Livestock disease controls are often linked to movements between farms, for example, via quarantine and pre- or post-movement testing. Designing effective controls, therefore, benefits from accurate assessment of herd-to-herd transmission. Household models of human infections make use of R*, the number of groups infected by an initial infected group, which is a metapopulation level analogue of the basic reproduction number R0 that provides a better characterization of disease spread in a metapopulation. However, existing approaches to calculate R* do not account for individual movements between locations which means we lack suitable tools for livestock systems. We address this gap using next-generation matrix approaches to capture movements explicitly and introduce novel tools to calculate R* in any populations coupled by individual movements. We show that depletion of infectives in the source group, which hastens its recovery, is a phenomenon with important implications for design and efficacy of movement-based controls. Underpinning our results is the observation that R* peaks at intermediate livestock movement rates. Consequently, under movement-based controls, infection could be controlled at high movement rates but persist at intermediate rates. Thus, once control schemes are present in a livestock system, a reduction in movements can counterintuitively lead to increased disease prevalence. We illustrate our results using four important livestock diseases (bovine viral diarrhoea, bovine herpes virus, Johne's disease and Escherichia coli O157) that each persist across different movement rate ranges with the consequence that a change in livestock movements could help control one disease, but exacerbate another.
Livestock diseases have an important impact not only on the economy and animal welfare [1,2], and can also pose a zoonotic risk to humans [3–5]. Many are introduced into herds via movements of infected animals, e.g. bovine tuberculosis (bTB), brucellosis, bovine viral diarrhoea (BVD), scrapie, foot-and-mouth disease (FMD) and Johne's disease [5–11]. Livestock disease control is therefore often implemented at the point of between-farm movement [12,13]. Controls that target infected animals moving between farms, include vaccination, quarantine, restricting movement for farms found to have infected animals, or even international movement restrictions . This leads us to the question of how hard a disease is to control when control is directed at herd-to-herd spread and how disease control effort depends on the rate of livestock movement.
The usual metric for assessing the required degree of control is the basic reproduction ratio, R0 [15–17]. R0 is the number of secondary infectives following introduction of a single typical primary infected individual into an entirely susceptible population. If R0 > 1, then a disease can invade, whereas if R0 ≤ 1 it cannot. The aim of disease intervention is often described in terms of reducing effective R0 to below 1. For example, if a proportion q of the population is vaccinated, then the effective R0 is R = (1 − q)R0, giving the critical coverage to prevent disease spread of qc = 1 − 1/R0 [15,18].
However, R0 is an individual-based rather than a group-based metric, and a system with high R0 could have high within-group (i.e. within farm) transmission, but only low between-group (between farm) transmission ; R0 can therefore be poor at describing transmission within metapopulations , such as the risk of disease in one farm spreading to others, e.g. via livestock movements.
There have been several approaches to address this deficiency. Early patch-based models proved analytically tractable, but only considered the infected status of a patch as a whole, and assumed that the timescale of reaching a quasi-stationary state was short relative to movement dynamics [21–23]. This sort of simple model has sometimes failed to predict more complex and unintuitive disease dynamics . Household models examine disease persistence within a metapopulation of a large number of small groups (e.g. households), and typically assume that disease spreads between groups that share individuals (e.g. children mix with other children at school, the adults mix with other adults at work and both return to the household), or that proximity is sufficient (e.g. transmission between patches of plant populations) [25–27]. However, household models neglect more long-lived movements such as those from livestock moving between farms, or wildlife dispersing from their natal range [25,28,29], and in doing so ignore the depletion of infectives from the primary group.
In the context of household models, Ball & Neal [30–32] introduce R*, a group-level analogue of R0, describing the number of secondary infected groups generated by a primary infected group. As with R0, R* = 1 provides a threshold for disease spread within the metapopulation. Similarly, 1 − 1/R* provides the degree of disease intervention necessary to prevent disease spread. In situations where disease control is at the group-to-group level, R* provides a convenient alternative to R0 for predicting levels of disease control required, especially in highly heterogeneous metapopulations.
Here, we derive R* using the next-generation matrix (NGM) technique [33,34], for a generic metapopulation model with disease spread via explicit animal movements that does account for the depletion of susceptibles from the primary group. In the simplest case, R* takes an intuitive form in terms of the movement rate, the within-herd prevalence and the within-herd persistent time. We demonstrate that R* peaks at intermediate movement rates, revealing ranges of movement rates where disease intervention will be most difficult.
We illustrate our findings for four important livestock infections—bovine herpes virus (BHV), bovine viral diarrhoea virus (BVDV), Mycobacterium avium ssp paratuberculosis (paraTB) and Escherichia coli O157 (E. coli O157)—showing that a reduction in movement rates could counterintuitively result in an increase in disease prevalence, and moreover, that control to reduce one disease could exacerbate another.
1.1. The next-generation matrix approach
In a recent helpful overview of NGM approaches, Diekmann et al. cover their use over a wide range of single population disease models [34,35], but do not consider metapopulation models. The NGM provides a natural basis for the calculation of R0. In brief, the approach is to obtain a matrix K, where the entries Kij represent the expected number of new cases with state-at-infection i, arising from one individual with state-at-infection j. R0 is the dominant eigenvalue of this matrix.
To illustrate the technique, consider a single population with SEIR disease dynamics: where individuals are born into susceptible state S, following infection they enter exposed state E and incubate the disease, then progress to the infectious state I, and finally recover to the immune state R. N is the population size, μ is the per capita mortality rate, here set equal to the birth rate, β is the disease transmission coefficient, 1/α is the average incubation period and γ is the per capita recovery rate. The state space is the vector .
To obtain K, first linearize around the disease-free equilibrium, , giving for small E and I the linearized infectious subsystem where only the production of new infectives and changes in the state of existing infectives are captured. The linearized subsystem is the form , where and is the Jacobian matrix.
Now, decompose A into the sum of two matrices , where is the matrix of transmissions, where TEI represents the rate at which newly infected individuals in state E are created by infectious individuals I, and is the matrix of transitions, where, for example, is the rate at which individuals move into state I from state E. Negative entries represent a net flow out of the state in question; hence, shows the rate at which individuals that start in E leave this class, without returning. The matrix is interpreted biologically as the matrix of sojourn times . Thus, the entries of the first column of matrix are the expected time spent in states E and I conditional on starting in state E (likewise entries of the second column are the expected times conditional on starting in state I).
The NGM with large domain, KL, is given by the matrix product of transmission rate and residence time, that is , and so where, for example, KEI is the number of infections of type E generated by an index case in the I class. R0 is then the dominant eigenvalue of KL
By including only the rows and columns of KL related to categories of state-at-infection (i.e. exposed E, but not infectious I), KL can be reduced to the NGM matrix K which is smaller and mathematically easier to work with, and has a biological interpretation convenient for direct construction using epidemiological principles . The dominant eigenvalue is the same for both KL and K, and either may be used to calculate R0.
In the scalar case above, R0 = KEE, which, on taking the limits and , reduces to the familiar for the SIR model. This calculation for the SIR model also follows by identifying transmission T = β and the transition rate , whence the time spent infectious is , and the expected number of secondary infections from an index case in an otherwise susceptible population is . The NGM approach thus rigorously extends such arguments to more complex settings.
2. Next-generation matrix approach for homogeneous metapopulation dynamics with one disease category
We now apply the NGM approach to disease spread among a metapopulation of livestock herds, first illustrating the approach for a disease system with one disease category, and then showing how this may be naturally extended to more complex diseases. We show that R* may be given by the intuitive formula with per capita movement rate κ, herd size N, herd expected infectious lifetime Tinf and average prevalence of infectives during the infectious lifetime Ppos. This is conceptually similar to the SIR model formula if one considers substituting the rate at which new infectious individuals are formed, β, with the average rate at which infectives leave herds κNPpos, and substituting in the expected infectious period, 1/γ, with the expected time disease persists in the herd, Tinf.
2.1. Derivation of R*
Consider an SIS disease dynamic in a metapopulation of herds each containing N individuals. In the absence of infection, individuals die and are replaced with susceptibles at per capita rate μ. We assume frequency-dependent disease transmission with transmission rate β, recovery at per capita rate γ and a per capita movement rate between herds of κ.
For analytic tractability, we maintain constant herd size by assuming that the birth and death processes are coupled. Thus, the status of each herd may be defined by just the number of infectives, i, because the number of susceptibles is s = N − i. We consider a homogeneous metapopulation where we assume undirected movement between herds, and that movements are equally likely between any herds.
We choose to represent the metapopulation dynamics using the master equation approach (also known as the Chapman–Kolmogorov forward equation, see  for a detailed explanation) that allows us to capture the probability of a herd being in a state with i infectives. We begin by considering the respective rates l(i) and g(i) at which infectives are lost and gained. We assume when an animal leaves a herd it is replaced with a susceptible or infected individual in proportion to their prevalence in the metapopulation. Consequently, l(i) and g(i) depend on PS and PI, the mean prevalence of susceptibles and infectives in the metapopulation, i.e. where pi is the probability that a herd contains i infectives.
In a herd with i infectives, the net loss of infectives owing to movements is (because a proportion PS of replacements are susceptible); therefore, the net loss of infectives via mortality, recovery and movement is
Similarly, the net gain of infectives owing to movements is equal to the net loss of susceptibles, which is given by . Therefore, the net gain of infectives via disease transmission and movement is where s = N − i.
We may now write down the master equation governing the probability pi(t) of a herd containing i infectives at time t: for i = 1,2, … ,N and subject to
Here, is a vector of length N + 1. Removing the disease-free state i = 0, gives q(t), a vector of length N describing the probability of i infectives in the infectious subsystem.
To determine R*, we first linearize around the disease-free state . For qi close to the disease-free state for i = 1, … ,N, we obtain and
This can be written in matrix form where A is the Jacobian matrix, and is decomposed into , where T is the matrix of transmissions, and is the matrix of transitions. Here where δij = 1 if i = j and 0 otherwise, i.e. and
For the metapopulation model, the interpretation of T and differs from the single population model as follows. In a single herd model, T describes the production of new infections via within-herd transmission ; however, in the metapopulation model, it represents the production of new infected herds via movement of infected individuals from an infected herd to a susceptible one. In a single herd model, represents transitions between different disease states; in the metapopulation model, it represents the transitions between different states (in this case, different numbers of infectives) of an infected herd via within-herd transmissions, recoveries or mortalities, and movement of infectives to already infected herds.
As above, the matrix is the matrix of sojourn times, where the entry Sij is the expected time that a herd currently observed in state j will thereafter spend in state i. Because infected herds are assumed to begin with a single infective (i.e. i = 1), the total expected infectious period, Tinf, is the sum of the times spent in each state, i.e. the sum of the entries in column 1, gives
As T is zero everywhere other than the first row, the NGM of large domain is also zero everywhere except the first row (in this case, the dominant eigenvalue of KL is equal to the first entry of KL). The only state-at-infection is I1, and so K = [KL]11. Thus, R* is given by
The expected proportion of time spent by a herd in state i, having started in state 1, is given by Si1/Tinf. Using this, we now define the expected prevalence in an infected herd, Ppos, by
Rearranging, we obtain 2.1
Therefore, R*, the expected number of secondary infected herds, is (intuitively) given by the product of the expected rate at which infectives leaving a herd (κNPpos), and the duration of the infection in a herd (Tinf). This form is instructive, both because of its close relation to the definition of R0 via β and γ, and because calculating directly may be computationally infeasible for even moderately complicated models, but it can be relatively straightforward to calculate Ppos and numerically (see §2 of the electronic supplementary material).
2.2. Dependence of R* on movement rate, R0, heterogeneity and implications for control
2.2.1. Features of R*
In this section, we illustrate the features of R* within a metapopulation of herds using SIS model dynamics. We use the formulation of R0 that reflects the primary infective's total capacity to generate secondary cases, irrespective of movement between herds (see §1.1). Then, for an underlying R0 > 1, R* is zero in the absence of movements, rises above 1 as the movement rate increases, peaks at an intermediate movement rate, and then declines to 1 from above (figure 1a). Note that, for an underlying R0 ≤ 1, R* approaches 1 for large movement rates from below (not shown). R* provides a threshold for persistence of infection in the metapopulation as indicated by the quasi-equilibrium proportion of infected herds: zero for R* ≤ 1, and greater than zero for R* > 1 (figure 1b).
R* initially rises, because the disease multiplies within the herd before infectives are exported to other herds via movement. However, R* eventually declines as it becomes more likely that the primary infective leaves the herd before it has a chance to transmit infection within the herd (or recover or die). This results in an intermediate peak occurring when movement is low enough that the disease is sustained within the herd, but fast enough that it can reach other herds before being removed by stochastic extinction.
The peak in R* increases in magnitude and shifts to lower movement rates as R0 increases (figure 2a) with a corresponding shift in the threshold for persistence (figure 2b). In addition, for the same R0, slowly progressing diseases (i.e. those with a low recovery rate; figure 2a, red curves) have a higher equilibrium proportion of infected herds at the same movement rate than a rapidly progressing disease (i.e. those with a high recovery rate; figure 2a, blue curves), with corresponding shifts in the threshold for persistence (figure 2b).
From the expression for R* (equation (2.1)), we see that herd size N contributes to R* via the number of movements out of the herd and also via its potential effect on the prevalence when infected, Ppos, and the persistence time, Tinf. The net result is substantial nonlinear increases in R* with increased herd size, but relatively little change in the position of the peak in R* (figure 2c).
2.2.2. Implications for control
To simulate the effect of control measures, we define, for any set of individuals selected to move, p to be the proportion of infectious individuals that are treated or prevented from bringing the disease into another group. This interception occurs at the point of movement, therefore, only the remaining proportion 1 − p of individuals successfully carry the infection to another herd. We therefore obtain the effective reproduction ratio in the presence of control, R*(p), which is This leads to an important result. Disease can spread in the presence of control only if R*(p) remains above 1, leading to ‘islands’ of persistence (figure 3b).
Using our metapopulation model (see §2 of the electronic supplementary material), we calculated R*(p) for a range of levels of disease intervention and the corresponding equilibrium proportion of infected herds in the metapopulation. Near the R* peak (intermediate movement rate), even high levels of disease intervention may fail to control the disease (figure 3a, yellow curve), but when the movement rate is high even low levels of control may be sufficient to reduce R*(p) to below 1 and prevent the disease from spreading.
Consequently, for a range of intermediate values of the movement rate the infection persists in the meta-population for a given level of control, but at higher or lower movement rates, infection cannot persist under the same level of control (e.g. figure 3b, yellow curve). The range of values of the movement rate for which disease persists depends on the level of control applied.
3. Next-generation matrix approach for heterogeneous systems
In this section, we demonstrate how R* may be constructed for the more complex disease systems and explore the impact of such heterogeneity on R*.
3.1. Multiple disease categories
Consider a disease with two possible infectious states: types A and B (e.g. a regular shedder and a supershedder). As above, we assume the herd size N is constant, so the herd has potential disease states xa,b, where a and b correspond to the number of individuals in a herd in categories A and B, respectively; here , and the disease-free state is . The state space x(t) is obtained by enumerating over all the possible infected herd states, and then the NGM with large domain KL needed to calculate R* may be constructed by proceeding as before. Here, we illustrate the process.
Because infection in a herd is initiated by one individual, a disease with a single infectious category has one entry point x1, whereas with two infectious categories, there are two entry points: x1,0 and x0,1, depending on which type of infective first enters the disease-free herd. Therefore, in this case, the transmission matrix T (and hence also KL) has two rows with non-zero entries, and therefore, K is a 2 × 2 matrix.
Any given herd state can be reached by a limited number of adjacent states via the various event types, and each row in will have as many entries as possible transitions (e.g. here under the constraint of fixed herd size N there are four, corresponding to increases in A and B due to infection, and decreases owing to recovery or mortality). The matrix of sojourn times is dense, but as above we exploit the fact that the columns corresponding to the entry points determine the total infection duration Tinf, which now depends on which entry point is reached (i.e. we must now consider both and ).
Extracting the elements of KL relating to the entry points, we obtain the reduced NGM K, which has entries: where, for example, means the expected prevalence of B given entry point A, and is the expected duration of the infection in the herd given entry point A.
Here, KBA is the number of secondary herds initially infected by a class B individual that are caused by a primary infected herd initially infected by a class A individual. This is convenient, as it means that the entries in K may be computed via simulation of a single herd, by seeding an infection with an infective of category j, and approximating each entry Kij as the number of category i infectives moving to susceptible herds (averaged over a sufficiently large number of repeated simulations).
Note that because R* is a function of all entries in K, it is possible that a disease with multiple infectious categories may have multiple R* peaks (this phenomenon is just distinguishable in the curve for BHV in figure 5).
3.1.1. Example illustrating the impact of within-herd heterogeneity in infectiousness
Consider a livestock infection such as E. coli O157, which exhibits substantial heterogeneity between individuals in transmissibility [37–39]. Here, we characterize this heterogeneity using a simple low shedder–high shedder version of an SIS model which we call the SLHS model (see §1.2 of the electronic supplementary material). We assumed that susceptibles S become either supershedders H (high) or regular infectives L (low), with probabilities p and 1 − p, respectively, and that supershedders are η times more infectious than regular infectives. To illustrate the effect of heterogeneity on R*, we chose η, p and a normalizing constant (see §1.2 of the electronic supplementary material) to ensure that R0 remains constant as we vary the relative contributions to transmission from the low and high shedders.
We calculate R* by simulating herds where the initial infection is either a low or high shedder, and each case populates a column in the NGM as described in §3.1.
The highest R* comes from the most homogeneous disease transmission (figure 4a). The explanations for this are a combination of (i) susceptible depletion, i.e. while R0 (which ignores susceptible depletion) remains constant, the initial supershedder, when highly infectious, is unable to reach its full potential owing to a lack of susceptibles and (ii) an increased chance of stochastic extinction when the majority of the transmission is due to the relatively rare supershedders.
3.2. Between-herd heterogeneity
We now consider the case of an SIS disease with heterogeneity in herd size N and movement rate κ. Suppose the population consists of n herd types where a proportion pj of herds have Nj individuals and per capita movement rate κj. The mean herd size is , and the mean movement rate is . Here the state vector is of size , representing the numbers of infectives for each herd size where is the number of herds of type j with i infectives.
The transmission matrix T is of size M × M, but has only n entry points, corresponding to to and is therefore composed entirely of zeros except for n rows. The transition matrix is an M × M block matrix, where each diagonal block is a tridiagonal submatrix of size Nj × Nj (because the only state change is to increase or decrease the number of infectives by 1), and each off-diagonal block is an Ni × Nj zero submatrix (because herds do not change size). is tridiagonal, and so has dense diagonal blocks. Consequently, KL is size M × M, and K is n × n.
As above, we avoid calculation of T and and proceed by direct calculation of the elements of K. Because herd ‘susceptibility’ and ‘transmissibility’ are independent of who is infecting whom, each entry Kij only requires computation of the expected persistence time and the expected prevalence when infected for each herd type j denoted by and respectively. Then, each Kij is the number of secondary infections in a herd type i corresponding to entry from a herd of type j. Because infectives leave herds of type j, and enter disease-free herds of type i with probability this gives and so
Because K is the outer product of two vectors, and so all rows of K are linear multiples of each other, there is just one non-zero eigenvalue, given by the sum of the diagonal elements of K, i.e. 3.1This form has natural parallels with the expression for R0 on a random network: where kin and kout refer to the number of infectious in and out links per node . In our expression, the number of outward infectious links also captures the within-node disease dynamics via the terms and for the expected on farm prevalence while infected and the expected duration of infection.
Note that to maintain herd sizes, we assume that movement in κin equals movement out κout. However, in more complex scenarios, such as asymmetric cattle movement, this restriction may be relaxed, relying on within herd dynamics to maintain herd size. This would lead to more complex expressions for and , however this is beyond the scope of this paper.
3.3. Heterogeneity in herd size N and movement rate κ
Now using the NGM method described in §3.2, we examine how R* in the SIS model depends on heterogeneity in herd size N and movement rate κ. Heterogeneity is created by keeping a fixed mean, but varying the variance-to-mean ratio of a gamma distribution (discretized in the case of herd size).
R* is higher in populations with greater heterogeneity in herd size (figure 4b), but lower in populations with greater heterogeneity in movement rate (figure 4c), which can be explained heuristically as follows. Larger herds are associated with a lower chance of stochastic extinction  and therefore, a larger Tinf. Thus, larger herds will have a greater N and Tinf and therefore contribution disproportionately to R*.
If each herd has its own per capita movement rate κi, then each herd will contribute differently to R*. As there is a movement rate κ* that maximizes R*, the highest R* should occur in the homogeneous case where κi = κ* for all herds. Any heterogeneity in κi should reduce R*, as some herds will contribute less to R*. Consider the extreme case, where the population is composed of two groups, a small number of herds with high κi, which contribute R* ≈ 1, and a large number of herds with low κi, which contribute R* ≈ 0. Therefore, R* is maximized by homogeneous movement, and this result is indeed shown in figure 4c.
4. R* in four important livestock disease systems
We consider four important and epidemiologically different cattle diseases: BVDV, BHV, Mycobacterium avium ssp paratuberculosis (ParaTB, the pathogen responsible for Johne's disease), and Escherichia coli O157 (E. coli O157). Models and parameters for the first three are based on non-spatial deterministic models described by Carslake , whereas those for E. coli O157 are based on [37,43], (see §1 of the electronic supplementary material).
We calculated R* for each model by populating the NGM K directly, obtaining each entry KYX by simulation, introducing a single individual of infectious type X to a susceptible herd, and counting the number of infectious type Y leaving the herd via movement until the infection died out in the primary herd. To find the associated quasi-equilibrium proportion of infected herds, we also simulated a metapopulation of n = 100 herds each with N = 50 individuals. Our assumption of a homogeneous metapopulation means that we assume undirected movement between herds, and that movements are equally likely between any herds.
We considered movement rates κ between 0.0001 and 100 per year (see §2 of the electronic supplementary material for details on the methods used). Cattle typically move around one to four times during their lifetime, which has a mean of around 3 years . Consequently, the range of movements of most interest is around κ = 1 (one movement per animal per year). However, these models are intended to expose the range of behaviours of R*, rather than make precise predictions, and thus we consider a wider range of movement rates than is typically recorded.
For each of the exemplar diseases, R* reaches an intermediate peak above 1 for some intermediate movement rate (figure 5). The slowly progressing ParaTB has a peak at low movement rates, whereas the rapidly progressing BVDV has a peak at high movement. BHV and E. coli O157 have intermediate transmission rates, and thus peak at intermediate movements; however, the two categories of infective for BHV lead to a double peak.
Comparing R* with the proportion of infected herds (figure 6) shows that while all diseases achieve the maximum proportion of infected herds for high movement rates in the absence of disease intervention, even a relatively weak control effort (p = 0.2, indicating that 20% of infected individuals are identified and treated, blue lines) is sufficient to control the disease at high movement rates. With low movement rates, ParaTB is difficult to control (even a high control effort fails to control infection), but with greater movement effective control becomes easier.
The work reported here is motivated by the desire to control disease in regional and national livestock populations and addresses the lack of suitable metrics for determining the level of effort required when movement-based disease control is used to reduce disease transmission that is primarily driven by movement (trading) of livestock.
We describe a novel formulation of the threshold for disease spread in a structured population, R*, that explicitly captures group to group transmission via animal movements. While a number of previous studies have addressed the impact of group structure on disease invasion, some analytically [29,30,45] and some via statistical and simulation methods [19,24], this is the first demonstration of a threshold parameter for disease invasion in a metapopulation that captures within-group stochastic dynamics coupled with the explicit movement of infected individuals between groups.
Following Diekmann and Heesterbeek, we use an NGM approach to calculate R* and show how this may be used for disease systems with heterogeneities and multiple infectious states. We show for a simple disease system that R* is given by the intuitive expression where κ is the movement rate, N is the herd size, Tinf is the expected persistence time and Ppos is the expected prevalence in an infected herd. Note that this factorization of R* is non-trivial and accounts for the fact that prevalence and persistence time may be correlated. Pellis et al.  make a similar observation about their factorization of R* for household models.
A key feature is the presence of a peak in R* at intermediate movement rates. This novel observation arises, because we have explicitly modelled the herds' gain and loss in infectives that occurs when disease is spread by livestock movements. In household models where the contact process resulting in disease transmission is captured phenomenologically, there would be no such peak [30,32].
The R* peak depends on the interaction between movement rate κ, the within-herd disease persistence time Tinf, and the expected prevalence Ppos in an infected herd. Movement contributes directly to R*, but crucially also removes infectives from the herd, and therefore can reduce Tinf and Ppos. It is this trade-off that leads to the characteristic intermediate peak. Theoretically, for very high movement rates, an infective animal would arrive on farm and then immediately leave with virtually no opportunity to make infectious contacts, recover or die; for this reason R* tends to 1 at high movement rates.
The peak in R* has important consequences for control directed at livestock moving between herds. The degree of control effort required also peaks at intermediate movement rates, and consequently a given level of control may be sufficient to prevent persistence at low or high movement rates, but be insufficient over a range of intermediate movement rates. This phenomenon arises, because increased movement exposes more animals to testing, with the consequence that controls need to be less effective at identifying infected animals at high movement rates to achieve a given reduction in prevalence.
R* increases dramatically with increased herd sizes that substantially increase the persistence of infection. In addition, rather modest values of R0 can, depending on the disease system, be associated with values of R* that are orders of magnitude larger. This finding indicates that for some disease systems control directed at reducing R0 may be more effective than controls directed at animals moving between holdings.
We also demonstrated that R* is maximized when there is the least heterogeneity between farms in movement rates and when there is the least individual variation in infectiousness; conversely, increasing heterogeneity in herd size increases R*.
Our exemplar disease models and their parametrizations were selected, not to give precise predictions, but to provide a range of R* behaviours across four important livestock diseases. The different disease dynamics result in quite different R* profiles, leading to potential trade-offs between the control of different diseases. All the exemplar diseases have low R* near the intermediate per capita movement rate of one movement per year, but our predictions indicate that ParaTB and BVDV would have much higher R* at lower movements for ParaTB and at higher movements for BVDV. ParaTB (Johne's disease), a slowly progressing disease which persists in a herd for a long time, has an R* that peaks at low movement rates indicating that it might prove difficult to control if movement rates were reduced; however, increasing movement rates slightly could expose it to sufficient intervention that it would be unable to spread between herds.
In contrast, E. coli O157, a rapidly progressing disease with an R* peak at higher movement rates may be better able to persist in the face of movement-based controls at higher movement rates. BHV, which can also persist in herds for long periods is able to invade at lower movement rates than would be needed for invasion by E. coli O157 or BVDV. These findings concur with the observations that chronic diseases are more likely to invade than acute diseases with the same R0 .
The consequence of the differing R* profiles is that if, for example, movement restrictions were put in place to reduce E. coli O157, ParaTB could become more difficult to control via movement-based controls. On the other hand, if movement rates increased, ParaTB could be easier to control via movement-based controls, at the cost of increased prevalence of E. coli O157. Overall, our results indicate that at current livestock movement rates, disease control implemented at the point of between-farm movement alone can be sufficient to control some pathogens, but for infections such as ParaTB control at herd level is likely to be needed in addition.
Inevitably, the models analysed in this paper include a number of simplifying assumptions; nevertheless, our methodology (a key result of this paper) is applicable to more realistic scenarios. The extensive explorations presented in this paper indicate that the following results will hold in more complex scenarios: R* will peak and decline, leading to ‘islands’ of persistence when control is implemented. In addition, we anticipate that different diseases will have different R* profiles, potentially leading to conflicting requirements when controlling multiple diseases.
In summary, our formulation of R* provides novel theoretical insights into the likely effectiveness of alternative control strategies and an important addition to the selection of tools available to epidemiologists to be used in conjunction with R0 for disease control in livestock systems.
We declare we have no competing interests.
We thank the Scottish Government for funding through the Strategic Partnership in Animal Science Excellence (SPASE). J.C.P. is supported by FSS project FS101055. L.M. is supported by National Science Foundation DEB1216040, BBSRC grants BB/K01126X/1, BB/L004070/1, BB/L018926/1, BB/L018926/1, BB/N013336/1. L.M. and G.M. are supported by the Scottish Government Rural and Environment Science and Analytical Services Division, as part of the Centre of Expertise on Animal Disease Outbreaks (EPIC). G.M., M.R.H. and T.N.M. are supported by the Strategic Research programme of the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS).
The authors also thank three anonymous referees and Rowland Kao for their helpful comments.
Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.3601649.
- Received July 5, 2016.
- Accepted December 2, 2016.
- © 2017 The Authors.
Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.