Multi-state modelling reveals sex-dependent transmission, progression and severity of tuberculosis in wild badgers

Graham, Jordon; Smith, Graham C.; Delahay, Richard J.; Bailey, Trevor; McDonald, Robbie A.; Hodgson, Dave

doi:10.1017/s0950268812003019

Cited by 49 publications

(77 citation statements)

References 34 publications

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“…Mortality patterns highlight raised mortality in males following infection, but otherwise comparable trajectories, suggesting weaker immunological defences. This is consistent with results of prior studies that showed males suffering rapid disease progression [14] and substantial weight loss [33] following infection. Immunological defences are costly and can trade off with other physiological processes [14,34,35], perhaps resulting in differential investment between the sexes.…”

Section: Discussionsupporting

confidence: 93%

“…This is consistent with results of prior studies that showed males suffering rapid disease progression [14] and substantial weight loss [33] following infection. Immunological defences are costly and can trade off with other physiological processes [14,34,35], perhaps resulting in differential investment between the sexes. Although chromosomal differences and other physiological processes cannot be discounted, sex hormones are suggested to be strong determinants of immune response [36], responsible for sex-specific differences to mycobacterial infection across study species [3].…”

Section: Discussionsupporting

confidence: 93%

“…While increased mortality due to bTB infection is already known to occur in male badgers [14,15], we have now located where in the infection process these sex-related differences arise, and can begin to infer the mechanisms that might generate this variation. Mortality rates at the point of infection are very similar between the sexes, suggesting that elevated mortality in infected males is not due to the coincidental risks of natural mortality and infection.…”

Section: Discussionmentioning

confidence: 97%

“…Sex differences in epidemiological traits have been observed in bTB-infected badgers, with males suffering increased mortality during early disease stages and faster progression into advanced diseased states [14], where they experience double the rate of disease-induced mortality when compared with females [14,15]. We test contrasting hypotheses and describe sex-related differences in the mortality trajectories of badgers.…”

Section: Introductionmentioning

confidence: 98%

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Mortality trajectory analysis reveals the drivers of sex-specific epidemiology in natural wildlife–disease interactions

et al. 2014

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In animal populations, males are commonly more susceptible to disease-induced mortality than females. However, three competing mechanisms can cause this sex bias: weak males may simultaneously be more prone to exposure to infection and mortality; being ‘male’ may be an imperfect proxy for the underlying driver of disease-induced mortality; or males may experience increased severity of disease-induced effects compared with females. Here, we infer the drivers of sex-specific epidemiology by decomposing fixed mortality rates into mortality trajectories and comparing their parameters. We applied Bayesian survival trajectory analysis to a 22-year longitudinal study of a population of badgers (Meles meles) naturally infected with bovine tuberculosis (bTB). At the point of infection, infected male and female badgers had equal mortality risk, refuting the hypothesis that acquisition of infection occurs in males with coincidentally high mortality. Males and females exhibited similar levels of heterogeneity in mortality risk, refuting the hypothesis that maleness is only a proxy for disease susceptibility. Instead, sex differences were caused by a more rapid increase in male mortality rates following infection. Males are indeed more susceptible to bTB, probably due to immunological differences between the sexes. We recommend this mortality trajectory approach for the study of infection in animal populations.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 98%

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Mortality trajectory analysis reveals the drivers of sex-specific epidemiology in natural wildlife–disease interactions

et al. 2014

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“…By comparison with an earlier published version of the model [18], badger mortality rates and disease state definitions were updated on the basis of recent work [24]. Simulations explored the impact of social perturbation of badger populations extending over different spatial scales and temporal duration until the model outcomes best matched the latest analysis of cattle herd breakdown (CHB) rates over time from the Randomised Badger Culling Trial (RBCT), an extensive field-based investigation into the effects of badger culling in South West England [7].…”

Section: Methodsmentioning

confidence: 99%

Model of Selective and Non-Selective Management of Badgers (Meles meles) to Control Bovine Tuberculosis in Badgers and Cattle

et al. 2016

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Bovine tuberculosis (bTB) causes substantial economic losses to cattle farmers and taxpayers in the British Isles. Disease management in cattle is complicated by the role of the European badger (Meles meles) as a host of the infection. Proactive, non-selective culling of badgers can reduce the incidence of disease in cattle but may also have negative effects in the area surrounding culls that have been associated with social perturbation of badger populations. The selective removal of infected badgers would, in principle, reduce the number culled, but the effects of selective culling on social perturbation and disease outcomes are unclear. We used an established model to simulate non-selective badger culling, non-selective badger vaccination and a selective trap and vaccinate or remove (TVR) approach to badger management in two distinct areas: South West England and Northern Ireland. TVR was simulated with and without social perturbation in effect. The lower badger density in Northern Ireland caused no qualitative change in the effect of management strategies on badgers, although the absolute number of infected badgers was lower in all cases. However, probably due to differing herd density in Northern Ireland, the simulated badger management strategies caused greater variation in subsequent cattle bTB incidence. Selective culling in the model reduced the number of badgers killed by about 83% but this only led to an overall benefit for cattle TB incidence if there was no social perturbation of badgers. We conclude that the likely benefit of selective culling will be dependent on the social responses of badgers to intervention but that other population factors including badger and cattle density had little effect on the relative benefits of selective culling compared to other methods, and that this may also be the case for disease management in other wild host populations.

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Defining and testing a wildlife intervention framework for exotic disease control

Smith

Brough

Podgórski

et al. 2022

Ecol Sol and Evidence

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1. Outbreaks of disease at the wildlife-livestock interface may require management interventions. Where they involve exotic/non-endemic pathogens in wildlife, then such plans may need to be rapidly conceived and implemented to prevent further disease spread. However, detailed information on the distribution of infection is likely to be absent, whilst limited resources and tools, and ethical considerations impose constraints on what is possible and proportionate.2. We describe four considerations to inform decision-making on whether to intervene. With reference to two recent examples (bovine tuberculosis in badgers in an otherwise disease-free area, and African swine fever in wild boar in central Europe), we outline six steps to guide implementation. 3. In both examples, a Disease Control Area was defined by determining a minimum infected area (MIA) and a buffer zone based on wildlife ecology and the potential for unrecorded spread of infection. The total area was the subject of management intervention, with the level of infection used to inform adaptive management.4. The MIA is defined as the minimum area that could contain all infected wildlife, given our current level of information, with a surrounding buffer zone to account for the possibility of further onward spread. In our examples, the zones were defined using a combination of field data and existing knowledge of host spatial organization and movement.5. The MIA (plus buffer zone) approach provides a framework for optimizing the targeting of resources in a proportionate response to a disease outbreak. Where infected animals occur outside the MIA, our examples show how wildlife management has been adapted. In principle, this approach is suitable for the delivery of disease outbreak control in wildlife, whether through culling, vaccination or other forms of control.

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Multi-state modelling reveals sex-dependent transmission, progression and severity of tuberculosis in wild badgers

Cited by 49 publications

References 34 publications

Mortality trajectory analysis reveals the drivers of sex-specific epidemiology in natural wildlife–disease interactions

Mortality trajectory analysis reveals the drivers of sex-specific epidemiology in natural wildlife–disease interactions

Model of Selective and Non-Selective Management of Badgers (Meles meles) to Control Bovine Tuberculosis in Badgers and Cattle

Defining and testing a wildlife intervention framework for exotic disease control

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