Potential benefits of cattle vaccination as a supplementary control for bovine tuberculosis

Abstract

Vaccination for the control of bovine tuberculosis (bTB) in cattle is not currently used within any international control program, and is illegal within the EU. Candidate vaccines, based upon Mycobacterium bovis bacillus Calmette-Guérin (BCG) all interfere with the action of the tuberculin skin test, which is used to determine if animals, herds and countries are officially bTB-free. New diagnostic tests that Differentiate Infected from Vaccinated Animals (DIVA) offer the potential to introduce vaccination within existing eradication programs. We use within-herd transmission models estimated from historical data from Great Britain (GB) to explore the feasibility of such supplemental use of vaccination. The economic impact of bovine Tuberculosis for farmers is dominated by the costs associated with testing, and associated restrictions on animal movements. Farmers' willingness to adopt vaccination will require vaccination to not only reduce the burden of infection, but also the risk of restrictions being imposed. We find that, under the intensive sequence of testing in GB, it is the specificity of the DIVA test, rather than the sensitivity, that is the greatest barrier to see a herd level benefit of vaccination. The potential negative effects of vaccination could be mitigated through relaxation of testing. However, this could potentially increase the hidden burden of infection within Officially TB Free herds. Using our models, we explore the range of the DIVA test characteristics necessary to see a protective herd level benefit of vaccination. We estimate that a DIVA specificity of at least 99.85% and sensitivity of >40% is required to see a protective benefit of vaccination with no increase in the risk of missed infection. Data from experimentally infected animals suggest that this target specificity could be achieved in vaccinates using a cocktail of three DIVA antigens while maintaining a sensitivity of 73.3% (95%CI: 61.9, 82.9%) relative to post-mortem detection.

Fig 1. Age-stratified patterns of reactors in Great Britain.

A The relative risk of infection by age RR(a) measured relative to the risk for 0–1 year old cattle, Error bars denote 95% credible intervals. RR(a) is calculated as the ratio of the force of infection for each age group divided by the estimate for the 0–1 year old group. The force of infection is a combined estimate for all national herds (beef and dairy) calculated from reactor cattle reported between 2004–2009 as described in [21]. B The probability of “confirmation” of reactor animals by the presence of visible lesions or culture as a function of age at slaughter, estimated as the proportion of reactor animals within each (200 day) age-class that demonstrated visible lesions or a positive culture result. Estimates are calculated using all reactor animals from within our study population of herds. The qualitative pattern is robust between different test-types and parish testing intervals [21].

Fig 2. Persistence and surveillance metrics for bTB in GB herds (2003–2011).

Within-herd measures of persistence and surveillance used as target metrics for ABC and to assess model fit. We present four key measures, from left to right: the proportion of prolonged (restrictions of greater duration than 240 days) and recurrent breakdowns, the proportion of herds with evidence of visible lesions and the total number of reactors per breakdown. Breakdowns are classified as either OTF-S (officially TB free suspended), where no reactors are found to have visible lesions (lime green circles), or OTF-W (officially TB free withdrawn) where at least one reactor was found to have evidence of visible lesions or be culture positive (magenta squares). The proportion of such OTF-W breakdowns is shown along with the proportion of these that were initiated by a slaughterhouse case (black circles). The relationship of each measure with herd size is plotted, with breakdowns further stratified by the historical parish testing interval (A, PTI1; B, PTI 2; C PTI 4) and breakdown status. Mean target observations are plotted with uncertainty estimated as ±1.96 standard errors around the mean. Predictive distributions from our within-herd (SORI) model for each of these measures are plotted as shaded density strips where the intensity of color is proportional to the probability density at that point [34].

Fig 3. SORI model fit to age-distributions of reactors.

SORI model predictive distributions for the age of reactors (A). Age of “confirmed” reactors with evidence of visible lesions (B). Slaughterhouse cases (C) and the proportion of animals with visible lesions stratified by age (D). Solid points and lines indicate empirical target distributions, model predictive distributions are once again overplotted as shaded density strips where the intensity of color is proportional to the probability density at that point [34].

Fig 4. Break-even points for vaccine efficacy under alternative testing scenarios using γ-DIVA test.

We estimate the break-even point for a protective benefit of BCG vaccination at the herd level under three alternative testing scenarios. We model DIVA testing using parameter estimates that optimize DIVA specificity of 99.4% under the constraint of maintaining a DIVA sensitivity comparable to tuberculin testing of 64.4%. We consider four key measures of the epidemiological, and economic, costs associated with bTB testing: A the number of animals condemned as reactors; B the number of tests (tuberculin and DIVA) needed to clear restrictions; C The number of infected animals left in herds after restrictions are lifted (burden of infection missed by testing) D The number of herds that experience a breakdown before the herd clears the singleton challenge. For all panels, solid black lines indicate the median break-even point for the baseline scenario with no vaccination. Dashed lines indicate the 95% quantiles of the baseline scenario. The distribution for each measure is calculated from 100 simulations with parameters drawn from the (approximate) posterior distributions of our estimated model, with each parameter set simulated once for each herd within our representative study population (of 6,601 herds).

Fig 5. Probability of a protective herd level benefit of vaccination for alternative DIVA testing strategies.

We explore how the probability of seeing a protective benefit of vaccination depends on the assumed sensitivity and specificity of DIVA testing. The probability of benefit is calculated for a singleton challenge of infection and relative to the current statutory regime of tuberculin testing and slaughterhouse surveillance. Benefit is estimated from 100 simulations of our study population (6,601 herds) for three key measures (across columns): the total number of tests required to clear restrictions (A,D,G), the probability of restrictions being applied before the herd clears infection (B,E,H) and the probability of infection remaining in a herd when restrictions are lifted (C,F,I). We define the break-even point as 50% of herds demonstrating a protective benefit illustrated by the white band in the color map with red values worse this threshold and grey points better. We compare the three strategies described in the main text (across rows): (A,B,C) Under the DIVA negation scenario, the break-even point is limited by the considerable overhead in testing, with an increased probability of restrictions being applied before the herd clears infection (B) and an increase in testing (A) even for a 100% sensitive and specific DIVA test. (D,E,F) Under DIVA replacement, a protective benefit of vaccination can be achieved for DIVA specificities > 99.90% (D). The break-even point also depends on DIVA sensitivity, with a sensitivity of at least 40% being necessary to avoid increased risk of leaving infection in the herd after restrictions are lifted (F). (G,H,I) Under the VLend scenario, linking the maintenance of restrictions to detection of lesioned reactor animals mitigates the addition costs of testing under other scenarios (G). However, a specificity of greater than 99.85% is still required to see no increase in the number of breakdowns with vaccination (H), with the break-even point depending again on a DIVA sensitivity of greater than 40% (I).