Open access
Research Article
23 May 2017

Predicting the Ability of Preclinical Diagnosis To Improve Control of Farm-to-Farm Foot-and-Mouth Disease Transmission in Cattle

This article has been corrected.


Foot-and-mouth disease (FMD) can cause large disruptive epidemics in livestock. Current eradication measures rely on the rapid clinical detection and removal of infected herds. Here, we evaluated the potential for preclinical diagnosis during reactive surveillance to reduce the risk of between-farm transmission. We used data from transmission experiments in cattle where both samples from individual animals, such as blood, probang samples, and saliva and nasal swabs, and herd-level samples, such as air samples, were taken daily during the course of infection. The sensitivity of each of these sample types for the detection of infected cattle during different phases of the early infection period was quantified. The results were incorporated into a mathematical model for FMD, in a cattle herd, to evaluate the impact of the early detection and culling of an infected herd on the infectious output. The latter was expressed as the between-herd reproduction ratio, Rh, where an effective surveillance approach would lead to a reduction in the Rh value to <1. Applying weekly surveillance, clinical inspection alone was found to be ineffective at blocking transmission. This was in contrast to the impact of weekly random sampling (i.e., using saliva swabs) of at least 10 animals per farm or daily air sampling (housed cattle), both of which were shown to reduce the Rh to <1. In conclusion, preclinical detection during outbreaks has the potential to allow earlier culling of infected herds and thereby reduce transmission and aid the control of epidemics.


Foot-and-mouth disease (FMD) is a contagious viral disease caused by the foot-and-mouth disease virus (FMDV). It affects domestic ruminants such as cattle, sheep, and goats as well as pigs and other cloven-hoofed wild and domestic mammals. If not brought under control, the disease can spread rapidly, resulting in significant economic impacts with regard to trade and animal productivity (1). For example, during the 2001 epidemic in the UK, in excess of 2,000 cases were confirmed, leading to several million animals being culled. Some £2.5 billion was paid by the government in compensation for slaughtered animals and the costs associated with the cleanup and safe disposal of animal carcasses (2).
For FMD-free countries, the focus on maintaining their FMD-free status centers on avoiding the introduction of the virus. However, once an outbreak has been declared, the emphasis changes to one of disease control through the culling of animals on farms known to be infected, tracing dangerous contacts, and implementing animal movement restrictions. The success of reactive control measures such as those mentioned above depends critically on the time between a farm becoming infected and the virus being detected and removed (3, 4). Currently, the detection of FMDV-infected farms relies on the identification and reporting of animals showing clinical signs. Although previous studies of FMDV in cattle have shown that most transmission occurs after the onset of clinical signs (4, 5), any delays in detection can potentially compromise the effectiveness of control measures. FMDV can be detected in secretions and excretions, such as blood, nasal fluid, saliva, esophageal-pharyngeal fluid, or exhaled air, from infected animals before they show clinical disease (47). This raises the possibility of detecting preclinical shedding of the virus, which could in turn enable earlier intervention in the transmission cycle, leading to a decreased likelihood of onward spread.
The objective of this study was to assess the potential for preclinical (early) detection of infected herds as a control tool to reduce the risk of transmission between herds during epidemics. We used data from transmission experiments to estimate the sensitivity (Se) of different sample matrices for the detection of FMDV-infected cattle throughout the incubation period. Sample matrices included individual samples, such as blood, esophageal-pharyngeal fluid, and saliva and nasal swabs, and group-level samples, such as exhaled air. These Se estimates were incorporated into a mathematical model to evaluate the efficacy of different surveillance methods (sample matrix, number of samples, and frequency of sampling) for the early detection of an infected herd and the subsequent reduction of the infectious output. This paper concludes by discussing the potential for specific preclinical samples to be used as surveillance tools to aid in reactive control measures during an epidemic and comments on their performance as tools for early detection. Although we focus on FMD, this approach could also be applicable to other diseases such as African swine fever and classical swine fever, where virus genomes can be detected before infected pigs become infectious (8).


Infection and shedding patterns.

In the transmission experiments, all inoculated and contact-infected calves started shedding virus (as detected by real-time quantitative reverse transcription-PCR [qPCR]) at 1 day postinoculation (dpi) or at 1 day postchallenge. With regard to inoculated calves, vesicles other than those at the inoculation sites in the tongue were observed at between 1 and 1.5 dpi, the assumed start of the clinical phase of infection. In the contact-infected calves, the clinical phase started (i.e., vesicles were observed) at between 4 and 5 dpi. Figure 1 shows the distribution of shedding measured in each of the different samples evaluated. With the exception of probang samples, the highest level of shedding was found to occur during the clinical phase. For nasal samples in particular, the level of shedding was significantly higher (P < 0.05) during this phase than during the preclinical or early recovery phase. Results for exhaled aerosols are shown only for the cyclone (glass) sampler because it was the most sensitive sampler (see below) and therefore provided more observations for analysis. No significant differences among the different phases (Fig. 2) were observed in this case.
FIG 1 Virus shedding measured in different sample types. For analysis, the disease process is divided into preclinical, clinical, and early recovery phases. A linear mixed-regression model, where each calf identification was introduced as a random effect, was used to compare shedding levels during each phase of the disease process. When the clinical phase was used as a reference for comparison, the level of nasal shedding was significantly lower (P < 0.001) during both the preclinical and recovery phases. The level of shedding in saliva was lower (P < 0.01) in the preclinical phase, while the level of shedding in serum was lower (P < 0.001) in the recovery phase. No significant differences in shedding were found for probang samples (P = 0.06, recovery phase).
FIG 2 Virus shedding as measured by the cyclone air sampler. The disease process is divided into preclinical, clinical, and early recovery phases. A linear mixed-regression model, where each calf identification was introduced as a random effect, was used to compare shedding levels during each phase of the disease process. No significant differences in virus shedding were observed between the different phases. Although other air sampling devices were used, only the cyclone results were analyzed, as this sampler proved to be the most sensitive and provided more data for analysis.

Diagnostic sensitivity of qPCR using different types of samples.

The numbers of diagnostic and air samples taken during the course of the experiments and subsequently used for this analysis are summarized in Table 1. The estimated Se values for each of the diagnostic samples evaluated were not significantly different during the preclinical, clinical, and early recovery phases. Of particular interest for this study, however, was the preclinical-phase results. All sample types taken during this phase, with the exception of serum, had a Se greater than 0.85 (85%). The probang samples gave the highest Se values, while serum samples gave the lowest values (P < 0.05) (Fig. 3a).
TABLE 1 Total numbers of samples tested for each different sample type and evaluated during the preclinical, clinical, and early recovery phases of the disease process
Sample typeNo. (%) of samples during phase
PreclinicalClinicalEarly recovery
    Nasal swabs3228 (88)3229 (91)2016 (80)
    Probang2827 (96)2525 (100)1716 (94)
    Saliva swabs3226 (81)3229 (91)2019 (95)
    Serum3224 (75)3226 (81)2011 (55)
Air close to calves
    Airport-MD854 (80)1916 (83)1512 (80)
    BioBadge104 (40)2912 (41)156 (40)
    BioSampler32 (67)1310 (77)126 (50)
    Cyclone33 (100)88 (100)1310 (77)
    May32 (67)1511 (73)96 (67)
Room air
    Cycloneb44 (100)1212 (100)77 (100)
Samples are taken from 16 calves (8 inoculated and 8 contact infected). Most inoculated calves were euthanized before the early recovery phase, hence the lower number of samples tested in this phase.
Cyclone samplers used for room sampling were a glass cyclone sampler and the Coriolis sampler. Because all samples were positive (sensitivity for each phase of infection of 100%), the lower 95% confidence limits were estimated by using the Wilson score method. This method is suitable for small sample sizes (26).
FIG 3 Se of different sample-qPCR combinations. Estimates of Se are stratified for the different phases of the disease process: preclinical, clinical, and early recovery. Se and 95% confidence intervals were modeled by using a logistic mixed-regression model (16), where each calf identification was introduced as a random effect. (a) Se estimates for each of the diagnostic samples evaluated; (b) Se estimates for the air samplers evaluated.
In contrast to the diagnostic samples, there were significant differences (P < 0.05) in the Se of the different air samplers when used in close proximity to calves. The cyclone, AirPort-MD8, and May samplers, which had the highest air sampling rates, had the highest Se (Se of >0.7) during the preclinical phase. The BioBadge air sampler was shown to have the lowest Se (Fig. 3b).
Ambient room air was collected with the cyclone samplers, and all samples taken during the preclinical, clinical, and recovery phases were positive (Table 1). The lower 95% confidence limits (95% LCLs) for the Se at the preclinical, clinical, and early recovery phases were 0.51, 0.75, and 0.64, respectively. We also estimated the Se for room air sampling using data from a previous study (6). The highest Se was estimated for the samplers with the highest sampling rates (Fig. 4).
FIG 4 Se of filter-based air samplers with different sampling rates for preclinical detection of foot-and-mouth disease virus serotype A aerosols. Data used for these estimates were reported previously (6). Se and 95% confidence intervals were modeled by using a logistic regression model (16), which, for this data set, fit better than a mixed model. Only data for the preclinical phase were collated and analyzed.

Preclinical detection can be used to stop transmission between farms.

To assess the impact of preclinical detection on transmission between farms, we consider the between-herd reproduction ratio, Rh. This quantity is defined as the expected number of secondarily infected farms arising from one infectious farm during its infectious period. An epidemic can sustain itself only if Rh is >1, and consequently, preclinical detection will be effective at controlling spread if it is able to reduce the Rh to <1.
Table 2 summarizes the expected reduction in Rh as a result of active surveillance using each of the sampling matrices during epidemics. As observed during the 2001 UK epidemic, clinical inspection (based mainly on farmer reports) does not reduce the infectious output (Rh = 1.54) of infected farms to a level sufficient to control spread. The implementation of preclinical surveillance using any of the evaluated sample matrices reduced the Rh to <1 for sampling at a frequency of 10 animals per farm once a week. When using air samplers, daily sampling would be required to reduce the Rh to <1. Similar results (i.e., Rh of <1) were observed during the sensitivity analysis, when a shorter incubation period and a longer infectious period were used. However, the sample size had to be doubled in this case (Table 2). For all surveillance methods, increasing either the number of samples taken (see Fig. S1 in the supplemental material) or the frequency of sampling (Fig. S2) results in a larger reduction in Rh.
TABLE 2 Reduction of the transmission potential of an infected herd due to implementation of preclinical detection and subsequent cullinga
Scenario and type of sample usedSampling frequency per wkSample size (no. of samples)bRhSample size (no. of samples)bRh
No surveillance3.23.2
Baseline transmission parameters
    Reporting clinical casesDetection delay of 8 days1.5
    Clinical inspectionsTwice101200.5
    Nasal swabOnce50.8100.3
    Saliva swabOnce50.8100.3
    Air (herd)dDaily0.51.410.6
Shorter latent period (2.25 days) and longer infectious period (4.85 days)c
    Reporting clinical casesDetection delay of 8 days1.7
    Clinical inspectionsTwice150.7200.5
    Nasal swabOnce100.5150.2
    Saliva swabOnce100.5150.2
    Air (herd)bDaily11.220.5
The transmission potential of a herd is expressed in terms of the reproduction ratio Rh.
Different sample sizes are shown as examples of the effect of sample size on Rh.
This means that cattle become infectious 2.25 days before showing clinical signs. This was done for the sensitivity analysis.
The Se of air sampling was made conditional on a minimum prevalence of infected animals (incubation period) of 10%. For prevalences of <10%, Se is 0.


This study attempted to evaluate the potential for preclinical diagnosis during reactive surveillance to bring about a reduction in the risk of between-farm transmission of FMD. Results obtained from analyses of the virus levels in diagnostic samples indicate that, with the exception of probang samples, the majority of virus shedding occurred during the clinical stage of infection. Airborne virus shedding, as measured by the cyclone air sampler, suggested that this is also true for exhaled aerosol emissions. More importantly for this paper, however, is virus shedding during the preclinical stage of infection. First, the results show that early-phase virus shedding (i.e., during the preclinical phase) can be detected by using a range of diagnostic and aerosol samples, with a high degree of sensitivity. Second, these samples can be used as part of emergency surveillance activities for the early detection of infected herds, thereby minimizing the risk of further transmission between herds.
In particular, shedding levels in nasal swabs were significantly higher during the clinical phase than during the preclinical and early recovery phases. Similarly, virus levels recovered in aerosols were also higher (although not significantly so) during the clinical phase. These differences in shedding levels in both nasal and aerosol samples might be associated with the previously reported higher probability of transmission during the clinical phase than during either the preclinical or the early recovery phase (4, 9). These observations suggest that nasal shedding could be used as a potential correlate of infectiousness in cattle. For example, the virus load in nasal samples could be readily used as a measure of infectiousness when evaluating vaccine efficacy during challenge experiments.
The use of blood samples for reactive surveillance and detection of infected herds before animals show clinical signs was demonstrated during the 2007 UK FMD epidemic (10). Our results are in agreement with those empirical findings; furthermore, we have quantified the Se of using blood samples as well as other diagnostic samples as preclinical biomarkers. These Se estimates are necessary to define the sample size and frequency of visits during reactive surveillance. Air sampling in closed environments was also evaluated for preclinical detection. For this variable, the Se appears to depend on the volumes of air sampled, with devices with higher sampling rates, such as the cyclone sampler or the sampler evaluated previously by Pacheco et al. with a sampling rate of 144 liters/min (6), showing the highest Se.
There are, however, some fundamental challenges associated with sampling in the field as opposed to sampling in a controlled experiment. For animals at pasture in the open air, the virus aerosol can be rapidly dispersed (or mixed), and sampling to capture enough virus to be detected in situations where a few animals have become infected will present a considerable challenge. Air sampling within an enclosed area such as a milking parlor is more practical and, due to slower dilution and removal of the virus aerosols, is likely to greatly increase the chances of detecting the virus. Detection sensitivity will be improved by sampling large volumes of ambient air using samplers, such as the cyclone sampler, which were previously shown to detect airborne viruses under field conditions (11, 12).
Any of the diagnostic samples evaluated could be used for the early detection of infected herds. Ideally, surveillance methods need to be quick, easy to employ, and as noninvasive as possible. These considerations rule out active clinical inspections and collection of probang and serum samples. They may also rule out the use of nasal sampling, as it can be very intrusive. In contrast, saliva and aerosol samplings are relatively noninvasive, and for aerosol samples, sampling instruments may be deployed in communal areas such as milking parlors. As well as using swabs, it might be possible to develop passive collection systems based on baits or licks attractive to cattle, as reported previously for pigs and wild boar (13). Both methods may be utilized by farmers and farm staff, which will boost surveillance resources and reduce the need for surveillance personnel to enter the farms to manipulate animals, thereby improving biosecurity. In addition, if either of these methods were to be employed in conjunction with a pen-side test used to detect virus genomes (14), rapid confirmation of infection may be obtained, speeding up the surveillance process even further.
Our results are based on experiments undertaken in controlled laboratory environments and may consequently differ in some respects from the real-world conditions of an outbreak. Although the models used in this paper are analytically robust (15, 16), the diagnostic and transmission parameters that are used to inform the model have been quantified experimentally. Bearing this in mind, a more conservative approach was adopted, using the lower confidence limits of the Se estimates for the evaluations. Nevertheless, field validation of the parameters might be necessary to improve our confidence in the model output. In addition, much of the fundamental work for this paper was undertaken using a FMDV serotype O strain, and transmission and disease characteristics may be different for other serotypes or strains (1720). To account for the possible differences in transmission dynamics, we evaluated the surveillance strategies assuming a shorter latent period and, consequently, a longer infectious period and a higher within-herd basic reproduction ratio, R0. The results still indicated that early detection is possible. In addition, we also quantified the diagnostic Se for air sampling using data reported previously for another serotype (serotype A) (6).
The spread of FMD can be so rapid that by the time a sick animal has been identified and infection has been confirmed, several other animals or farms may already have been exposed to the virus. Therefore, the rapid identification of secondarily infected farms is essential if we are to limit the onward spread of disease. Current control measures involve the culling of animals on farms where the virus is detected (based mainly on clinical reporting), restrictions on transport on and off infected premises, and screening and tracing of dangerous contacts. If this response is not effective, additional preemptive culling or vaccination programs are employed to curb the epidemic. These policies do not, however, consider preclinical identification, and the results highlighted in this paper and the experience of the outbreak in the UK in 2007 (10) suggest that the fast identification and removal of infected animals on farms where animals do not yet show clinical signs can result in a reduction of the risk of onward transmission and possibly the need for preemptive culling. It should be noted, however, that the effectiveness of the preclinical approach highlighted in this paper remains reliant upon the frequency of surveillance visits as well as the number of animals sampled.
To conclude, we show that reactive surveillance for the preclinical detection of infected herds has the potential to be a valuable alternative control tool with which to improve our ability to interrupt the cycle of infection and transmission of FMDV during epidemics and thus bring about a speedier and efficient end to epidemics.


Animal experiments.

Data for this study were obtained from a series of transmission experiments that were performed at The Pirbright Institute, Pirbright, UK, and Wageningen Bioveterinary Research (WBVR), Lelystad, the Netherlands.

Experimental procedures.

For the first set of experiments, eight healthy conventional Holstein Friesian calves housed in the high-containment units at The Pirbright Institute were used. Four calves, housed in pairs, were inoculated by intradermolingual injection with 0.2 ml of a virus suspension containing 105 50% tissue culture infective doses (TCID50) of FMDV serotype O strain UKG/34/2001. At 2 dpi, inoculated calves exhibited clinical signs, and each calf was paired with two naive contact calves for a period of 24 h (contact transmission). At the end of each direct contact transmission experiment (3 dpi), the inoculated calves were euthanized, and the contact calves were moved from the room and housed individually in clean rooms for observation. Contact calves were monitored until the vesicles in their mouths started healing, at around days 6 to 8 post-contact challenge (dpc). All experimental procedures were reviewed and approved by the ethical committee at The Pirbright Institute.
For the second set of experiments, eight healthy conventional Holstein Friesian calves housed at the high-containment units at WBVR were used. Calves were housed in pairs, in independent rooms. First, four calves (two per room) were needle inoculated according to the same procedures (including the use of the same virus) performed at The Pirbright Institute. At 1 dpi, two naive contact calves were placed into each room in direct contact with two inoculated calves for a period of 48 h. At the end of the contact challenge at 3 dpi, the inoculated calves were euthanized, and the contact calves were moved to clean rooms (two per room), where they were observed for the development of clinical signs up to 8 dpc. All experimental procedures were reviewed and approved by the ethical committee at WBVR.

Sampling and diagnostics.

All calves were tested for the presence of FMDV genomes or antibodies against the virus before the day of inoculation. Following inoculation or contact challenge, inoculated and contact calves were monitored twice per day for clinical signs. Nasal and saliva swabs, probang (esophageal-pharyngeal fluid) samples, and serum (blood) samples (here referred to as “diagnostic samples”) were taken daily from day 1 postinoculation to the time when these calves were euthanized. For the experiments performed at The Pirbright Institute, samples were taken twice daily.
During the experiments performed at The Pirbright Institute, air samples were taken twice daily after inoculation. Air samples were collected during a 10-min period with the room ventilation turned off and using five different sampling devices. The devices had different sampling flow rates and were as follows: the AirPort-MD8 (50 liters/min), BioBadge (10 liters/min), BioSampler (12 liters/min), May Multi-Stage Liquid Impinger (55 liters/min), and glass cyclone (570 liters/min) samplers. In addition to this, two sampling approaches were evaluated: (i) close-proximity samples taken from individual calves (sampler placed ∼10 cm from the nostril, within the path of exhaled air), and (ii) ambient room samples, with the cyclone sampler being located at the center of room, ∼1.2 m from the floor. The evaluated aerosol samplers were previously used for the detection of FMDV aerosols, and technical descriptions of these samplers were reported previously (21, 22).
Air sampling at WBVR was performed by using the AirPort-MD8 device for the close-proximity samples and a commercially available cyclone sampler (Coriolis) with a sampling flow rate of 300 liters/min for the ambient room samples. The cyclone sampler was placed close to the rooms' air exhaust, ∼1.7 m from the floor. Sampling was performed for 10 min, with the room ventilation turned on.
Both clinical and air samples were tested for the presence of virus genomes by using qPCR. For virus load quantification, 10-fold dilutions of the virus supernatant with a known titer (TCID50), determined by virus titration in bovine thyroid cells, were prepared as standard curves. qPCR results were expressed as the equivalent TCID50 per milliliter (23) for diagnostic samples or the equivalent TCID50 per cubic meter of air for air samples.

Additional air sampling data.

Data reported previously by Pacheco et al. (6) on the use of air sampling in experimental rooms to assess the diagnostic potential of air sampling for the detection of FMDV were also used for analysis in this study. Those authors described the use of air samplers for the preclinical detection of FMDV in experimental rooms housing calves infected with FMDV serotype A. One calf was housed per room, and samplers with different sampling rates (4.6, 15, and 144 liters/min) were used to sample air from these rooms daily. Data were obtained from a total of 12 independent trials (rooms), providing a total of 36 room samples.

Data analysis.

We first evaluated the level of shedding and quantified the diagnostic Se of qPCR using each of the different samples taken during the experiments. Next, the Se estimates were used to inform a mathematical model used to evaluate the effect of preclinical surveillance on blocking between-herd transmission.

Diagnostic performance.

To quantify the Se of qPCR, the infection process was subdivided into three phases: (i) the preclinical phase, the period in days from challenge to the onset of clinical signs; (ii) the clinical phase, the period from the onset of clinical signs (which was defined as the appearance of one or more vesicles on the feet, mouth, tongue, or nose) to the rupture of the first vesicles; and (iii) the early-recovery phase, which was the period from the rupture of vesicles to the end of monitoring (at ∼7 or 8 dpc).
Statistical analysis was undertaken by using generalized linear mixed models (GLMMs), where each calf was included as a random effect (to account for repeated observations of the same animals). To compare shedding levels during each of the three phases of the infection process, a GLMM with a Gaussian distribution was formulated, where the relative titer in the sample as measured by qPCR was the response variable and the phase of infection was the predictor variable. For the quantification the diagnostic Se of qPCR, each sample result was classified as positive/negative, and a GLMM with a binomial error distribution was fitted (16), where the sample result (i.e., positive or negative) was the response variable. GLMMs were fitted by using the lme4 package (24) with R statistical software (25).
All the ambient air samples taken by using the cyclone sampler were positive, and consequently, it was not possible to estimate the Se for this sample-test combination using a GLMM. Instead, the lower 95% confidence limits of the Se were estimated by using the Wilson score method (26).

Early detection of infected herds.

To evaluate the impact of using preclinical diagnosis for the early detection of infected herds during an epidemic, we combined the estimates of the diagnostic Se of the different sample type-PCR combinations in an early detection model developed previously (15). In brief, this model takes into account the infection and disease dynamics within an infected herd, where the daily prevalence of susceptible cattle, latently infected cattle, cattle in the incubation period, and infectious and recovered cattle is modeled by using a susceptible-exposed-infectious-recovered (SEIR) model, which also included a compartment for infected animals in the incubation period (see Fig. S3 in the supplemental material). This model also takes into account surveillance characteristics such as the diagnostic Se of the tests, the number of animals sampled at random (sample size) from the assessed herd, and the sampling frequency (sampling interval). All these characteristics, together with the prevalence at the time of sampling, define the Se of surveillance. The higher the Se, the lower the probability of an infected farm escaping detection within a sampling interval. Emergency surveillance starts when one (the first) infected farm is detected and control measures start. In this model, it is assumed that the spread of infection (or introduction) to at-risk farms (neighbors or dangerous contacts) takes place at any random (unknown) moment in time between the last time (a random day) when the farm was tested and the next one. A maximum of 7 days was used as a sampling interval (Table 3).
TABLE 3 Parameters in the mathematical model used to evaluate the effect of preclinical detection on the reduction of the risk of between-farm transmissiona
Within-herd infection dynamics
    Transmission rate (day−1)10.15See the supplemental material
    Latent period (days)4.65 (2.31)a
    Incubation period (days)4.33
    Infectious period (days)2.97 (5.94)a
Diagnostic and surveillance parameters
    Se for nasal swabs0.7795% LCL of estimated SebThis paper
    Se for probang0.91
    Se for saliva0.79
    Se for serum0.58
    Se for clinical inspection (days)8Avg time to detection during the 2001 epidemic29
    Se for clinical inspection0.4095% UCL of the Sec; this parameter value was used as an alternative to the 8-day delay used above15
    Se for cyclone (room air sampling)0.5595% LCL of estimated Seb; this Se is conditional on a minimum prevalence of infected animals (incubation period) of 10%; for a prevalence of <10%, Se = 0This paper
    No. of samples10No. of randomly sampled cows per herd; a minimum of 5 samples per herd was evaluated
    Frequency of sampling (no. of visits/wk)≥1Interval between visits to one herd
    Delay from detection to culling (days)1A 1-day delay between detection and culling of an infected herd was used for estimation of the reduction of infectious output
Between-herd transmission
    Rh3.295% UCL of the between-herd reproduction no. (Rh) estimated during the initial phase of the epidemic in 2001 in the UK30
Values in parentheses are parameter values changed for the sensitivity analysis and are similar to those used by Backer et al. (28).
See Fig. 3. LCL, lower confidence limit.
The Se of clinical inspection in partially immune populations was reported to be 0.31 (95% confidence interval, 0.22 to 0.40) (15). The upper confidence limit (UCL) of this estimate was used as an approximation to the Se in nonvaccinated populations.
These infection and surveillance parameters are combined to calculate the reduction in the infectious output due to surveillance and culling of infected animals on farms where the virus is detected and, hence, the reduction in the Rh. The list of parameters and parameter values used are presented in Table 3. To be conservative and sensitive to the potential limitations of using experimental data for the estimation of the Se in the field, we used the lower 95% confidence limits of the Se estimates (Table 3) for modeling.
For sensitivity analysis, parameters determining the infection dynamics in the model, specifically the durations of the latent and infectious periods, were also changed to evaluate the effect of a longer infectious period, before the onset of clinical signs (Table 3). The model was coded by using the deSolve package (27) with R statistical software (25).


We thank the animal services staff at The Pirbright Institute and WBVR for their assistance in running the animal experiments. We are also grateful for the advice and support offered by Caroline Wright, Joanne Stoner, and Christopher Sanders (Pirbright) and by Aldo Dekker and Phaedra Eblé (WBVR).
This study was funded by the Department for Environment, Food, and Rural Affairs (grants SE2814 and SE2815). Simon Gubbins also acknowledges funding from the Biotechnology and Biological Sciences Research Council (grant BB/E/I/00001717). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.


[This article was published on 23 May 2017 with a standard copyright line (“© 2017 American Society for Microbiology. All Rights Reserved.”). The authors elected to pay for open access for the article after publication, necessitating replacement of the original copyright line with the one above, and this change was made on 24 July 2017. The authors intend to publish an Author Correction announcing this change in the October 2017 issue of the Journal of Clinical Microbiology.]

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Knight-Jones TJ and Rushton J. 2013. The economic impacts of foot and mouth disease—what are they, how big are they and where do they occur?Prev Vet Med112:161–173.
The Royal Society. 2002. Infectious diseases in livestock. Policy document 10/02. The Royal Society, London, United Kingdom.
Fraser C, Riley S, Anderson RM, and Ferguson NM. 2004. Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci U S A101:6146–6151.
Charleston B, Bankowski BM, Gubbins S, Chase-Topping ME, Schley D, Howey R, Barnett PV, Gibson D, Juleff ND, and Woolhouse MEJ. 2011. Relationship between clinical signs and transmission of an infectious disease and the implications for control. Science332:726–729.
Orsel K, Bouma A, Dekker A, Stegeman JA, and de Jong MC. 2009. Foot and mouth disease virus transmission during the incubation period of the disease in piglets, lambs, calves, and dairy cows. Prev Vet Med88:158–163.
Pacheco JM, Brito B, Hartwig E, Smoliga GR, Perez A, Arzt J, and Rodriguez LL. 2017. Early detection of foot-and-mouth disease virus from infected cattle using a dry filter air sampling system. Transbound Emerg Dis64:564–573.
Stenfeldt C, Lohse L, and Belsham GJ. 2013. The comparative utility of oral swabs and probang samples for detection of foot-and-mouth disease virus infection in cattle and pigs. Vet Microbiol162:330–337.
Guinat C, Gubbins S, Vergne T, Gonzales JL, Dixon L, and Pfeiffer DU. 2016. Experimental pig-to-pig transmission dynamics for African swine fever virus, Georgia 2007/1 strain. Epidemiol Infect144:25–34.
Chase-Topping ME, Handel I, Bankowski BM, Juleff ND, Gibson D, Cox SJ, Windsor MA, Reid E, Doel C, Howey R, Barnett PV, Woolhouse ME, and Charleston B. 2013. Understanding foot-and-mouth disease virus transmission biology: identification of the indicators of infectiousness. Vet Res44:46.
Ryan E, Gloster J, Reid SM, Li Y, Ferris NP, Waters R, Juleff N, Charleston B, Bankowski B, Gubbins S, Wilesmith JW, King DP, and Paton DJ. 2008. Clinical and laboratory investigations of the outbreaks of foot-and-mouth disease in southern England in 2007. Vet Rec163:139–147.
Corzo CA, Culhane M, Dee S, Morrison RB, and Torremorell M. 2013. Airborne detection and quantification of swine influenza A virus in air samples collected inside, outside and downwind from swine barns. PLoS One8:e71444.
Dee S, Otake S, Oliveira S, and Deen J. 2009. Evidence of long distance airborne transport of porcine reproductive and respiratory syndrome virus and Mycoplasma hyopneumoniae. Vet Res40:39.
Mouchantat S, Haas B, Bohle W, Globig A, Lange E, Mettenleiter TC, and Depner K. 2014. Proof of principle: non-invasive sampling for early detection of foot-and-mouth disease virus infection in wild boar using a rope-in-a-bait sampling technique. Vet Microbiol172:329–333.
Waters RA, Fowler VL, Armson B, Nelson N, Gloster J, Paton DJ, and King DP. 2014. Preliminary validation of direct detection of foot-and-mouth disease virus within clinical samples using reverse transcription loop-mediated isothermal amplification coupled with a simple lateral flow device for detection. PLoS One9:e105630.
Gonzales JL, Boender GJ, Elbers AR, Stegeman JA, and de Koeijer AA. 2014. Risk based surveillance for early detection of low pathogenic avian influenza outbreaks in layer chickens. Prev Vet Med117:251–259.
Coughlin SS, Trock B, Criqui MH, Pickle LW, Browner D, and Tefft MC. 1992. The logistic modeling of sensitivity, specificity, and predictive value of a diagnostic test. J Clin Epidemiol45:1–7.
Bravo de Rueda C, de Jong MCM, Eblé PL, and Dekker A. 2015. Quantification of transmission of foot-and-mouth disease virus caused by an environment contaminated with secretions and excretions from infected calves. Vet Res46:43.
Gonzales JL, Barrientos MA, Quiroga JL, Ardaya D, Daza O, Martinez C, Orozco C, Crowther J, and Paton DJ. 2014. Within herd transmission and evaluation of the performance of clinical and serological diagnosis of foot-and-mouth disease in partially immune cattle herds. Vaccine32:6193–6198.
Donaldson AI, Herniman KA, Parker J, and Sellers RF. 1970. Further investigations on the airborne excretion of foot-and-mouth disease virus. J Hyg68:557–564.
Hyslop NSG. 1965. Airborne infection with the virus of foot-and-mouth disease. J Comp Pathol75:119–126.
Amaral Doel CM, Gloster J, and Valarcher JF. 2009. Airborne transmission of foot-and-mouth disease in pigs: evaluation and optimisation of instrumentation and techniques. Vet J179:219–224.
Colenutt C, Gonzales JL, Paton DJ, Gloster J, Nelson N, and Sanders C. 2016. Aerosol transmission of foot-and-mouth disease virus Asia-1 under experimental conditions. Vet Microbiol189:39–45.
Alexandersen S, Oleksiewicz MB, and Donaldson AI. 2001. The early pathogenesis of foot-and-mouth disease in pigs infected by contact: a quantitative time-course study using TaqMan RT-PCR. J Gen Virol82:747–755.
Bates D, Mächler M, Bolker B, and Walker S. 2015. Fitting linear mixed-effects models using lme4. J Stat Softw67:1–48.
R Core Team. 2015. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Brown LD, Cai TT, and DasGupta A. 2001. Interval estimation for a binomial proportion. Stat Sci16:101–117.
Soetaert K, Petzoldt T, and Setzer RW. 2010. Solving differential equations in R: package deSolve. J Stat Softw33:1–25.
Backer JA, Hagenaars TJ, Nodelijk G, and van Roermund HJ. 2012. Vaccination against foot-and-mouth disease I: epidemiological consequences. Prev Vet Med107:27–40.
Chis Ster I, Singh BK, and Ferguson NM. 2009. Epidemiological inference for partially observed epidemics: the example of the 2001 foot and mouth epidemic in Great Britain. Epidemics1:21–34.
Haydon DT, Chase-Topping M, Shaw DJ, Matthews L, Friar JK, Wilesmith J, and Woolhouse ME. 2003. The construction and analysis of epidemic trees with reference to the 2001 UK foot-and-mouth outbreak. Proc Biol Sci270:121–127.

Information & Contributors


Published In

cover image Journal of Clinical Microbiology
Journal of Clinical Microbiology
Volume 55Number 6June 2017
Pages: 1671 - 1681
Editor: Brad Fenwick, University of Tennessee
PubMed: 28330886


Received: 30 January 2017
Accepted: 11 March 2017
Published online: 23 May 2017


  1. FMD
  2. surveillance
  3. transmission
  4. early detection
  5. sensitivity
  6. diagnostics
  7. PCR
  8. foot-and-mouth disease virus



Noel Nelson
The Pirbright Institute, Pirbright, Woking, United Kingdom
Met Office, Exeter, United Kingdom
David J. Paton
The Pirbright Institute, Pirbright, Woking, United Kingdom
Simon Gubbins
The Pirbright Institute, Pirbright, Woking, United Kingdom
Claire Colenutt
The Pirbright Institute, Pirbright, Woking, United Kingdom
Emma Brown
The Pirbright Institute, Pirbright, Woking, United Kingdom
Sophia Hodgson
The Pirbright Institute, Pirbright, Woking, United Kingdom
The Pirbright Institute, Pirbright, Woking, United Kingdom
Wageningen Bioveterinary Research, Lelystad, the Netherlands


Brad Fenwick
University of Tennessee


Address correspondence to Jose L. Gonzales, [email protected].
N.N. and J.L.G. contributed equally to this work.

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