) epidemic in the main poultry-production area in northern Italy

We evaluated the effects of risk factors and control policies following the highly pathogenic avian influenza (HPAI) epidemic that struck northern Italy's poultry industry in the winter of 1999-2000. The epidemic was caused by a type-A influenza virus of the H7N1 subtype, that originated from a low-pathogenic AI virus which spread among poultry farms in northeastern Italy in 1999 and eventually became virulent by mutation. Most infected premises (IP) were located in the regions of Lombardy and Veneto (382 out of 413, 92.5%), and the eradication measures provided for in the European legislation were enforced. In Veneto, where flock density was highest, infection-control was also accomplished by means of depopulation of susceptible flocks through a ban on restocking and pre-emptive slaughter of flocks that were in the vicinities of or that had dangerous contacts with IPs. In Lombardy, such control measures were applied to a lesser extent. Infection incidence rate (IR) was 2.6 cases per 1000 flocks per day in Lombardy and 1.1 in Veneto. After the implementation of infection-control measures, the at-risk population, the percentage of flocks < or =1.5 km from IPs, and the HPAI-IR underwent a greater reduction in Veneto than in Lombardy. Although the proximity (< or =1.5 km) to IPs in the temporal risk window (TRW) was a major risk factor for HPAI at the individual flock level, its effect at the population level (population-attributable fraction) did not exceed 31.3%. Viral transmission therefore also occurred among relatively distant flocks. Turkey flocks were characterised by greater IR of HPAI compared with other bird species such as layer hens, broilers, gamebirds, and waterfowl, even when located at distances >1.5 km from IPs. In Lombardy, IR for species other than turkeys was also relatively high.

Models of foot-and-mouth disease

During the 2001 foot-and-mouth disease outbreak in the UK, three very different models were used in an attempt to predict the disease dynamics and inform control measures. This was one of the first times that models had been used during an epidemic to support the decision-making process. It is probable that models will play a pivotal role in any future livestock epidemics, and it is therefore important that decision makers, veterinarians and farmers understand the uses and limitations of models. This review describes the utility of models in general before focusing on the three foot-and-mouth disease models used in 2001. Finally, the future of modelling is discussed, analysing the advances needed if models are to be successfully applied during any subsequent epidemics.

The foot-and-mouth disease epidemic in Dumfries and Galloway, 2001. 1: Characteristics and control

The foot-and-mouth disease epidemic in Dumfries and Galloway in south-west Scotland comprised 177 infected premises (IPS) in 24 geographical clusters, and ran from March 1 until May 23, 2001. Initial seeding of infection was by livestock (predominantly sheep) that had passed through Longtown Market in adjacent Cumbria. Thereafter, spread within existing, and to new, clusters was associated with the movement of personnel and vehicles, with further transmission by Longtown Market contacts and across common boundaries. Sheep and cattle premises were equally affected. After the peak of the epidemic at the beginning of the third week of March, the upper possible limit of attack rates for premises contiguous to IPS, and premises within 3 km, remained around 10 per cent, with new clusters emerging more distantly. Control procedures included traditional methods of slaughter of all animals on IPS and, elsewhere, of animals considered by veterinary assessment to be Dangerous Contacts; movement restrictions; enhanced biosecurity; tracing of potential sources and spread of virus; and surveillance of premises subsequently considered at risk. These methods were supplemented by the novel pre-emptive slaughter, without veterinary assessment, of all susceptible livestock on all premises contiguous to IPS, and of small ruminants and pigs within a 3 km radius (known as the Protection Zone) around IPS. In total, approximately 80,000 cattle, 564,000 sheep, 2600 pigs and 500 goats were slaughtered, the novel methods accounting for 29 per cent of all cattle and 75 per cent of all sheep killed. Limitations of existing national databases necessitated the development of local databases to administer control procedures.

Relationship of speed of slaughter on infected premises and intensity of culling of other premises to the rate of spread of the foot-and-mouth disease epidemic in Great Britain, 2001

During the foot-and-mouth disease epidemic in the UK in 2001, two major control policies were the rapid identification of cases and the culling of animals on infected premises and on dangerous contact premises. Dangerous contact premises were divided into two groups, premises contiguous to an infected premises and non-contiguous premises. In England, the largest numbers of geographically clustered infected premises were in Cumbria, the South West (Somerset, Devon and Cornwall) and the Settle/Clitheroe area straddling the Yorkshire-Lancashire border. In each of these clusters, the rate of spread of the disease, the average time from the first lesion to slaughter on infected premises, and the intensity of culling of contiguous premises and non-contiguous premises were calculated for seven-day periods. Linear regression analysis was used to look for relationships between these factors and the rate of spread of the disease. The average time from the first lesion to slaughter had a statistically significant relationship in two of the three clusters and the intensity of culling of non-contiguous premises had a significant relationship in one. The intensity of culling of contiguous premises had no significant relationship in any of the three clusters.

Evaluation of the appicatoion of veteroinary judgement in the pre-emptive cull of contiguous premises during the epidemic of foot-and-mouth disease in Cumbria in 2001

This paper presents a detailed analysis of the application of contiguous culling in Cumbria between May 1 and September 30, during the outbreak of foot-and-mouth disease in 2001. The analysis shows that the application of veterinary risk assessment and judgement identified and removed groups of susceptible stock which were at risk of direct transmission of infection and avoided infected animals being left that might have spread the disease. When compared with an automatic contiguous cull, fewer culls were made and some of these were reduced in scale, providing economies in the use of resources. The data suggest that farms contiguous to an infected premises faced a 5 per cent risk of infection by direct transmission and a 12 per cent risk of infection by indirect transmission.