Department of Mathematics and Statistics, 16 Richmond Street, University of Strathclyde, Glasgow, G1 1XQ, UK

Animal Health and Veterinary Laboratories Agency, New Haw, Addlestone, Surrey, KT15 3NB, UK

Department of Mathematics, Mantell Building, University of Sussex, Falmer, Brighton, BN1 9RF, UK

Faculty of Veterinary Medicine, 464 Bearsden Road, Glasgow, G61 1QH, UK

Clinical Trials Research Unit, University of Leeds, Leeds, LS2 9JT, UK

Abstract

Background

Highly pathogenic avian influenza (HPAI) viruses have had devastating effects on poultry industries worldwide, and there is concern about the potential for HPAI outbreaks in the poultry industry in Great Britain (GB). Critical to the potential for HPAI to spread between poultry premises are the connections made between farms by movements related to human activity. Movement records of catching teams and slaughterhouse vehicles were obtained from a large catching company, and these data were used in a simulation model of HPAI spread between farms serviced by the catching company, and surrounding (geographic) areas. The spread of HPAI through real-time movements was modelled, with the addition of spread via company personnel and local transmission.

Results

The model predicted that although large outbreaks are rare, they may occur, with long distances between infected premises. Final outbreak size was most sensitive to the probability of spread via slaughterhouse-linked movements whereas the probability of onward spread beyond an index premises was most sensitive to the frequency of company personnel movements.

Conclusions

Results obtained from this study show that, whilst there is the possibility that HPAI virus will jump from one cluster of farms to another, movements made by catching teams connected fewer poultry premises in an outbreak situation than slaughterhouses and company personnel. The potential connection of a large number of infected farms, however, highlights the importance of retaining up-to-date data on poultry premises so that control measures can be effectively prioritised in an outbreak situation.

Background

For a wide range of epidemic infections, contact structures can be used to describe the potential transmission of infection in a population

In GB, the poultry industry can be divided into the primary breeding sector and the production sector. The biosecurity levels in the primary breeding sector are considered to be consistently high, making the probability of introduction of pathogens into this sector extremely low. In the production sector, birds are purchased from a primary breeding company when they are one day old. Birds then remain on specialist rearing farms until approximately eighteen weeks of age before they are moved to production farms or to hatcheries. Before meat birds enter the food chain, a catching company may be brought in to assist in the catching of birds to be sent to slaughter. Some catching companies may operate on multiple independently owned farms, and some farms may not use a catching company at all, choosing to send birds directly to the slaughterhouse. Vehicles used to transport birds between farms and slaughterhouses are often owned by the slaughterhouse and therefore may act as a link between different production farms. Partly due to the increase in the number and types of movements made on to and off production farms and partly due to increased exposure of birds to the environment in the production sector, it is here where diseases such as AIV have the opportunity to enter a farm, rendering the production sector the focus of this study.

It has already been shown that AIVs have the potential to be spread to a large number of poultry premises via movement of humans and fomites

Based on these new data, an individual farm-based transmission model was developed where nodes are poultry premises with links representing potential transmission routes between premises. Although the static approach (assuming all links between farms are potentially active) that we have previously adopted

Methods

Data sources

Movement data from a major catching company were obtained for all movements made over the 32 month period between 02/01/2005 and 11/08/2007. These data contain the times, dates and premises details for approximately 55,500 movements associated with 68 catching teams (within the company) over 415 poultry premises in GB. The premises associated with this catching company are distributed across GB, as shown in Figure

Distribution of catching company farms in GB

**Distribution of catching company farms in GB**. Map to show the distribution of poultry premises associated with the catching company studied. Each point represents a poultry farm.

Population data on all commercial poultry premises housing 50 or more birds, and within 15 km of farms visited by teams belonging to the catching company, were taken in November 2007 from an extract of the GB Poultry Register (GBPR), provided by the Department for environment, food and rural affairs (Defra). The addition of these premises allowed us to consider disease transmission that can occur between premises that are in close spatial proximity, enabling us to consider how likely infection is to jump from the network of premises for which we have movement data to another network of premises for which no movement data were available for analysis. The addition of these premises brought the number of premises studied up to 10,692. The total number of premises recorded in the GBPR extract studied was 24,075. Although the total number of premises serviced by the catching company is small (approximately 2% of all premises in the GBPR), by comparing the number of premises serviced by the catching company with the number of premises known to be associated with the largest catching companies in England and Wales, we estimate that between 30% and 50% of premises serviced by a major contracted catching company are accounted for in this dataset [13 and unpublished data]. No data were available for small, independent catching companies.

Premises in the catching company database were matched to premises in the GBPR so that the model could be parameterised using location, premises population size and species data. Temporal aspects were accounted for by preserving the order in which poultry premises were visited by individual catching teams on a daily basis. Additional information on which premises belong to integrated companies was obtained from a sample survey of integrated companies.

In the absence of quantitative movement data of company personnel for the farms studied, we sought expert opinion [P. Mcmullin, Poultry Health Services,

Model parameters.

**Parameter**

**Value**

**Source**

Incubation period

Up to 1 day (shedding after 8 hours, death after one day)

I. Brown (

Survival of virus on seed premises

Up to15 days

Probability of staff working on multiple premises (Farm size)

0.45 (< 50,000 birds)

0.1 (50,000 to 200,000 birds)

0 (> 200,000 birds)

P. McMullin (

Distance travelled by staff between premises

Up to 35 km

P McMullin (

Frequency of vet visits

Every 50 days

P. McMullin (

Frequency of manager visits

Every 10 days (non-layer farms)

Every 50 days (layer farms)

P. McMullin (

Probability catching team catches species (multi species farms only)

0.7 Chicken

0.12 Turkey

0.16 Duck/goose

0.01 Other

Calculated from catching company data, where species type available

Probability of catching team, slaughterhouse and owner transmission

0 to 0.2, in steps of 0.01, with additional parameter at 0.001 added.

N/A

Time to detection

2 to 6 days (15 days later for ducks/geese)

Extrapolation from

Parameter values for the network simulation model of avian influenza transmission in Great Britain

Descriptive analysis

A descriptive analysis of the collected data is given in Additional File

**Descriptive analysis of catching company data**.

Click here for file

Simulation model (see also Additional File

**Simulation modelling methods and outputs**.

Click here for file

A stochastic simulation model at the farm level was developed where farms were classed as susceptible, infected, detected or culled. HPAI could be transmitted between premises in close spatial proximity or through contact via catching teams, slaughterhouse vehicles or personnel movements within an integrated (multi-site) company. A random number generator chooses a premises in the network to infect and a random date of infection (within the 886 days covered by the catching company data set). If the seed farm was not visited within 15 days of this time point, assumed to be the maximum time that HPAI would survive in the farm environment, then transmission will be limited to local spread, often resulting in little or no onward transmission from the seed premises. When transmission beyond the seed premises did occur, outbreaks were allowed to run their course.

We assumed a time step of one day in the model, so that for each day of the simulation, once a premises had become infected, we assumed silent spread up to the time of detection. Detection and culling dates were set within the model at time of infection and were dependent on whether the infected premises was in a protection zone (PZ), a surveillance zone (SZ), or neither, as described below.

On detection of notifiable HPAI in poultry, 10 km SZs and 3 km PZs are typically set up around infected premises. In the model, we assumed no transmission within the PZ/SZ via the normal movement of catching companies or slaughterhouse equipment since all movements in those zones would be monitored. Therefore, spread would only continue within the PZ/SZ via local spread. Time to detection within an infected flock was assumed to occur between 2 and 6 days (mean at 4 days) after infection

Catching team and slaughterhouse movements

When a movement occurred between infected and susceptible farms, infection was spread between farms with probability relating to the type of movement made. Where multiple species were held on one farm, we assumed that catching teams catch, on average, one species per visit with probability defined in Table

Company (personnel) movements

We assume that spread of infection between farms belonging to the same multi-site, integrated company could occur either via the movements of area managers or veterinary officials between premises, or via staff working on multiple farms. Movements of veterinary officials, of area managers and of company personnel were simulated on a per day basis by using farm size and distance between farms belonging to the same multi-site company to identify using parameters in Table

Local (spatial) spread (see Additional Files

**Sensitivity to spatial spread**.

Click here for file

Based on expert opinion, we assume spatial (primarily airborne) spread in GB is likely to occur with small probability (p ≤ 0.01) and only for distances up to a maximum 0.5 km [D. Alexander, R. Irvine

A sensitivity analysis of the model to the assumption that airborne spread could occur is given in Additional File

Analysis of simulation output

We first consider the proportion of outbreaks that resulted in onward spread beyond the seed case. Here, the results follow a linear trend and, as the outcome is a binary variable (essentially secondary spread, or no secondary spread) dependent on explanatory variables that can be categorised into multiple levels, the analysis lends itself to a logistic regression. Thus a binary logistic regression was done (using Minitab v16) on the proportion of outbreaks that resulted in onward spread beyond the seed case. We next consider final epidemic size. In order to determine how the different types of transmission affect the epidemic size, two logistic regression models were fitted (Minitab v16). In the first, the binary response variable describes whether a small (< 25 premises) epidemic occurs or not. In the second, the binary response variable describes whether a large (> 65 premises) epidemic occurs or not. In both cases, the explanatory variables are the simulated transmission probabilities for AIV transmission via catching company, slaughterhouse- and owner-related movements.

Results

Additional File

1. When the frequency of movements is not accounted for, slaughterhouse related movements connected 94% of premises, catching team movements connected 76% of premises and owner movements 11% of premises that are associated with the catching company. However, when time is considered, catching teams connect only 2 premises per day and slaughterhouses an average of 3 premises per day.

2. Contrary to expectations, the data presented show that premises do use multiple slaughterhouses and are associated with multiple catching teams within the same company (consecutively over the time period studied). There is no overlap between poultry companies (i.e. poultry premises are associated with only one poultry company).

3. There is an increase in frequency of visits to larger premises, implying that these premises will be at a higher risk of infection should infection be transmitted by catching team or slaughterhouse vehicles/equipment.

4. Slaughterhouse vehicles and catching teams travel long distances between premises, with 72% of movements between premises exceeding 10 km in length.

Simulation modelling

One hundred simulations were run for a total of 10,648 scenarios. Each scenario represents a different combination of transmission values for each of the transmission routes studied. In particular, each scenario was created by ranging parameters from 0 to 0.2, in a step-wise fashion, such that each parameter took on one of 22 possible values within this range, giving rise to ^{3 }= 10,648

Proportion of positive epidemics spread beyond the index case

The aims of the simulation model are to determine if a large outbreak of AIV is possible in the poultry industry in GB and, if so, what might cause a large outbreak to occur. One way of answering the first question is to consider how often infection spreads beyond the seed premises. That is to ask "how many simulated outbreaks result in secondary spread?"

When all scenarios and all simulation results are considered together, infection spread beyond the seed premises in approximately 15% of the simulations run (mean value over all simulations and all scenarios). Figure

The proportion of outbreaks that spread beyond the seed premises for all simulation results

**The proportion of outbreaks that spread beyond the seed premises for all simulation results**. Boxplots to show the median, quartiles and outer points of the proportion of outbreaks (over 100 simulations) that spread beyond the seed premises, for increasing rates of transmission. Here, transmission is recorded as the combined risk of AIV transmission over all routes, according to Equation 2.

for _{j }

Infection resulted in secondary spread (beyond the seed premises) in up to 35% of scenarios. The simulation that gave the maximum number of cases that spread beyond the seed premises was from the following scenario: catching team (cc) = 0.04, company personnel (owner) = 0.19 and slaughterhouse (sh) = 0.13. This suggests that high probabilities of transmission are not necessary in all three potential transmission routes for infection to (relatively) frequently spread beyond the index case.

Results from a logistic regression analysis are shown in Tables

Binary logistic regression, with odds ratios calculated for the probability of secondary spread versus catching company transmission rates

**Transmission rate**

**Odds Ratio**

**Lower 95% CI**

**Upper 95% CI**

**p-value**

0.001

0.97

0.93

1

0.064

0.01

1

0.97

1.04

0.813

0.02

0.95

0.92

0.98

0.005

0.03

0.97

0.94

1.01

0.14

0.04

1.02

0.98

1.06

0.303

0.05

0.96

0.93

0.99

0.024

0.06

0.96

0.93

1

0.043

0.07

0.99

0.95

1.02

0.451

0.08

0.96

0.93

1

0.038

0.09

0.95

0.92

0.99

0.011

0.1

0.97

0.94

1.01

0.124

0.11

0.98

0.94

1.01

0.23

0.12

0.99

0.96

1.03

0.677

0.13

1

0.97

1.04

0.906

0.14

0.98

0.94

1.01

0.187

0.15

0.98

0.95

1.02

0.267

0.16

0.96

0.92

0.99

0.018

0.17

1

0.96

1.03

0.871

0.18

0.98

0.95

1.02

0.313

0.19

0.96

0.93

1

0.047

0.2

0.98

0.95

1.02

0.359

Binary logistic regression, with odds ratios calculated for the probability of secondary spread versus owner transmission rates

**Transmission rate**

**Odds Ratio**

**Lower 95% CI**

**Upper 95% CI**

**p-value**

0.001

0.98

0.94

1.03

0.488

0.01

1.09

1.04

1.14

0

0.02

1.19

1.14

1.24

0

0.03

1.27

1.22

1.33

0

0.04

1.38

1.33

1.44

0

0.05

1.42

1.37

1.48

0

0.06

1.47

1.41

1.53

0

0.07

1.53

1.47

1.59

0

0.08

1.66

1.59

1.72

0

0.09

1.74

1.67

1.81

0

0.1

1.75

1.69

1.83

0

0.11

1.86

1.79

1.94

0

0.12

1.97

1.89

2.04

0

0.13

1.94

1.87

2.02

0

0.14

2.11

2.03

2.19

0

0.15

2.09

2.01

2.17

0

0.16

2.19

2.11

2.27

0

0.17

2.24

2.16

2.33

0

0.18

2.33

2.24

2.42

0

0.19

2.38

2.29

2.47

0

0.2

2.38

2.29

2.47

0

Binary logistic regression, with odds ratios calculated for the probability of secondary spread versus slaughterhouse transmission rates

**Transmission rate**

**Odds Ratio**

**Lower 95% CI**

**Upper 95% CI**

**p-value**

0.001

0.97

0.93

1.01

0.093

0.01

0.99

0.95

1.02

0.435

0.02

0.99

0.95

1.03

0.597

0.03

1

0.96

1.03

0.861

0.04

1

0.96

1.03

0.824

0.05

1

0.96

1.04

0.927

0.06

1.04

1

1.08

0.036

0.07

1.03

1

1.07

0.068

0.08

1.04

1.01

1.08

0.019

0.09

1.04

1.01

1.08

0.019

0.1

1.05

1.01

1.09

0.012

0.11

1.07

1.03

1.11

0

0.12

1.05

1.01

1.09

0.008

0.13

1.09

1.05

1.13

0

0.14

1.08

1.04

1.12

0

0.15

1.09

1.06

1.13

0

0.16

1.08

1.04

1.12

0

0.17

1.08

1.04

1.12

0

0.18

1.09

1.06

1.13

0

0.19

1.1

1.06

1.14

0

0.2

1.1

1.06

1.14

0

In order to visualise the effect that the interaction of different transmission routes can have on the results, each potential transmission route was considered on its own as well as in combination with one or more other potential routes of transmission. Figure

The proportion of outbreaks that spread beyond the seed premises for different parameter combinations

**The proportion of outbreaks that spread beyond the seed premises for different parameter combinations**. Boxplots of the proportion of outbreaks that result in spread beyond the seed premises, for different parameter combinations. gp1 = sh, gp2 = owner, gp3 = cc, gp4 = owner and sh, gp5 = cc and sh, gp6 = cc and owner, gp7 = cc, owner and sh. Within each group, parameters are varied from 0 to 0.2.

The statistical significance of interaction terms can be determined by refitting the logistic regression model, with interaction terms included. As the model did not converge when all tested transmission rates were considered as a single level, in order to consider the potential interaction between different networks the data were categorised into "high", "medium" and "low" probabilities of transmission and the model refitted (see Additional File

**Results - supplementary tables**.

Click here for file

Binary Logistic regression: secondary spread versus transmission rates for interaction between transmission routes at different levels of transmission.

**Predictor**

**Coefficient**

**SE**

**Odds Ratio**

**Lower 95% CI**

**Upper 95% CI**

**p-value**

Constant

-2.16587

0.071836

0

owncat

1

0.004215

0.076562

1

0.86

1.17

0.956

2

0.415397

0.075335

1.51

1.31

1.76

0

3

0.631829

0.074869

1.88

1.62

2.18

0

shcat

1

-0.22299

0.077486

0.8

0.69

0.93

0.004

2

-0.12265

0.077053

0.88

0.76

1.03

0.111

3

-0.0784

0.076875

0.92

0.8

1.07

0.308

owncat*shcat

1*1

0.248773

0.082484

1.28

1.09

1.51

0.003

1*2

0.227419

0.082041

1.26

1.07

1.47

0.006

1*3

0.229411

0.081855

1.26

1.07

1.48

0.005

2*1

0.207304

0.0812

1.23

1.05

1.44

0.011

2*2

0.154659

0.080772

1.17

1

1.37

0.056

2*3

0.140458

0.080593

1.15

0.98

1.35

0.081

3*1

0.228509

0.0807

1.26

1.07

1.47

0.005

3*2

0.174293

0.080272

1.19

1.02

1.39

0.03

3*3

0.15525

0.080095

1.17

1

1.37

0.053

Final model.

Level 1 = low transmission rate 0 - 0.06, level 2 = medium transmission rate 0.07 - 0.13, level 3 = high transmission rate 0.14 - 0.2. sh = slaughterhouse, own = company personnel. SE = standard error.

Epidemic size

In this part of the analysis, only epidemics that result in spread beyond the seed premises are considered. This accounts for approximately 15% of all simulation results.

For all results, there were no epidemics of size between 23 and 66 premises (see Figure

Epidemic size

**Epidemic size**. Histogram of epidemic size for infections resulting in onward spread beyond the seed premises. a) epidemics including fewer than 25 infected premises and b) epidemics including more than 65 infected premises.

There were a total of 330 individual premises that were included in the set of outbreaks that resulted in onward spread (~80% of population for which movement data were available). Of these, 95 individual premises were seed premises in the (249) "large" epidemics recorded. All 95 of these premises were also seed premises in the list of (130939) "small" epidemics. Premises size (number of birds) was available for 78% of seed premises for large epidemics, and for 94% of seed premises for small epidemics. The results (Table

Effect of seed premises on outbreak size.

**Seed premises size**

**Number unique premises in small epidemics (seed)**

**Number unique premises in large epidemics (seed)**

**Proportion outbreaks resulting in large epidemics**

Small (≤ 100,000 birds)

35

20

0.57

Medium (100,000 - 200,000 birds)

59

17

0.29

Large (> 200,000 birds)

141

37

0.26

The proportion of outbreaks that result in large/small epidemics for different size categories of seed premises.

In order to determine how the different types of transmission affect the epidemic size, two logistic regression models were fitted. In the first, the binary response variable describes whether a small (< 25 premises) epidemic occurs or not. In the second, the binary response variable describes whether a large (> 65 premises) epidemic occurs or not. In both cases, the explanatory variables are the simulated transmission probabilities for AIV transmission via catching company, slaughterhouse- and owner-related movements. The results are shown in Additional File

For small epidemics, Additional File

Additional File

Spatial spread

While the majority of outbreaks did not result in further onward transmission from the index premises, outbreaks could potentially cover up to 20% of the population (for a range of parameter values < 0.2) for which network data were available, covering distances of up to 730 km between premises (see Additional File

**Maximum distance between infected premises**.

Click here for file

In this model, infection can only be spread into premises that are not serviced by the catching company, by spatial transmission of disease, to premises within 500 m of infected premises. This results in infection of premises that are potentially connected to different sub-networks (via other catching companies, slaughterhouses or poultry companies for example) in less than 1% of the simulations run. However, we have seen that transmission via sh-linked movements is an important factor in determining final epidemic size, and slaughterhouses that are included in the network studied may also be used by poultry premises not included here. This implies that if this route is important, infection may leak into other sub-networks of the industry much more frequently.

Discussion

Despite the extent of data previously available on the British poultry industry, the detailed contact structures within the poultry industry in GB have only been poorly understood. Previous studies have been able to identify potential contact structures but assumptions have had to be made on the frequency and patterns of movements between farms

The results presented here show that restrictions on the frequency of movements can have an important role in determining disease spread risk. In particular, connections via slaughterhouses can connect a large number of premises over a large geographical area, important in the potential for virus dissemination. Spread via slaughterhouse-linked movements is most prominent when partial flock depopulation is being undertaken at a farm, as this action results in more premises being visited in one day and potential infection of birds that remain on the farm. This is also an important output for the control of diseases other than HPAI, such as

Despite the relatively heavy use of expert opinion to estimate model parameters in this study - in particular for the frequency of movements made by company personnel, we can use the model presented here to hypothesise about the importance of different types of potentially infectious links between poultry premises and we can conclude from these results that, where slaughterhouses can act as a reservoir for pathogens, the spread via this route should be minimized. This can be achieved through additional bio-security measures, such as thorough cleaning of the crates and vehicles that carry the birds, for example.

The results that catching team movements have little effect both on the probability of an outbreak resulting in onward spread beyond the seed premises and on the probability of a large epidemic occurring are important results, as they suggest that the number of farms that a catching team visits during the infectious period of the virus is too low to link a high number of farms, in GB, during an epidemic. For pathogens that can survive for longer periods in the environment or that are more prevalent than HPAI (such as Campylobacter spp.), the number of farms that can be linked by catching team movements will be (potentially significantly) higher. However, while extensive and therefore of value, the data used here correspond to only one (large) catching company that is made up of a 68 distinct catching teams. As each farm may be visited by one or more of the catching teams, there are no distinct regional divisions apparent within this company as was initially expected. Further, these data do not consider further spread once other networks (e.g. connected by slaughterhouses and catching companies) contain infected premises.

Although all three transmission routes were positive when a large proportion of (simulated) outbreaks resulted in spread beyond the seed premises, the fitting of a regression models suggests that only company personnel movements significantly influence the probability that infection will spread beyond the seed premises. This highlights the importance of obtaining more accurate estimates on the frequency of movements of company personnel and the probability of transmission via this route.

There was a significant interaction effect for the owner*slaughterhouse interaction on the proportion of outbreaks that result in onward spread. However, the combinations of potential transmission of disease via catching company and company personnel movements, or slaughterhouse-linked and catching company movements have little effect on the proportion of outbreaks that result in onward spread, particularly compared to the individual owner effect. This can be explained by the frequency of movements relative to premises size (Additional File

The results show that there is a "jump" from epidemics of size lower than 23 infected farms (< 5% of premises), to epidemics containing more that 65 infected farms (~20% of premises). This is in line with results published by _{0 }
_{0 }

When comparing the results for small epidemics against those for large epidemics, two factors that differ significantly between the two categories are worth noting: the effect of the probability of transmission via slaughterhouse movements and seed premises size. Large epidemics are up to 28 times more likely for higher levels of slaughterhouse transmission (compared to zero), implying that the characteristics of the network of slaughterhouse links are maintained even when a time component and control measures are added, resulting in connectivity between a higher proportion of premises via this route than via any other route. This result confirms that slaughterhouses are an important factor in this model. The size of seed premises plays a role here as there is an increase in frequency of catching team and slaughterhouse visits to larger premises (Additional File

We note that all slaughterhouses that appear in the movement data analysed are recorded as slaughtering birds from farms that are not visited by the catching company studied. This implies that the network of premises studied is not closed; with up to 131 additional farms sending birds to the same slaughterhouse (unpublished data), the possibility of disease spreading into other sub-networks within the industry is potentially high. It is therefore very important to ensure the data held on slaughterhouses and their customers is both complete and up to date. This will enable better prioritisation of the potentially large number of premises that could undergo surveillance in an outbreak situation.

Our results show that the distribution of poultry premises in GB is not dense enough for airborne transmission of AIV contribute significantly to between premises spread amongst premises recorded in the GBPR, so long as the distance for airborne transmission is less than 500 m. This has not been the case in past outbreaks in other countries, such as the Netherlands and Italy, where local spread is likely to have played a role in the transmission of disease from one farm to another. Should a virus strain that can easily transmit via airborne transmission be modelled, then local spread may result in spread between premises that have no other direct connections. For other virus strains, this could have a large impact on the proportion of outbreaks resulting in spread beyond the seed premises and the maximum epidemic size. This implies that there is possible scope to reduce the size of the 10 km SZs, freeing resources for use elsewhere. This could be explored further by using network data currently available to explore how large a SZ should be, taking into account resource constraints and simulating over a range of assumptions regarding transmission rates. The mean number of premises affected by an epidemic may be dependent not only on the underlying epidemiological parameters, but also on the total resources available. Resource constraints were not included in this model but the model could be adapted to aid future work in this area, important for exploring optimal resource allocation in order to provide the most efficient detection of AIV and the curtailing of the outbreak.

Conclusions

Previous work has shown that large proportions of the poultry industry are potentially connected by catching companies and by slaughterhouse

In line with previous work

Authors' contributions

MA designed the study. JED collected the data. JED, RRK and IZK analysed the data. JED wrote the simulation model. JED wrote the manuscript with input from MA, RRK and IZK. All authors have read and approved the final manuscript.

Acknowledgements

We would like to thank Defra for funding the project. We also thank Prof George Gettinby and Dr. Louise Kelly for statistical input at the final stages. Further, we gratefully acknowledge the following for helpful comments on the manuscript: Alasdair Cook and Richard Irvine (Animal Health and Veterinary Laboratories Agency) and Victor Del Rio Vilas (Defra). Finally, we would like to thank Lucy Snow (Animal Health and Veterinary Laboratories Agency) for her help with data collection.