Foot-and-mouth disease (FMD) is a highly contagious disease, which is known to spread easily within and between herds and cause severe economic losses in each herd as well as in the country (1). The control and eradication of FMD within the EU is governed by EU legislation (Council Directive 2003/85/EC of 29 September 2003; http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32003L0085&from=EN). Following the directive, EU member states are obliged to use a stamping out policy, involving quarantine, movement restrictions, zoning, and slaughter and disposal of all affected herds, followed by cleaning and disinfection (CD) of the farm. Additional control measures can be used, for example, preemptive culling or vaccination, after approval of the plan by the European Commission regulatory committee Standing Committee on the Food Chain and Animal Health.

Preparedness planning for a veterinary crisis, such as an FMD outbreak, is important in order to be fast and effective in the eradication of the disease. For countries with a large export of animals and animal products, each extra day in an epidemic will cost millions of Euros due to the closure of export markets. This is of utmost importance for the Danish swine industry, which had a yearly export of €4.4 billion in 2012 (2).

Modeling results have previously been used to inform decision-making related to disease control options (3–14). Models have traditionally been including limitations on resources for culling and vaccination, while only few models have included resources for other things such as surveillance visits [e.g., Ref. (15)]. With this work, we propose to also use the outputs of simulation modeling for planning and operational purposes in a Veterinary Administration of a country before and during a veterinary crisis.

Geering and Lubroth (16) describe how the first step in preparing a resource plan is to make a resource inventory, “listing all the resources needed to respond to a moderate-sized FMD outbreak or other high-priority emergency disease. The plan includes personnel, equipment, and other physical resources.” Garner et al. (17) estimated the needed resources during a hypothetical FMD-epidemic in Australia, focusing on the 90th percentile out of 100 iterations simulated to set-off from the area previously predicted to give the worst epidemics in terms of size and duration (18). For the Danish Veterinary and Food Administration, it is important to know how many persons must be available within hours and days in order to efficiently handle and eradicate the disease. And to consider whether these people are already available in the organization and how extra personnel can be recruited. Furthermore, it is important to consider, whether this personnel has the required level of education or if extra training and education is needed. Similarly, the need for materials and services during a veterinary crisis must be identified and quantified. These materials and services include for example cars, sampling materials and testing capacity at the laboratory, equipment for culling of animals, protective clothing, disinfection agents, valuators, trucks and rendering capacity.

The purposes of this project were (1) to develop an iterative tool with the aim of estimating the resources needed during an outbreak of FMD in Denmark and (2) to identify areas, which can be bottle necks in the veterinary administration, and thereby delay the control of the disease and the time to regain disease free status and the access to export markets. The tool developed should easily be updated, when new knowledge is gain from other veterinary crises or during an outbreak of FMD.

The simulated epidemics had a median size of 22 (5–95%: 2–155) infected and detected herds (Figure 1) and a median duration of 34 days (5–95%: 2–142), counted from first detection until the last herd is culled, but not taking into account the time until zones are lifted. The median number of SI was 110 (5–95%: 10–7,775).

Based on the results from the simulation models, we estimated that the need for personnel in the regions would peak in the first couple of days with a median of 116 veterinarians, 22 technicians needed, while the need for administrative personnel would peak a little later with a need for a median of 45 administrative personnel 21 days in the epidemic (Figures 2–4; Table 3). Furthermore, the numbers of needed veterinarians would also increase at day 21, caused by the second surveillance visit of herds in the protection zone (Figure 2). Additionally, 174 persons would be needed from DEMA at day 2, mostly recruits (Figure 5; Table 3).

From the sensitivity analyses (Table 4), it was clear that the time needed to perform clinical surveillance in farms (either suspected farms or farms located in protection and surveillance zones) influences the estimated numbers of veterinarians and technicians needed during an outbreak. Increased efficiency in the LCCs, leading to decreased time needed for each task, decreased the need for veterinarians, technicians and administrative personnel, and using DEMA personnel for only 1 day instead of 2 in detected herds, had large influence on the total numbers of DEMA staff needed. Furthermore, the involvement of only one LCC decreased the total manpower needed during the epidemics.

Based on results from a stochastic simulation model, it was possible to create a model in R to estimate the requirements for personnel during an FMD outbreak in Denmark. The model can easily be adjusted, when new information on resources appear from management of other crisis, or when new simulation results are available from new model runs in peacetime. Furthermore, it is possible to adjust the model during a crisis, when model results from daily runs of the stochastic simulation model gives more precise estimates on the specific epidemic, or when adjustments in management procedures becomes available.

It was not surprising to find that especially the number of staff needed for surveillance visit influenced our results, as the numbers of herds in zones are so large. This was also in line with what was found by Garner et al. (17). This means that if veterinarians doing surveillance visits can be more efficient, the number of needed veterinarians will decrease substantially. On the other hand, if veterinarians are not careful, the probability of detection by surveillance visits will decrease, resulting in larger and longer lasting epidemics.

A peak for veterinarians was predicted very early in the epidemic (Figure 2). However, the assumption in the model is to be able to survey 450 farms a day in the protection and surveillance zones. If resources for this surveillance are reduced, as described by Halasa et al. (15), surveillance visits will be delayed, leading to delayed detections, prolonged epidemic duration, and an expected right shift in the peak for resources needed.

The resources estimated here were based on simulated epidemics and were shown to follow the simulated epidemic peaks closely (Figures 1, 2 and 5), however, with some delays for technicians and administrative personnel (Figures 3 and 4) and with an increase in needed resources again around day. Varying model inputs in the simulation model have previously been shown to change the outputs (5, 19) and corresponding changes in resources can be expected. Especially, the low risk contacts and the probability of local spread and disease detection were highly influential (19). Furthermore, a decrease in the length of the high-risk period (HRP) would decrease the size, duration, and costs of an outbreak (5). However, using a conservative estimate with a mode of 21 days (18–23) as the HRP would relate to the 2001 FMD epidemic in Europe, where the HRP was estimated to 21 days in the UK (26, 27) as well as in the Netherlands (28).

Our estimates were based on daily outputs from 981 simulated epidemics under a basic control strategy, e.g., the strategy expected to be used, if an outbreak would occur tomorrow. However, in very large epidemics, there is a probability that decision makers would not choose to stick to the basic control strategy, but would most likely add extra control measures such as preemptive culling or emergency vaccination, and therefore, the resources needed in the extreme epidemics would change.

Surprisingly, it was shown that there was a very high need for recruits from the DEMA used in the CD of detected herds (Figure 5), which might turn out to be a bottle neck; while our expectations were that the Danish Veterinary and Food Administration could run out of veterinarians.

Before the UK 2001 outbreak of FMD, the UK State Veterinary Service used two scenarios in their contingency planning, one moderate scenario with 10 simultaneous outbreaks and 1 severe outbreak, also with 10 simultaneous outbreaks, but with a large herd density. And they found a need for 235 veterinary officers, which they extrapolated to around 300 in more severe outbreaks. During the UK 2001 outbreak, 57 premises were infected before the first herd was diagnosed, leading to an almost immediate need for all state veterinary officers. Before the end of the outbreak, another 2,500 temporary veterinary inspectors were appointed, nearly 70 from abroad, and another 700 foreign government vets and secondees assisted in periods (29).

Based on the experience in the UK, we could fear that we are underestimating the needs during an FMD crisis in Denmark. However, even though we were interested in estimating manpower and materials needed, we were also aware that we can end up with even larger epidemics. Therefore, it was important for us to create a model, which can easily be adjusted during a crisis in an iterative process. Each time new information become available, regarding the epidemic or the resources needed, it can be fed into the model, and new outputs can be calculated. For example, if the compositions of the veterinary teams for different tasks are changed, we will change the inputs in the model and rerun it. Or if we rerun the stochastic model with historic data from the first 10 days of the epidemic, we will have more precise estimates on the further development of the epidemic and that can be put into the resource model.

The results of our estimations seem somewhat lower than what was estimated in Australia (17). Direct comparisons are difficult, due to geographical differences, resulting in several state disease control centers and local disease control centers in Australia, differences in estimated size of the epidemic, i.e., Garner et al. chose a 90th percentile epidemic and differences in how results are presented. Garner estimated nearly 20% of staff needed was veterinarians, while we estimated 33%.

The calculation of resources needed is an iterative process. The simulation model includes assumptions regarding resources, to simulate realistic epidemics, as scarce resources will prolong the epidemics. After assuming the available resources, we then calculate the daily needs. Naturally, this seems like a circular argument. However, in the simulation model, resources are roughly set as numbers of animals or herds that can be processed daily for either depopulation or surveillance. In the resource calculation presented here, we go into details regarding the teams for each task, the time needed, and look at number of herds and numbers of animals to process. The influence of the assumptions regarding resources has previously been described for depopulation (5) and surveillance (19). In both situations, the influence of reducing the resources was limited, reflecting that plenty of resources were assumed for most simulated outbreaks. This means that the calculations presented in this paper closely reflect the daily needs, when resources are not a limiting factor.

One of the assumptions was that all three veterinary regions would be involved from the beginning of the epidemic. While this was not truly realistic, the influence of this assumption was assumed to be limited, as many parameters even in the LCC depended on the numbers of herds and animals involved in the epidemic rather than the numbers of LCC. However, overall, we did estimate a clear decrease in the manpower needed for veterinarians as well as technicians and administrative staff. Therefore, an adjustment of the model taking region into account will be considered in future versions of the model.

In the current estimations, the very basic needs during an epidemic were estimated. Traveling time between herds was taken into account in the estimates (Table 1), while logistic challenges were not taken into account, such as veterinarians stuck in a herd after a surveillance visit that turned out to become a detection of an infected herd. In situations like that the veterinarian will stay in the detected herd and will not be able to visit other herds for the two following days. However, we assumed that the veterinarian would then be able to carry out other tasks, for example in the LCC. The competences needed for personnel involved in each task are described in details in the contingency plan for FMD (24) and in the project report (22). Furthermore, geographical challenges were not taken into consideration in these calculations. Denmark is a rather small country, where farmost destinations can be reached in a reasonable driving time (3–4 h). However, longer driving time will of course reduce the number of herds a veterinarian can visit on a given day. Nevertheless, estimating the amount of personnel needed gives us no answers in itself. To be able to use these estimates, it is necessary for the Danish Veterinary and Food Administration to compare with the present staff available and to consider how and where more personnel can be recruited to meet the needs during a crisis and which type of training is required in peace time, to be ready for an outbreak. The working group has continued working on this matter to update the Danish FMD contingency plan according to the results of the resource estimations and has given detailed descriptions on required competences for different types of staff for different tasks and how people can be trained to meet the challenges during a crisis. All of these results are described in a report from the expert group, in Danish (22).

AB, SM, and MJ in study design; AB and TH developed the model; and all the authors contributed to the results and discussion. AB wrote the manuscript, and all the authors commented and approved the final version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.