{"paper_id":"1cc2ce12-cebb-4f95-8f4e-db966ae86a8d","body_text":"1\n1\n2 Bovine tuberculosis model validation against a field study of \n3 badger vaccination with selective culling\n4\n5 Graham C Smith 1* and Richard Budgey1\n6 1 National Wildlife Management Centre, WOAH Collaborating Centre in Risk Analysis and \n7 Modelling, Animal and Plant Health Agency, Sand Hutton, York\n8\n9 * Corresponding author\n10 Email: graham.smith@apha.gov.uk\n11\n12\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n2\n13 Abstract\n14 Bovine tuberculosis (TB) is a costly disease in Britain and Ireland shared by cattle and badgers \n15 (Meles meles), and to reduce the infection in cattle to low levels some form of badger \n16 management is considered necessary. We compare the results of a badger field trial where test-\n17 positive badgers are culled, and test-negative badgers vaccinated (a TVR approach) with the \n18 results of the simulation model originally used to predict the effect of the trial in Northern \n19 Ireland. Initial model results depended strongly on whether social perturbation occurred in the \n20 badger population following culling, and the field study demonstrated no evidence for such \n21 behavior. Here we re-run the model with the initial conditions of the TVR study and with no social \n22 perturbation and predict a similar outcome in terms of number of badgers caught, number \n23 testing positive, and the substantial decline in prevalence. These results validate our model and \n24 demonstrate the utility of such predictive modelling for this disease system. This is particularly \n25 important as the UK government moves away from widespread badger culling in England toward \n26 more vaccination, as this combined approach gives a more robust method of disease \n27 management than just vaccination on its own.\n28\n29 Introduction\n30 In the British Isles, bovine tuberculosis (TB: caused by Mycobacterium bovis) remains a costly \n31 disease shared by cattle and badgers (Meles meles), costing in excess of £150 million per year in \n32 England alone [1]. In the absence of management, it appears that both species could sustain TB \n33 [2-4] although the frequency of spread between the two species is highly variable in different \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n3\n34 populations [5-8]. Thus some form of badger disease management would be required to reduce \n35 and retain TB in cattle at very low levels. Various badger control strategies have been adopted: \n36 from reactive and localised culling to large-scale culling in England with some vaccination, \n37 vaccination in Wales, and culling and vaccination in Ireland. The relative efficacy of these \n38 methods has been evaluated with simulation models [9-12], but culling approaches have \n39 generally been non-selective. Such culling risks behavioural perturbation of the badger \n40 population, which can induce increased ranging behaviour [13] and may increase disease \n41 prevalence in badgers and possibly in cattle [14, 15]. \n42 Since 2010 an injectable vaccine, Bacillus Calmette–Guérin (BCG), has been available for use \n43 in badgers that leads to a substantial reduction in disease in free-living badgers [16] and a degree \n44 of herd protection for cubs [17]. Field trials have confirmed that badger vaccination is not inferior \n45 to continuing culling [18]. Vaccination does not remove any (test positive) infected animals, so a \n46 combined policy may be more effective. By using trap-side tests to diagnose TB (e.g. the dual \n47 path platform test: DPP), leads to the possibility of selective culling of test-positive animals and \n48 vaccination of test-negative animals. This is referred to as test and vaccinate or remove (TVR) \n49 approach. Selective culling may also be more acceptable than widespread culling.\n50 In Northern Ireland, where badger control had not previously been performed, an evaluation \n51 of this TVR approach was conducted. Initial modelling before the trial started suggested that the \n52 number of infected badgers remaining was very dependent on whether perturbation occurred: \n53 in the absence of perturbation a decline of about 70% in the number of infected badgers was \n54 seen, whereas with perturbation it was more modest [19].  Selective culling was also predicted \n55 to result in an 83% reduction in the number of animals culled [20]. With the completion of the \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n4\n56 subsequent five-year TVR study in Northern Ireland [21] we can re-examine these predicted \n57 effects on the badger population, and use the exact initial conditions in the field to validate the \n58 model output. \n59\n60 Materials and Methods\n61 The TBi computer simulation model [12, 20, 22, 23] was used to model the TVR study site in \n62 Northern Ireland. Input data included the initial population estimate, initial badger prevalence \n63 and the number of badgers captured each year [21]. Based on this, the model simulated the \n64 epidemiology, ecology and management of the badger population over the five-year course of \n65 the study to determine the population size and number infected. Estimates of annual disease \n66 prevalence during the trial, based on a Bayesian analysis combining multiple test methods [24], \n67 were used to validate the model output. \n68 TBi is a stochastic, individual-based, spatially explicit model which simulates the life histories \n69 of a population of badgers at two-month timesteps. Life histories were generated using the \n70 probabilities of reproduction, mortality, dispersal, disease progression and disease transmission \n71 collected from the population at APHA’s Woodchester Park research station in Gloucestershire. \n72 Population density was taken from badger sett surveys conducted in County Down before the \n73 trial, and the demographic makeup of social groups was matched to the local population. The \n74 retention of some parameter values from the English model would have had minimal effect on \n75 the simulated output as the epidemiology is driven by badger density and disease prevalence, \n76 which were closely matched to the Northern Ireland study site. All model parameters and their \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n5\n77 source are given in Table S1 and a full description of the model using the ODD protocol [25, 26] \n78 is in a supplementary file (S2).\n79 The model arena comprised of a 100 x100 grid, with each cell representing 200m x 200m; \n80 the total grid representing a 400 km2 landscape area. The population was 550 badgers in 85 social \n81 groups. The arena comprised a central core of approximately 100 km 2 where badger \n82 management was undertaken, and the boundary was defined by the extent of participating \n83 farmland. Outside the core was a surrounding buffer two social groups wide where the possible \n84 influence of control could be observed and outside this any effect of culling was expected to be \n85 negligible. The grid was wrapped to form a torus to eliminate edge effects. Social groups were \n86 randomly distributed across the arena and all badgers were members of a group and occupied a \n87 territory which defined which social groups were neighbours.\n88 Characterisation of badgers\n89 Individual badgers were characterized by the variables: social group, sex, age, and health-\n90 status. The age categories were cub, yearling (one-year old), and adult. The TB-status categories \n91 were defined as: healthy, infected, single-site and multi-site excretor. Probability of disease \n92 progression was based on field data from Woodchester Park [27]. Badger fecundity was density-\n93 dependent based on an upper limit of litters in each social group. Births were simulated at the \n94 start of the year, and litter size was modelled probabilistically from a distribution of known litter \n95 sizes [28], with a mean of 2.94 cubs per litter, and a sex ratio of 1:1. State-dependent mortality \n96 rates were based on field data from Woodchester Park [27]. Badgers up to two months of age \n97 (i.e. while still underground) had a higher mortality rate than older badgers [29]. Animals in the \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n6\n98 excretor disease classes also had higher mortality rates [27]. Badgers were allowed to disperse, \n99 usually to smaller social groups if available [30], based on sex-dependent probabilities (males \n100 more often than females) but independent of age and season. Badgers were also moved to \n101 neighbouring social groups in response to any demographic imbalance. The probability of \n102 transmission between individual badgers was adjusted so the population disease prevalence \n103 matched the reported prevalence at the start of the study, estimated at 0.14 [24]. Disease \n104 transmission occurred between animals of the same and neighbouring social groups. As badgers \n105 interact more frequently with their own social group than with neighbouring groups, within-\n106 group transmission was given a greater probability (20-fold) than between animals in \n107 neighbouring groups [12]. Transmission probability increased as animals moved from excretor to \n108 super excretor class.  \n109 Simulation of management operations\n110 Prior to simulation of management operations, the model was run for 100 years to allow the \n111 population and disease dynamics to stabilise after seeding. Management operations were \n112 simulated by allocating badgers a probability of capture based on the proportion of accessible \n113 land (0.94) supplied by DAERA (Menzies, F., pers. comm.) and trapping efficacy rates (0.54) [21]. \n114 Social groups were allocated to one of two trapping campaigns each year; territories not wholly \n115 within accessible land could still be subject to some level of control as badgers could be trapped \n116 away from the main sett. Badgers were individually marked during the study so recaptured \n117 animals were identifiable and this information was also available in the model.  \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n7\n118 In the field trial, animals were tested trap-side using the Dual-Path Platform VetTB test (DPP) \n119 on whole blood samples. In year one (2014), regardless of test result all badgers were vaccinated \n120 and released. In years two to five (2015-2018), test positive badgers were culled and test negative \n121 badgers vaccinated and released [21]. Field trial methods were replicated in the model with the \n122 simulation of one year of vaccination only, followed by four years of TVR. The model simulated \n123 DPP testing using a test sensitivity of 0.63 and specificity of 0.94 [24], based on the use of lines 1 \n124 or 2 in the DPP test under field conditions. In the study, animals were vaccinated with BCG Danish \n125 in years 1-3 and BCG Sophia in years 4-5 due to supply issues. It was assumed in the model that \n126 both vaccines gave a 0.6 probability of providing full protection from infection for susceptible \n127 badgers. Protection was for the lifetime of the badger, with further opportunity for full protection \n128 at subsequent capture for animals for which vaccination had previously been unsuccessful. A \n129 simulation of the same population with no control was also undertaken to provide a baseline. A \n130 total of 100 simulations was run for each model scenario.  \n131 Field results suggest limited social perturbation resulted from badger removal operations \n132 [31, 32]. Therefore, effects of perturbation were not simulated beyond the filling of demographic \n133 vacancies in neighbouring social groups described above. Although TVR does result in additional \n134 vacancies, there are many fewer than with non-selective culling and this demographic \n135 rebalancing contributes little additional transmission compared to the increased ranging \n136 behaviour seen in removal operations such as the Randomised Badger Culling Trial (RBCT) in \n137 England [33]. \n138\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n8\n139 Results\n140 Model output is reported for the number of unique badger captures and the number of \n141 badgers testing positive using the simulated DPP test; these are compared to results from the \n142 study. Model output is also recorded for disease prevalence in the core, buffer, outer area of the \n143 arena and mean of the whole arena under TVR and no control; prevalence in the core is compared \n144 to the empirical estimate of prevalence from a Bayesian model combining three test methods \n145 used in the field trial [24]. \n146 The number of unique badger captures did not vary substantially over the course of the study \n147 as relatively few were removed, and the population recovered before the following year. The \n148 model produced a similar result, although the number reported by the model was highest in the \n149 first year whereas in the field study the maximum occurred in the second year (Fig1). \n150\n151 Fig 1. Comparison between model results and data from the field trial for number of unique \n152 badger captures in each year of the trial. Points indicate results from the TVR trial and violin \n153 plots show the distribution of model predictions.\n154\n155 The simulated proportion of captured badgers that tested positive using the cage-side DPP \n156 test was in line with the general trend in population prevalence. The number testing positive in \n157 the model was in reasonable agreement with the study in each year except 2016, where the field \n158 results were unusually low, but even in that year there was overlap between model results and \n159 the 95% confidence limits of the empirical estimate (Fig 2). Since in year one (2014), all badgers \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n9\n160 were vaccinated and released regardless of test result, and in years 2-5 (2015-2018) test positive \n161 badgers were removed, for these later years the proportion testing positive is equal to the \n162 proportion of captured badgers removed.\n163\n164 Fig 2. Comparison between model results and data from the field trial for proportion of \n165 captures testing positive using the cage-side DPP test in each year of the trial . Points indicate \n166 results from the TVR trial with error bars showing 95% binomial confidence interval, and violin \n167 plots show the distribution of model predictions. \n168\n169 A large benefit of control was seen in the core where population level disease prevalence \n170 was predicted to reduce from the initial value of 0.14 to about 0.02, which closely matched the \n171 empirical estimate for change in annual prevalence [24] (Fig 3). The simulation was continued to \n172 year 2035 assuming no further control was applied (Fig 4). The model predicted prevalence in \n173 the core would slowly increase to about 0.05 some 15 years after control ended, although long-\n174 term model projections are always less reliable than short-term ones. The outcome when no \n175 control was applied was an unchanging population prevalence. A small benefit was seen in the \n176 buffer because some groups there may have partly overlapped with participating farms and \n177 therefore experienced some removal. There would also have been a small effect over the course \n178 of the study as some diseased animals in the higher prevalence buffer will have moved to the \n179 core, slightly reducing the benefit of control seen there and also some animals emigrated from \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n10\n180 the lower prevalence population in the core to the buffer, reducing the level of disease in the \n181 buffer.\n182\n183 Fig 3. Model results for median annual prevalence in the core area during the control period, \n184 with the no control scenario shown for comparison. Shading indicates the inter-quartile range \n185 for the model predictions. The dashed line represents the estimated population-level prevalence \n186 from each year of the trial [24].\n187 Fig 4. Model results for median annual prevalence in each zone and overall simulated area \n188 (Arena), predicted to year 2035. Vertical dotted lines indicate start and end of TVR trial. Shading \n189 indicates inter-quartile range for model predictions. \n190\n191 Discussion\n192 In recent decades there has been an increasing reliance on using computer models to predict \n193 the consequences of disease outbreaks or disease control. Such models can rarely be validated \n194 prior to any control in the field. Here we take the original model used to evaluate a TVR badger \n195 control study for bovine TB in Northern Ireland and uniquely validate it against the data from the \n196 field trial. Such validation increases support for its use in other locations, or other scenarios.\n197 During the five-year field trial a total of 824 badgers were caught, with between 271 and 341 \n198 unique captures each year [21]. This agrees well with the simulation, although the numbers \n199 caught in 2015 were higher than in 2014, whereas in the model the reverse was expected. Each \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n11\n200 year between 4% and 16% of badgers were removed: i.e., were DPP test positive [21]. This also \n201 agrees with our initial expectation of an 83% reduction in the number of badgers culled compared \n202 to a proactive cull: i.e., all trapped badgers would have been removed. \n203 However, the most important prediction of the model was a substantial reduction in disease \n204 prevalence if social perturbation did not occur. During the trial a total of 105 individual badgers \n205 were followed using GPS collars, and there was no evidence of a change in home range size, \n206 neither annually, nor monthly, between the years of the study [31]. This strongly suggests that \n207 social perturbation did not occur in this population during the study. The field trial demonstrated \n208 a substantial decline in prevalence during the trial [24], with the last years having a slightly lower \n209 prevalence than the simulated results, when we also assumed no social perturbation (Fig 3). \n210 Therefore, the model may be slightly conservative about the level of disease reduction. It is also \n211 worth noting that we predicted a slow recover of disease in the badger population, but it would \n212 require a repeated field study on this site to confirm or deny this longer-term prediction, and in \n213 general longer-term model predictions are less reliable as other factors may well occur in the \n214 interim. \n215 Overall, this points to the success of the model in predicting the effects of the TVR approach \n216 in Northern Ireland. Since the simulated output depends most heavily on the badger social \n217 groups size and density, it therefore seems likely that the TVR approach would have similar \n218 outcomes across Ireland or other areas where badger dynamics are similar, but we cannot \n219 immediately extrapolate these results to England and Wales where social group size and density \n220 are both higher. However, in many areas in England badgers have been subjected to recent \n221 culling, and the density and social group size may now be more similar to the field study. We also \n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n12\n222 did not account for any ongoing transmission from cattle to badgers throughout the trial and this \n223 would be expected to ‘seed’ the badger population with more M. bovis . Longer term model \n224 predictions are always less reliable than short term predictions, due to stochastic drift, changes \n225 in populations dynamics, habitat and farming, and the additional seeding that may occur from \n226 cattle would further erode the accuracy over time. Thus, we do not place any reliance on the \n227 longer-term dynamics of disease in the badger population at this stage. To gain more accurate \n228 longer-term dynamics would require linking this model to a cattle TB model, and including cattle \n229 management. \n230\n231 Conclusions\n232 Models are often used to evaluate disease management scenarios in both animals and man. \n233 Such models are often fitted to field data to help parameterize them, but validation against \n234 unrelated field data is uncommon. We used an established simulation model of badgers and \n235 bovine tuberculosis and adjusted it to the local situation in Northern Ireland to predict the \n236 outcome of a proposed field study of a selective cull/vaccinate strategy. In this paper we report \n237 on the output of that model, after parameter changes to exactly fit the start of the field trial. The \n238 field study confirmed that no substantial social perturbation was apparent, and the revised \n239 model used to make retro-predictions that closely matched the real-world data. This is the first \n240 time that the simulation model was validated against badger vaccination and means that the use \n241 of the model for badger vaccination in other circumstances, such as in England where the focus \n242 is changing from culling to vaccination, should be reliable.\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n13\n243\n244 Acknowledgments\n245 The authors would like to acknowledge the help of Fraser Menzies for helpful discussion on both \n246 the original modelling for the TVR study, and the start conditions for this follow up analysis.\n247\n248 References\n249 1. Defra. 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Is moving from targeted \n303 culling to BCG-vaccination of badgers ( Meles meles) associated with an unacceptable increased \n304 incidence of cattle herd tuberculosis in the Republic of Ireland? A practical non-inferiority wildlife \n305 intervention study in the Republic of Ireland (2011-2017). Prev Vet Med. 2020;179:105004. doi: \n306 https://doi.org/10.1016/j.prevetmed.2020.105004.\n307 19. Smith GC, Budgey R, Delahay  RJ. A simulation model to support a study of test and vaccinate or \n308 remove (TVR) in Northern Ireland. Department of Agriculture and Rural Development, Northern Ireland, \n309 2013 9th Sept. 2013. Report No.\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n16\n310 20. 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J Zool. 1997;242:705-28. doi: https://doi.org/.\n338 30. Rogers LM, Delahay R, Cheeseman CL, Langton S, Smith GC, Clifton-Hadley RS. Movement of \n339 badgers (Meles meles) in a high density population: individual, population and disease effects. Proc R \n340 Soc B. 1998;265:1269-76. doi: https://doi.org/10.1098/rspb.1998.0429.\n341 31. O'Hagan MJH, Gordon AW, McCormick CM, Collins SF, Trimble NA, McGeown CF, et al. Effect of \n342 selective removal of badgers ( Meles meles) on ranging behaviour during a ‘Test and Vaccinate or \n343 Remove’ intervention in Northern Ireland. Epidemiol Infect. 2021;149:e125. Epub 05/07. doi: \n344 https://doi.org/10.1017/S0950268821001096.\n345 32. Redpath SHA, Marks NJ, Menzies FD, O’Hagan MJH, Wilson RP, Smith S, et al. Impact of test, \n346 vaccinate and remove protocol on home ranges and nightly movements of badgers in a medium density \n347 population. Scientific Reports. 2023;13(1):2592. doi: DOI10.1038/s41598-023-28620-1.\n348 33. Jenkins HE, Woodroffe R, Donnelly CA. The duration of the effects of repeated widespread \n349 badger culling on cattle tuberculosis following the cessation of culling. PLoS One. 2010;5(2):e9090. doi: \n350 https://doi.org/10.1371/journal.pone.0009090.\n351\n352 Supporting Information\n353 S1 Table. Input model parameters.\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n18\n354 S2 File. Model ODD protocol.\n355\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \n(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 \nThe copyright holder for this preprintthis version posted March 3, 2025. ; https://doi.org/10.1101/2025.02.27.640543doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}