Bovine tuberculosis model validation against a field study of badger vaccination with selective culling

1 1 2 Bovine tuberculosis model validation against a field study of 3 badger vaccination with selective culling 4 5 Graham C Smith 1* and Richard Budgey1 6 1 National Wildlife Management Centre, WOAH Collaborating Centre in Risk Analysis and 7 Modelling, Animal and Plant Health Agency, Sand Hutton, York 8 9 * Corresponding author 10 Email: [email protected] 11 12 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 2 13 Abstract 14 Bovine tuberculosis (TB) is a costly disease in Britain and Ireland shared by cattle and badgers 15 (Meles meles), and to reduce the infection in cattle to low levels some form of badger 16 management is considered necessary. We compare the results of a badger field trial where test- 17 positive badgers are culled, and test-negative badgers vaccinated (a TVR approach) with the 18 results of the simulation model originally used to predict the effect of the trial in Northern 19 Ireland. Initial model results depended strongly on whether social perturbation occurred in the 20 badger population following culling, and the field study demonstrated no evidence for such 21 behavior. Here we re-run the model with the initial conditions of the TVR study and with no social 22 perturbation and predict a similar outcome in terms of number of badgers caught, number 23 testing positive, and the substantial decline in prevalence. These results validate our model and 24 demonstrate the utility of such predictive modelling for this disease system. This is particularly 25 important as the UK government moves away from widespread badger culling in England toward 26 more vaccination, as this combined approach gives a more robust method of disease 27 management than just vaccination on its own. 28 29 Introduction 30 In the British Isles, bovine tuberculosis (TB: caused by Mycobacterium bovis) remains a costly 31 disease shared by cattle and badgers (Meles meles), costing in excess of £150 million per year in 32 England alone [1]. In the absence of management, it appears that both species could sustain TB 33 [2-4] although the frequency of spread between the two species is highly variable in different .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 3 34 populations [5-8]. Thus some form of badger disease management would be required to reduce 35 and retain TB in cattle at very low levels. Various badger control strategies have been adopted: 36 from reactive and localised culling to large-scale culling in England with some vaccination, 37 vaccination in Wales, and culling and vaccination in Ireland. The relative efficacy of these 38 methods has been evaluated with simulation models [9-12], but culling approaches have 39 generally been non-selective. Such culling risks behavioural perturbation of the badger 40 population, which can induce increased ranging behaviour [13] and may increase disease 41 prevalence in badgers and possibly in cattle [14, 15]. 42 Since 2010 an injectable vaccine, Bacillus Calmette–Guérin (BCG), has been available for use 43 in badgers that leads to a substantial reduction in disease in free-living badgers [16] and a degree 44 of herd protection for cubs [17]. Field trials have confirmed that badger vaccination is not inferior 45 to continuing culling [18]. Vaccination does not remove any (test positive) infected animals, so a 46 combined policy may be more effective. By using trap-side tests to diagnose TB (e.g. the dual 47 path platform test: DPP), leads to the possibility of selective culling of test-positive animals and 48 vaccination of test-negative animals. This is referred to as test and vaccinate or remove (TVR) 49 approach. Selective culling may also be more acceptable than widespread culling. 50 In Northern Ireland, where badger control had not previously been performed, an evaluation 51 of this TVR approach was conducted. Initial modelling before the trial started suggested that the 52 number of infected badgers remaining was very dependent on whether perturbation occurred: 53 in the absence of perturbation a decline of about 70% in the number of infected badgers was 54 seen, whereas with perturbation it was more modest [19]. Selective culling was also predicted 55 to result in an 83% reduction in the number of animals culled [20]. With the completion of the .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 4 56 subsequent five-year TVR study in Northern Ireland [21] we can re-examine these predicted 57 effects on the badger population, and use the exact initial conditions in the field to validate the 58 model output. 59 60 Materials and Methods 61 The TBi computer simulation model [12, 20, 22, 23] was used to model the TVR study site in 62 Northern Ireland. Input data included the initial population estimate, initial badger prevalence 63 and the number of badgers captured each year [21]. Based on this, the model simulated the 64 epidemiology, ecology and management of the badger population over the five-year course of 65 the study to determine the population size and number infected. Estimates of annual disease 66 prevalence during the trial, based on a Bayesian analysis combining multiple test methods [24], 67 were used to validate the model output. 68 TBi is a stochastic, individual-based, spatially explicit model which simulates the life histories 69 of a population of badgers at two-month timesteps. Life histories were generated using the 70 probabilities of reproduction, mortality, dispersal, disease progression and disease transmission 71 collected from the population at APHA’s Woodchester Park research station in Gloucestershire. 72 Population density was taken from badger sett surveys conducted in County Down before the 73 trial, and the demographic makeup of social groups was matched to the local population. The 74 retention of some parameter values from the English model would have had minimal effect on 75 the simulated output as the epidemiology is driven by badger density and disease prevalence, 76 which were closely matched to the Northern Ireland study site. All model parameters and their .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 5 77 source are given in Table S1 and a full description of the model using the ODD protocol [25, 26] 78 is in a supplementary file (S2). 79 The model arena comprised of a 100 x100 grid, with each cell representing 200m x 200m; 80 the total grid representing a 400 km2 landscape area. The population was 550 badgers in 85 social 81 groups. The arena comprised a central core of approximately 100 km 2 where badger 82 management was undertaken, and the boundary was defined by the extent of participating 83 farmland. Outside the core was a surrounding buffer two social groups wide where the possible 84 influence of control could be observed and outside this any effect of culling was expected to be 85 negligible. The grid was wrapped to form a torus to eliminate edge effects. Social groups were 86 randomly distributed across the arena and all badgers were members of a group and occupied a 87 territory which defined which social groups were neighbours. 88 Characterisation of badgers 89 Individual badgers were characterized by the variables: social group, sex, age, and health- 90 status. The age categories were cub, yearling (one-year old), and adult. The TB-status categories 91 were defined as: healthy, infected, single-site and multi-site excretor. Probability of disease 92 progression was based on field data from Woodchester Park [27]. Badger fecundity was density- 93 dependent based on an upper limit of litters in each social group. Births were simulated at the 94 start of the year, and litter size was modelled probabilistically from a distribution of known litter 95 sizes [28], with a mean of 2.94 cubs per litter, and a sex ratio of 1:1. State-dependent mortality 96 rates were based on field data from Woodchester Park [27]. Badgers up to two months of age 97 (i.e. while still underground) had a higher mortality rate than older badgers [29]. Animals in the .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 6 98 excretor disease classes also had higher mortality rates [27]. Badgers were allowed to disperse, 99 usually to smaller social groups if available [30], based on sex-dependent probabilities (males 100 more often than females) but independent of age and season. Badgers were also moved to 101 neighbouring social groups in response to any demographic imbalance. The probability of 102 transmission between individual badgers was adjusted so the population disease prevalence 103 matched the reported prevalence at the start of the study, estimated at 0.14 [24]. Disease 104 transmission occurred between animals of the same and neighbouring social groups. As badgers 105 interact more frequently with their own social group than with neighbouring groups, within- 106 group transmission was given a greater probability (20-fold) than between animals in 107 neighbouring groups [12]. Transmission probability increased as animals moved from excretor to 108 super excretor class. 109 Simulation of management operations 110 Prior to simulation of management operations, the model was run for 100 years to allow the 111 population and disease dynamics to stabilise after seeding. Management operations were 112 simulated by allocating badgers a probability of capture based on the proportion of accessible 113 land (0.94) supplied by DAERA (Menzies, F., pers. comm.) and trapping efficacy rates (0.54) [21]. 114 Social groups were allocated to one of two trapping campaigns each year; territories not wholly 115 within accessible land could still be subject to some level of control as badgers could be trapped 116 away from the main sett. Badgers were individually marked during the study so recaptured 117 animals were identifiable and this information was also available in the model. .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 7 118 In the field trial, animals were tested trap-side using the Dual-Path Platform VetTB test (DPP) 119 on whole blood samples. In year one (2014), regardless of test result all badgers were vaccinated 120 and released. In years two to five (2015-2018), test positive badgers were culled and test negative 121 badgers vaccinated and released [21]. Field trial methods were replicated in the model with the 122 simulation of one year of vaccination only, followed by four years of TVR. The model simulated 123 DPP testing using a test sensitivity of 0.63 and specificity of 0.94 [24], based on the use of lines 1 124 or 2 in the DPP test under field conditions. In the study, animals were vaccinated with BCG Danish 125 in years 1-3 and BCG Sophia in years 4-5 due to supply issues. It was assumed in the model that 126 both vaccines gave a 0.6 probability of providing full protection from infection for susceptible 127 badgers. Protection was for the lifetime of the badger, with further opportunity for full protection 128 at subsequent capture for animals for which vaccination had previously been unsuccessful. A 129 simulation of the same population with no control was also undertaken to provide a baseline. A 130 total of 100 simulations was run for each model scenario. 131 Field results suggest limited social perturbation resulted from badger removal operations 132 [31, 32]. Therefore, effects of perturbation were not simulated beyond the filling of demographic 133 vacancies in neighbouring social groups described above. Although TVR does result in additional 134 vacancies, there are many fewer than with non-selective culling and this demographic 135 rebalancing contributes little additional transmission compared to the increased ranging 136 behaviour seen in removal operations such as the Randomised Badger Culling Trial (RBCT) in 137 England [33]. 138 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 8 139 Results 140 Model output is reported for the number of unique badger captures and the number of 141 badgers testing positive using the simulated DPP test; these are compared to results from the 142 study. Model output is also recorded for disease prevalence in the core, buffer, outer area of the 143 arena and mean of the whole arena under TVR and no control; prevalence in the core is compared 144 to the empirical estimate of prevalence from a Bayesian model combining three test methods 145 used in the field trial [24]. 146 The number of unique badger captures did not vary substantially over the course of the study 147 as relatively few were removed, and the population recovered before the following year. The 148 model produced a similar result, although the number reported by the model was highest in the 149 first year whereas in the field study the maximum occurred in the second year (Fig1). 150 151 Fig 1. Comparison between model results and data from the field trial for number of unique 152 badger captures in each year of the trial. Points indicate results from the TVR trial and violin 153 plots show the distribution of model predictions. 154 155 The simulated proportion of captured badgers that tested positive using the cage-side DPP 156 test was in line with the general trend in population prevalence. The number testing positive in 157 the model was in reasonable agreement with the study in each year except 2016, where the field 158 results were unusually low, but even in that year there was overlap between model results and 159 the 95% confidence limits of the empirical estimate (Fig 2). Since in year one (2014), all badgers .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 9 160 were vaccinated and released regardless of test result, and in years 2-5 (2015-2018) test positive 161 badgers were removed, for these later years the proportion testing positive is equal to the 162 proportion of captured badgers removed. 163 164 Fig 2. Comparison between model results and data from the field trial for proportion of 165 captures testing positive using the cage-side DPP test in each year of the trial . Points indicate 166 results from the TVR trial with error bars showing 95% binomial confidence interval, and violin 167 plots show the distribution of model predictions. 168 169 A large benefit of control was seen in the core where population level disease prevalence 170 was predicted to reduce from the initial value of 0.14 to about 0.02, which closely matched the 171 empirical estimate for change in annual prevalence [24] (Fig 3). The simulation was continued to 172 year 2035 assuming no further control was applied (Fig 4). The model predicted prevalence in 173 the core would slowly increase to about 0.05 some 15 years after control ended, although long- 174 term model projections are always less reliable than short-term ones. The outcome when no 175 control was applied was an unchanging population prevalence. A small benefit was seen in the 176 buffer because some groups there may have partly overlapped with participating farms and 177 therefore experienced some removal. There would also have been a small effect over the course 178 of the study as some diseased animals in the higher prevalence buffer will have moved to the 179 core, slightly reducing the benefit of control seen there and also some animals emigrated from .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 10 180 the lower prevalence population in the core to the buffer, reducing the level of disease in the 181 buffer. 182 183 Fig 3. Model results for median annual prevalence in the core area during the control period, 184 with the no control scenario shown for comparison. Shading indicates the inter-quartile range 185 for the model predictions. The dashed line represents the estimated population-level prevalence 186 from each year of the trial [24]. 187 Fig 4. Model results for median annual prevalence in each zone and overall simulated area 188 (Arena), predicted to year 2035. Vertical dotted lines indicate start and end of TVR trial. Shading 189 indicates inter-quartile range for model predictions. 190 191 Discussion 192 In recent decades there has been an increasing reliance on using computer models to predict 193 the consequences of disease outbreaks or disease control. Such models can rarely be validated 194 prior to any control in the field. Here we take the original model used to evaluate a TVR badger 195 control study for bovine TB in Northern Ireland and uniquely validate it against the data from the 196 field trial. Such validation increases support for its use in other locations, or other scenarios. 197 During the five-year field trial a total of 824 badgers were caught, with between 271 and 341 198 unique captures each year [21]. This agrees well with the simulation, although the numbers 199 caught in 2015 were higher than in 2014, whereas in the model the reverse was expected. Each .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 11 200 year between 4% and 16% of badgers were removed: i.e., were DPP test positive [21]. This also 201 agrees with our initial expectation of an 83% reduction in the number of badgers culled compared 202 to a proactive cull: i.e., all trapped badgers would have been removed. 203 However, the most important prediction of the model was a substantial reduction in disease 204 prevalence if social perturbation did not occur. During the trial a total of 105 individual badgers 205 were followed using GPS collars, and there was no evidence of a change in home range size, 206 neither annually, nor monthly, between the years of the study [31]. This strongly suggests that 207 social perturbation did not occur in this population during the study. The field trial demonstrated 208 a substantial decline in prevalence during the trial [24], with the last years having a slightly lower 209 prevalence than the simulated results, when we also assumed no social perturbation (Fig 3). 210 Therefore, the model may be slightly conservative about the level of disease reduction. It is also 211 worth noting that we predicted a slow recover of disease in the badger population, but it would 212 require a repeated field study on this site to confirm or deny this longer-term prediction, and in 213 general longer-term model predictions are less reliable as other factors may well occur in the 214 interim. 215 Overall, this points to the success of the model in predicting the effects of the TVR approach 216 in Northern Ireland. Since the simulated output depends most heavily on the badger social 217 groups size and density, it therefore seems likely that the TVR approach would have similar 218 outcomes across Ireland or other areas where badger dynamics are similar, but we cannot 219 immediately extrapolate these results to England and Wales where social group size and density 220 are both higher. However, in many areas in England badgers have been subjected to recent 221 culling, and the density and social group size may now be more similar to the field study. We also .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 12 222 did not account for any ongoing transmission from cattle to badgers throughout the trial and this 223 would be expected to ‘seed’ the badger population with more M. bovis . Longer term model 224 predictions are always less reliable than short term predictions, due to stochastic drift, changes 225 in populations dynamics, habitat and farming, and the additional seeding that may occur from 226 cattle would further erode the accuracy over time. Thus, we do not place any reliance on the 227 longer-term dynamics of disease in the badger population at this stage. To gain more accurate 228 longer-term dynamics would require linking this model to a cattle TB model, and including cattle 229 management. 230 231 Conclusions 232 Models are often used to evaluate disease management scenarios in both animals and man. 233 Such models are often fitted to field data to help parameterize them, but validation against 234 unrelated field data is uncommon. We used an established simulation model of badgers and 235 bovine tuberculosis and adjusted it to the local situation in Northern Ireland to predict the 236 outcome of a proposed field study of a selective cull/vaccinate strategy. In this paper we report 237 on the output of that model, after parameter changes to exactly fit the start of the field trial. The 238 field study confirmed that no substantial social perturbation was apparent, and the revised 239 model used to make retro-predictions that closely matched the real-world data. This is the first 240 time that the simulation model was validated against badger vaccination and means that the use 241 of the model for badger vaccination in other circumstances, such as in England where the focus 242 is changing from culling to vaccination, should be reliable. .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 13 243 244 Acknowledgments 245 The authors would like to acknowledge the help of Fraser Menzies for helpful discussion on both 246 the original modelling for the TVR study, and the start conditions for this follow up analysis. 247 248 References 249 1. Defra. 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Jenkins HE, Woodroffe R, Donnelly CA. The duration of the effects of repeated widespread 349 badger culling on cattle tuberculosis following the cessation of culling. PLoS One. 2010;5(2):e9090. doi: 350 https://doi.org/10.1371/journal.pone.0009090. 351 352 Supporting Information 353 S1 Table. Input model parameters. .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint 18 354 S2 File. Model ODD protocol. 355 .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint .CC-BY 4.0 International licenseavailable under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint