Assessing the efficacy of vaccination strategies in curbing epidemics of Foot- and Mouth Disease in The Netherlands

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1 Assessing the efficacy of vaccination strategies in curbing epidemics of Foot- and Mouth Disease in The Netherlands Boender, G.J., Hagenaars, T.J., van Roermund, H.J.W. and de Jong, M.C.M. Animal Sciences Group (ASG), P.O. Box 65, 8200AB Lelystad, the Netherlands, Abstract The 2001 epidemic of Foot- and Mouth Disease (FMD) in the Netherlands has been brought to a halt by a combination of pre-emptive culling, emergency vaccination and depopulation measures in a large area comprising about 1800 farms. After the large Dutch HPAI epidemic in poultry in 2003, public acceptance of intervention strategies based on massive culling has further eroded. Policy makers in the Netherlands are therefore interested in assessing the use of emergency vaccination as a basis for intervention in the future. Here we use spatial transmission models to analyse the transmission potential of FMD between farms in the Netherlands, and to assess the expected efficacy of a set of alternative emergency vaccination strategies in curbing FMD spread. Our results, presented in the form of risk-maps for FMD spread, suggest that ring-culling or ring-vaccination strategies are insufficiently effective to achieve epidemic control in certain important areas in the Netherlands (with high densities of farms). In these areas only area-wide culling and/or vaccination strategies would stand a chance of being effective. On the positive side, our results suggest that in much of the Netherlands outside the high-density areas, standard intervention measures as required by the EU (a movement standstill, bio-security measures and culling of infected farms and dangerous-contact farms) would be sufficient to curb local propagation of the epidemic. Introduction The 2001 epidemic of foot-and-mouth disease (FMD) in the Netherlands has exemplified the need for quantitative assessments of the efficacy of intervention strategies designed to curb a Dutch FMD epidemic. Assessing the effectivity of emergency vaccination strategies in particular is important to inform policy making now and in the future. In this paper we present preliminary results of a mathematical modelling analyis of the transmission characteristics of FMD between farms in The Netherlands. Mathematical modelling enables us to integrate the available knowledge about the transmission of FMD virus and, subsequently, to evaluate possible intervention strategies in a scenario analysis. For a comparative review of the modelling work on the British FMD epidemic, see Kao (2002). Here we develop and parameterize a spatial transmission model for FMD within the Netherlands. The purpose of this model is twofold: it is a means to assess transmission risk (and its regional variation) once the infection is introduced and it enables one to analyse the expected effects of a range of alternative intervention strategies. In developing and parameterizing the transmission model, we identify biological unknowns and epidemiological uncertainties relevant to the transmission of FMD. A model sensitivity analysis allows us to evaluate the resulting uncertainty in both the assessment of transmission risk and the expected effect of possible intervention strategies.

2 Material and Methods FMD transmission: quantitative information In the relatively small epidemic in The Netherlands in 2001, one main area was affected (with 23 infected farms detected), and two further spots occurred away from this area, one in Ee/Anjum (2 infected farms) and one in Kootwijkerbroek (1 farm). The main area roughly takes the shape of a triangle, spanned by the towns Apeldoorn, Deventer and Zwolle, and will be referred to as the Triangle Apeldoorn-Deventer-Zwolle in this paper. A first, non-spatial, analysis of the transmission dynamics and the effect of control measures during this epidemic is described by Bouma et al. (2003). The control measures included movement restrictions, culling of infected farms, culling of contact farms and (later) preemptive vaccination (followed by culling) of farms in a large area (317 km 2 ) approximately spanned by the towns Apeldoorn, Deventer and Zwolle. The analysis of Bouma et al. concludes that altogether, these control measures reduced transmission sufficiently to bring the epidemic under control. One important aspect that was ignored in the analysis of Bouma et al. is the spatial locality of the transmission, especially in the period after the first detection: susceptible farms have an increased risk of getting infected when being in the vicinity of infected farms, an effect that has also been demonstrated for the British epidemic (Keeling et al. 2001, Ferguson et al. 2001a-b, Haydon et al. 2004). Model development We will construct spatial models solely for the period after the moment of first detection. The evidence that local spread of the infection ( neighbourhood infections ) (Ferguson et al. 2001a-b, Keeling et al. 2001, for Avian Influenza see: Mannelli et al. 2004, for Classical Swine Fever see: Stegeman et al., 2002) is the dominant type of between-farm transmission once a movement standstill and bio-security measures are in place, motivates the consideration of spatially-defined additional interventions (such as ring culling and/or ring vaccination). In order to capture the spatial nature of the transmission between nearby farms and to able to study the effect of spatially-defined interventions, the model needs to incorporate geographical space. This is achieved here by an individual-based approach: each individual farm (with cattle, sheep and/or pigs) in the Netherlands is included, differing from all the others in its two-dimensional spatial coordinates. Furthermore, in order to capture the scope for variation in epidemic outcome due to pure chance, the model is formulated stochastically. Mathematical definition of the transmission model. The model is defined on a farm level: farms are the individual units, which differ from each other only by their locations. The model calculates the evolution in time of the infection status of each farm. The transmission process after instigation of a movement standstill and biosecurity measures is governed by a transmission kernel p(r), which describes the transmission rate between two farms as a function of the distance r between these farms. If farm i is currently susceptible, the rate (or probability per day) λ i at which it is becoming infected is given by: λ = p( r ), with j running over all infectious farms. Eq. (1) i j ij Here r ij is the distance between farms i and j. The transmission kernel p(r) is the central part of the spatial transmission model, and its estimated shape for FMD in the Netherlands (shown in Figure 1) is a central result of the work described in this paper. In this study p(r) will be estimated from the Dutch 2001 FMD epidemic data. Although we have also studied other functional forms, we will concentrate on results obtained for a so-

3 called power-law kernel p(r). Alternative functions studied give very similar results. The power-law kernel has the following mathematical structure: p0 p ( r) =, Eq. (2) α r 1+ r0 In this expression, p 0, r 0 and α are parameters with positive values. We also define the kernel π ( r) as: π r 1 exp( p r T ). Eq. (3) ( ) ( ) Here T denotes the expected length of the infectious period of a farm. Whereas p(r) is a transmission probability per day, the kernel π ( r) represents the probability of transmission occurring at any time over the entire infectious period of the source farm. In order to obtain a well-behaved kernel (with a large-r tail approaching zero fast enough) we require 0 π ( r)2 rdr <, where π(r) is defined in Eq. (3). This condition is satisfied provided α >2. The power-law kernel type was employed by Keeling et al. (2004) to describe the transmission of FMD in Great Britain in An alternative but similar kernel form was used by Ferguson et al. (2001a-b). We concentrate on constructing risk maps by calculating a single number for each farm, representing the net transmission potential given a certain intervention strategy. As this approach, to be discussed in more detail below, yields spatial summary information in the form of risk maps, its results are very useful for getting the main picture of how the control of FMD epidemics may be attempted in The Netherlands. Basic reproduction number and threshold behaviour The main characteristics of epidemiological risk and disease control can often be summarized by listing the values of a small number of summary parameters. One of such parameters is the basic reproduction number R 0 between farms, which measures the risk of between-farm transmission of the virus. R 0 is defined as the expected number of secondary infections caused by one primary infection throughout its infectious period in a naïve population of farms. Here naïve refers to a population in which there is no (recent) history of (FMD) infections. Once a kernel π(r) is estimated and if a dataset of all farm locations is available, it is possible to calculate a local between-farm R 0 for each particular farm, simply by adding up all transmission probabilities to all other farms. Basic mathematical theory of epidemic spread shows that the concept of R 0 allows us to characterize the nonlinear behavior in the following compact manner: if R 0 >R c, introductions of the infection may lead to large outbreaks; if R 0 <R c, the transmission chain will never be able to maintain itself and outbreaks are always small. In non-spatial transmission models, the threshold value is usually R c =1. In spatial models however, if the transmission is sufficiently local in character, R c >1. R 0 =R c marks the transition between a situation in which outbreaks always terminate locally due to local depletion of the pool of susceptible units, and a situation where large outbreaks can percolate through space. In Boender et al (2006a) a phenomenological theory is developed that allows an approximate analytical calculation of R c for any given kernel p(r).

4 Results Transmission risks and potential for control in the Netherlands The power-law kernel of Eq. (2) can be estimated from the outbreak data for the 2001 FMD epidemic in the Netherlands as described in (Boender et al, 2006b). Using maximum likelihood estimation we arrive at the point estimates p 0 = , r 0 =1.22 km, α= 2.8. These correspond to the point-estimate kernel shown in Figure p (r ) r (distance in kilometers) Figure 1. Infection probability p(r) per day as a function of distance between farms; shown is the result for maximum-likelihood values of the kernel parameters estimated from the Dutch 2001 FMD epidemic case data (ignoring 3 cases outside the triangle Apeldoorn-Deventer- Zwolle). Effective reproduction number (in the presence of additional control measures) In order to quantify the effectiveness of intervention strategies that use ring vaccination or ring culling (in addition to the minimum interventions by EU legislation), we use the estimated transmission kernel p(r) to derive an effective kernel p eff (r), yielding an effective reproduction number. In Figure 2 we show the form of the effective kernel π(r) for the intervention policy of EU measures plus preventive culling within a 1-km radius around infected farms. This π(r) describes the effective probability of transmission, given the intervention policy, between an infectious farm and a susceptible farm separated by a distance r over the whole infectious period of the source farm. Here it is assumed that due to the preventive culling, farms that are infected within a 1-km radius are limited in their contribution to further transmission such that their infectious period T is halved from 7 days to 3.5 days on average. The remaining 3.5 days are due to the fact that these farms are on average already infected for some time before the source farm is detected. We note that subsequently the ring culling has to be carried out, typically giving rise to further delay. However, in assuming a reduction to 3.5 days, we are, optimistically, assuming that ring culling is instantaneous.

5 p (r ) EU int erv ent ion measures 'EU' plus 1-km ring culling r (distance in km) Figure 2. The kernel p(r) as calculated from the point estimate for p(r) using an infectious period T=7 days (dashed line). p(r) is the probability of transmission between an infectious and a susceptible farm, over the full infectious period of the source farm, as a function of the distance r between the two farms. The full curve represents the effective kernel for 1-km ring culling calculated by assuming T=3.5 days for transmission to farms within a 1-km radius of the infected farm. This corresponds to the optimistic assumption that ring culling is completed instantaneously once the source farm is detected. The threshold behavior of the transmission process leads to the distinction of two types of areas depending on farm density: one of low risk, where the reproduction number R 0 is below R c and thus insufficiently large to cause sustained transmission; and a second one of high risk, where R 0 is above R c and thus additional interventions are required to achieve epidemic control. By calculating the local R 0 for all relevant farms in the Netherlands, one obtains an FMD risk map. Such a risk map is to be read in the following way: the local reproduction number quantifies the potential for local spread in the situation where EU-required intervention measures are in place: a movement standstill, bio-security measures and culling of infected farms and dangerous-contact farms. Risk maps for the triangle Apeldoorn-Deventer-Zwolle We now turn to the calculation of risk maps for local spread. We first concentrate on our results for the triangle Apeldoorn-Deventer-Zwolle (Figure 3). As described above, risk maps are obtained by calculating the local R 0 for all relevant farms in the Netherlands. The red dots in Figure 3 correspond to farms with a local reproduction number exceeding the threshold value R c. The red areas formed by these red dots are therefore high-risk areas for FMD spread. In particular, the red areas in Figure 3 are at risk of propagating local spread in the situation where EU-required intervention measures are in place: a movement standstill, biosecurity measures and culling of infected farms and dangerous-contact farms. The remaining colour coding of the risk map distinguishes different ranges of R 0 values below 1: Orange: 0.75<R 0 <1; Yellow: 0.5< R 0 <0.75; Green: R 0 <0.5. The main message of the graph is: a large part of the triangle Apeldoorn-Deventer-Zwolle is a high-risk area for local spread of FMD (when EU-required intervention measures are in place).

6 Figure 3. Risk maps for an EU strategy for the triangle Apeldoorn-Deventer-Zwolle and surroundings. (EU = a movement standstill, bio-security measures and culling of infected farms and dangerous-contact farms ). Red: R 0 >1; Orange: 0.75<R 0 <1; Yellow: 0.5< R 0 <0.75; Green: R 0 <0.5; Grey: regions without farms. Maximum-likelihood estimates for the kernel parameters have been used, i.e. using the kernel shown as in Figure 1. In Figure 4 we go one step further, considering the strategy EU plus 2-km ring culling. Whereas we are being optimistic here again in assuming that culling is immediate, we counterbalance this with a pessimistic focus on the results using the kernel with the longest range (95% upper bound) consistent with the data (kernel not shown in Figure 1, but see Boender et al, 2006b). From this risk map we conclude that even supplementing EU measures with a 2-km ring culling strategy is likely to fail in controlling local spread in the triangle Apeldoorn-Deventer-Zwolle. The same conclusion can thus be drawn for EU plus 2-km ring vaccination. The same type of risk map for EU plus 3-km ring culling (not shown) still shows (small) red areas. Thus, our calculations suggest that ring strategies in general, certainly those using ring vaccination, are unable to reduce R 0 to below threshold in this example area.

7 Figure 4. Risk maps for an EU plus 2-km ring culling strategy for the triangle Apeldoorn- Deventer-Zwolle and surroundings. (EU = a movement standstill, bio-security measures and culling of infected farms and dangerous-contact farms ). Red: R 0 >1; Orange: 0.75<R 0 <1; Yellow: 0.5< R 0 <0.75; Green: R 0 <0.5; Grey: regions without farms. Here we use, pessimistically, the kernel with the longest range (95% upper bound) consistent with the data. This result also serves as an optimistic risk map for an EU plus 2-km ring vaccination strategy (see main text). In the same way risk maps were constructed for the whole country. These maps show that ring interventions are not sufficient in a few other areas in the country, like in the triangle Apeldoorn-Deventer-Zwolle as shown above. But on the positive side, our results suggest that in much of the Netherlands outside these high-density areas, standard intervention measures as required by the EU (a movement standstill, bio-security measures and culling of infected farms and dangerous-contact farms) would be sufficient to curb local propagation of the epidemic.

8 Discussion and conclusions Due to the availability of the 2001 epidemic data, a theoretical framework now exists in which what-if questions on between-farm FMD transmission in the Netherlands, given that an introduction of the virus has taken place, can be addressed. Analyses within this framework suggest that ring-culling or ring-vaccination strategies are insufficiently effective to achieve epidemic control in certain important areas in the Netherlands (with high densities of farms) such as the triangle Apeldoorn-Deventer-Zwolle. In these areas only area-wide interventions would stand a chance of being effective. Depending on an economic assessment, preventive area-wide vaccination strategies might be judged appropriate once FMD epidemic would have been confirmed within a certain distance from a high-density area. The limited effectiveness of ring culling and ring vaccination strategies predicted by our calculations is in essence due to the fact that local transmission is estimated to be not very local : the tail of the transmission kernel is dropping quite slowly. Transmission kernels estimated in the literature for local spread of FMD in Great Britain (2001 epidemic) have similar tails (Ferguson et al. 2001a-b; Keeling et al. 2001). On the positive side, our results suggest that in much of The Netherlands outside the highdensity areas, standard intervention measures as required by the EU (a movement standstill, bio-security measures and culling of infected farms and dangerous-contact farms) would be sufficient to curb local propagation of the epidemic. Acknowledgements We are grateful to Annemarie Bouma, Aldo Dekker, Phaedra Eblé, Mirjam Nielen, Karin Orsel and Monique Mourits for useful discussions, for provision of data and for help with interpreting data. We also thank the Dutch expert group on FMD for their comments and questions. References Boender, G.J., Meester, R.W.J., Gies, T.J.A., de Jong, M.C.M. 2006a. The local threshold for geographical spread of infectious diseases between farms. Submitted. Boender, G.J., Hagenaars, T.J., van Roermund, H.J.W. and de Jong, M.C.M., 2006b. Modelling the transmission of foot-and-mouth disease virus in the Netherlands. Report Animal Sciences Group ASG06- I00569, Lelystad, The Netherlands, 20 pp. Bouma, A., Elbers, A.R.W., Dekker, A., de Koeijer, A., Bartels, C., Vellema, P., van der Wal, P., van Rooij, E.M.A., Pluimers, F.H., de Jong, M.C.M., The foot-and-mouth disease epidemic in The Netherlands in Preventive Veterinary Medicine 57, Ferguson, N.M., Donnelly, C.A., Anderson, R.M., 2001a. The foot-and-mouth epidemic in Great Britain: Pattern of spread and impact of interventions. Science 292, Ferguson, N.M., Donnelly, C.A., Anderson, R.M., 2001b. Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain. Nature 413,

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