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

A cost-benefit analysis of foot and mouth disease control program for smallholder cattle farmers in Cambodia

The Cambodian government is attempting to mobilise government, donor and private sector funding to implement a coordinated FMD vaccination program (FMDVP). A necessary first step is to convince the farmers of the benefits of participating in and potentially financially supporting this program. Information was collected from 300 farmers in order to estimate the on-farm benefits and costs of their participation in an FMDVP. Implementing a successful vaccination program is difficult, and farmers understand from previous experience that there may be institutional, social, technical and financial constraints which limit its success. A benefit-cost analysis needs to take into account that outbreaks do not occur every year, not all cattle will be successfully vaccinated, not all sick animals successfully treated and sometimes sick animals simply sold. This study sensitises these variables in order to give a realistic estimation of the farmer participation benefits in an FMDVP. A general result is that it is worthwhile for farmers to participate in the FMDVP if there are average annual outbreaks, or at least two major outbreaks, in the ensuing 5 years. However, the results are influenced by the interaction of vaccination success and treatment success and coverage. Ineffective coverage and poor treatment of sick animals reduce the benefits of an FMDVP. It is also important that farmers do not sell sick stock and, if they do, that they are able to breed replacements rather than purchase replacements. There are many factors in the smallholder cattle farming system that will influence the success of an FMDVP; farmers will only choose to participate if they can be convinced of the short and long-term economic benefits.

Challenges and opportunities for using national animal datasets to support foot-and-mouth disease control

National level databases of animal numbers, locations and movements provide the essential foundations for disease preparedness, outbreak investigations and control activities. These activities are particularly important for managing and mitigating the risks of high-impact transboundary animal disease outbreaks such as foot-and-mouth disease (FMD), which can significantly affect international trade access and domestic food security. In countries where livestock production systems are heavily subsidized by the government, producers are often required to provide detailed animal movement and demographic data as a condition of business. In the remaining countries, it can be difficult to maintain these types of databases and impossible to estimate the extent of missing or inaccurate information due to the absence of gold standard datasets for comparison. Consequently, competent authorities are often required to make decisions about disease preparedness and control based on available data, which may result in suboptimal outcomes for their livestock industries. It is important to understand the limitations of poor data quality as well as the range of methods that have been developed to compensate in both disease-free and endemic situations. Using FMD as a case example, this review first discusses the different activities that competent authorities use farm-level animal population data for to support (1) preparedness activities in disease-free countries, (2) response activities during an acute outbreak in a disease-free country, and (3) eradication and control activities in an endemic country. We then discuss (4) data requirements needed to support epidemiological investigations, surveillance, and disease spread modelling both in disease-free and endemic countries.

Real-Time Standard Analysis of Disease Investigation (SADI)-A Toolbox Approach to Inform Disease Outbreak Response

An incursion of an important exotic transboundary animal disease requires a prompt and intensive response. The routine analysis of up-to-date data, as near to real time as possible, is essential for the objective assessment of the patterns of disease spread or effectiveness of control measures and the formulation of alternative control strategies. In this paper, we describe the Standard Analysis of Disease Investigation (SADI), a toolbox for informing disease outbreak response, which was developed as part of New Zealand's biosecurity preparedness. SADI was generically designed on a web-based software platform, Integrated Real-time Information System (IRIS). We demonstrated the use of SADI for a hypothetical foot-and-mouth disease (FMD) outbreak scenario in New Zealand. The data standards were set within SADI, accommodating a single relational database that integrated the national livestock population data, outbreak data, and tracing data. We collected a well-researched, standardised set of 16 epidemiologically relevant analyses for informing the FMD outbreak response, including farm response timelines, interactive outbreak/network maps, stratified epidemic curves, estimated dissemination rates, estimated reproduction numbers, and areal attack rates. The analyses were programmed within SADI to automate the process to generate the reports at a regular interval (daily) using the most up-to-date data. Having SADI prepared in advance and the process streamlined for data collection, analysis and reporting would free a wider group of epidemiologists during an actual disease outbreak from solving data inconsistency among response teams, daily “number crunching,” or providing largely retrospective analyses. Instead, the focus could be directed into enhancing data collection strategies, improving data quality, understanding the limitations of the data available, interpreting the set of analyses, and communicating their meaning with response teams, decision makers and public in the context of the epidemic.

Evaluation of Movement Restriction Zone Sizes in Controlling Classical Swine Fever Outbreaks

The objective of this study was to compare the impacts of movement restriction zone sizes of 3, 5, 9, and 11 km with that of 7 km (the recommended zone size in the United States) in controlling a classical swine fever (CSF) outbreak. In addition to zone size, different compliance assumptions and outbreak types (single site and multiple site) were incorporated in the study. Three assumptions of compliance level were simulated: baseline, baseline ± 10%, and baseline ± 15%. The compliance level was held constant across all zone sizes in the baseline simulation. In the baseline ± 10% and baseline ± 15% simulations, the compliance level was increased for 3 and 5 km and decreased for 9 and 11 km from the baseline by the indicated percentages. The compliance level remained constant in all simulations for the 7-km zone size. Four single-site (i.e., with one index premises at the onset of outbreak) and four multiple-site (i.e., with more than one index premises at the onset of outbreak) CSF outbreak scenarios in Indiana were simulated incorporating various zone sizes and compliance assumptions using a stochastic between-premises disease spread model to estimate epidemic duration, percentage of infected, and preemptively culled swine premises. Furthermore, a risk assessment model that incorporated the results from the disease spread model was developed to estimate the number of swine premises under movement restrictions that would experience animal welfare outcomes of overcrowding or feed interruption during a CSF outbreak in Indiana. Compared with the 7-km zone size, the 3-km zone size resulted in a longer median epidemic duration, larger percentages of infected premises, and preemptively culled premises (P’s < 0.001) across all compliance assumptions and outbreak types. With the assumption of a higher compliance level, the 5-km zone size significantly (P < 0.001) reduced the epidemic duration and percentage of swine premises that would experience animal welfare outcomes in both outbreak types, whereas assumption of a lower compliance level for 9- and 11-km zone sizes significantly (P < 0.001) increased the epidemic duration and percentage of swine premises with animal welfare outcomes compared with the 7-km zone size. The magnitude of impact due to a zone size varied across the outbreak types (single site and multiple site). Overall, the 7-km zone size was found to be most effective in controlling CSF outbreaks, whereas the 5-km zone size was comparable to the 7-km zone size in some circumstances.

Epidemic protection zones: centred on cases or based on connectivity?

When an exotic infectious disease invades a susceptible environment, protection zones are enforced. Historically, such zones have been shaped as circles of equal radius (ER), centred on the location of infected premises. Because the ER policy seems to assume that epidemic dissemination is driven by a similar number of secondary cases generated per primary case, it does not consider whether local features, such as connectivity, influence epidemic dispersal. Here we explored the efficacy of ER protection zones. By generating a geographically explicit scenario that mimicked an actual epidemic, we created protection zones of different geometry, comparing the cost-benefit estimates of ER protection zones to a set of alternatives, which considered a pre-existing connecting network (CN) - the road network. The hypothesis of similar number of cases per ER circle was not substantiated: the number of units at risk per circle differed up to four times among ER circles. Findings also showed that even a small area (of <115 km(2) ) revealed network properties. Because the CN policy required 20% less area to be protected than the ER policy, and the CN-based protection zone included a 23.8% greater density of units at risk/km(2) than the ER-based alternative, findings supported the view that protection zones are likely to be less costly and more effective if they consider connecting structures, such as road, railroad and/or river networks. The analysis of local geographical factors (contacts, vectors and connectivity) may optimize the efficacy of control measures against epidemics.

The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis

This paper explores methods for describing the dynamics of early epidemic spread and the clustering of infected cases in space and time when an underlying contact network structure is influencing disease spread. A novel method of describing an epidemic is presented that applies social network analysis to characterise the importance of both spatial location and contact network position. This method enables the development of a model of how these clusters formed, incorporating spatial clustering and contact network topology. Based on data from the first 30 days of the 2007 equine influenza outbreak in Australia, clusters of infected premises (IPs) were identified and delineated using social network analysis to integrate contact-tracing and spatial relationships. Clusters identified by this approach were compared to those detected using the space-time scan statistic to analyse the same data. To further investigate the importance of network and spatial location in epidemic spread, kriging by date of onset of clinical signs was used to model the spatio-temporal spread without reference to underlying contact network structure. Leave-one-out cross-validation was used to derive a summary estimate of the accuracy of the kriged surface. Observations poorly explained by the kriged surface were identified, and their position within the contact network was explored to determine if they could be better explained through analysis of the underlying contact network. The contact network was found to lie at the core of a combined network model of spread, with infected horse movements connecting spatial clusters of infected premises. Kriging was imprecise in describing the pattern of spread during this early phase of the outbreak (explaining only 13% of the variation in date of onset of IPs), because early dissemination was dominated by network-based spread. Combined analysis of spatial and contact network data demonstrated that over the first 30 days of this outbreak local spread emanated outwards from the small number of infected premises in the contact network, up to a distance of around 15km. Consequently, when a contact network structure underlies epidemic spread, we recommend applying a method of spatial analysis that incorporates the network component of disease spread. Linking the spatial and network analysis of epidemics facilitates inference of the methods of disease transmission, providing a description of the sequence of spatial cluster formation.

The role of pre-emptive culling in the control of foot-and-mouth disease

The 2001 foot-and-mouth disease epidemic was controlled by culling of infectious premises and pre-emptive culling intended to limit the spread of disease. Of the control strategies adopted, routine culling of farms that were contiguous to infected premises caused the most controversy. Here we perform a retrospective analysis of the culling of contiguous premises as performed in 2001 and a simulation study of the effects of this policy on reducing the number of farms affected by disease. Our simulation results support previous studies and show that a national policy of contiguous premises (CPs) culling leads to fewer farms losing livestock. The optimal national policy for controlling the 2001 epidemic is found to be the targeting of all contiguous premises, whereas for localized outbreaks in high animal density regions, more extensive fixed radius ring culling is optimal. Analysis of the 2001 data suggests that the lowest-risk CPs were generally prioritized for culling, however, even in this case, the policy is predicted to be effective. A sensitivity analysis and the development of a spatially heterogeneous policy show that the optimal culling level depends upon the basic reproductive ratio of the infection and the width of the dispersal kernel. These analyses highlight an important and probably quite general result: optimal control is highly dependent upon the distance over which the pathogen can be transmitted, the transmission rate of infection and local demography where the disease is introduced.

Statistical modeling of holding level susceptibility to infection during the 2001 foot and mouth disease epidemic in Great Britain

BACKGROUND

An understanding of the factors that determine the risk of members of a susceptible population becoming infected is essential for estimating the potential for disease spread, as opposed to just focusing on transmission from an infected population. Furthermore, analysis of the risk factors can reveal important characteristics of an epidemic and further develop understanding of the processes operating.

METHODS

This paper describes the development of a mixed effects logistic regression model of susceptibility of holdings to foot and mouth disease (FMD) during the 2001 epidemic in Great Britain following the imposition of a national ban on the movements of susceptible animals (NMB).

RESULTS

The principal risk factors identified in the model were shorter distances to the nearest infectious seed (a holding infected before the NMB) and the county of the holding (principally Cumbria). Additional risk factors included holdings that are mixed species rather than single species, the surface area of the holding, and the number of cattle within 10km (all p<0.001), but not surrounding sheep densities (p>0.1). The fit of the model was evaluated using the area under the receiver operator characteristic curve (ROC) and the Hosmer and Lemeshow Chi-squared statistic; the fit was good with both tests (area under the ROC=0.962 and Hosmer and Lemeshow Chi-squared statistic=49.98 (p>0.1)).

CONCLUSIONS

Holdings at greatest risk of infection can be identified using simple readily available risk factors; this information could be employed in the control of future FMD epidemics.

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.

Accuracy of models for the 2001 foot-and-mouth epidemic

Since 2001 models of the spread of foot-and-mouth disease, supported by the data from the UK epidemic, have been expounded as some of the best examples of problem-driven epidemic models. These claims are generally based on a comparison between model results and epidemic data at fairly coarse spatio-temporal resolution. Here, we focus on a comparison between model and data at the individual farm level, assessing the potential of the model to predict the infectious status of farms in both the short and long terms. Although the accuracy with which the model predicts farms reporting infection is between 5 and 15%, these low levels are attributable to the expected level of variation between epidemics, and are comparable to the agreement between two independent model simulations. By contrast, while the accuracy of predicting culls is higher (20–30%), this is lower than expected from the comparison between model epidemics. These results generally support the contention that the type of the model used in 2001 was a reliable representation of the epidemic process, but highlight the difficulties of predicting the complex human response, in terms of control strategies to the perceived epidemic risk.

The potential for improving welfare standards and productivity in United Kingdom sheep flocks using veterinary flock health plans

Data from industry sources detailing variable costs in 2003 revealed that the average farmer keeping 1000 lowland ewes in the United Kingdom spent 3500 UK pounds annually on veterinary fees and medicines. Despite such expenditure, psoroptic mange and cutaneous myiasis are common in the UK, resistance to one or more anthelmintic group is not only common but increasing in frequency and distribution, and abortion outbreaks caused by Toxoplasma gondii and Chlamydophila abortus are frequently reported by veterinary laboratories. Welfare concerns also arise from farmers' intransigence towards tail docking and castration in lambs (mutilations), reported market forces necessitating long distance road transportation to slaughter plants, and an unwillingness to employ veterinary surgeons for obstetrical problems. The spread of sheep scab in the UK over the past decade illustrates the failure of flock owners to effect rudimentary biosecurity and disease control measures. A first step towards improving the health and welfare of sheep would be the immediate implementation of basic good husbandry practices, including ectoparasiticide treatment for sheep scab eradication, prophylaxis for cutaneous myiasis in selected lambs, and appropriate vaccination strategies for clostridial diseases and certain abortion agents. There would also be money from within current farm expenditure to provide veterinary attention for obstetrical problems affecting up to 2% of ewes per annum. Planned use of ecto- and endoparasiticides is urgently needed to maintain the efficacy of these unique drugs.

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.