Estimating the hidden burden of bovine tuberculosis in Great Britain

Abstract

The number of cattle herds placed under movement restrictions in Great Britain (GB) due to the suspected presence of bovine tuberculosis (bTB) has progressively increased over the past 25 years despite an intensive and costly test-and-slaughter control program. Around 38% of herds that clear movement restrictions experience a recurrent incident (breakdown) within 24 months, suggesting that infection may be persisting within herds. Reactivity to tuberculin, the basis of diagnostic testing, is dependent on the time from infection. Thus, testing efficiency varies between outbreaks, depending on weight of transmission and cannot be directly estimated. In this paper, we use Approximate Bayesian Computation (ABC) to parameterize two within-herd transmission models within a rigorous inferential framework. Previous within-herd models of bTB have relied on ad-hoc methods of parameterization and used a single model structure (SORI) where animals are assumed to become detectable by testing before they become infectious. We study such a conventional within-herd model of bTB and an alternative model, motivated by recent animal challenge studies, where there is no period of epidemiological latency before animals become infectious (SOR). Under both models we estimate that cattle-to-cattle transmission rates are non-linearly density dependent. The basic reproductive ratio for our conventional within-herd model, estimated for scenarios with no statutory controls, increases from 1.5 (0.26-4.9; 95% CI) in a herd of 30 cattle up to 4.9 (0.99-14.0) in a herd of 400. Under this model we estimate that 50% (33-67) of recurrent breakdowns in Britain can be attributed to infection missed by tuberculin testing. However this figure falls to 24% (11-42) of recurrent breakdowns under our alternative model. Under both models the estimated extrinsic force of infection increases with the burden of missed infection. Hence, improved herd-level testing is unlikely to reduce recurrence unless this extrinsic infectious pressure is simultaneously addressed.

Figure 1. Parish Testing Intervals (PTI) for bTB in GB (2003–2005).

Routine surveillance for bTB in GB is based upon the regular (SICCT) testing of herds at a frequency determined by the local incidence of affected premises. Panel (A) maps the shortest recorded PTI for each parish over our study period of 2003–2005. High incidence areas are spatially clustered with the greatest incidence, and thus intensity of testing, in the south-west of England and south Wales. PTI therefore offers a crude categorization of herds according to epidemiological risk, past history of testing and to a lesser extent, geographical location. In contrast to surveillance testing, the sequence of tests (B) following a breakdown are dependent only on the outcome of tests on the affected premises. A failed surveillance test leads to a sequence of short interval tests (SI) at intervals of at least 60 days. Confirmation of infection, through isolation of M. bovis or evidence of visible lesions at slaughter, leads to tests being re-interpreted under a “severe” interpretation of the SICCT, until one test is passed; testing then continues until an additional test at the standard interpretation is passed. After a breakdown is cleared follow-up tests are scheduled at intervals of at least 6 and 12 months, after which testing frequency reverts to the local parish testing frequency.

Figure 2. The persistence of bTB in GB herds (2003–2005).

The within-herd persistence of bTB in GB as measured by the probability of all GB breakdowns from our study population being prolonged (duration of greater than 240 days, top panel) or recurrent within 24 months (middle panel). The relationship of each measure is plotted against herd size, with breakdowns further stratified by parish testing interval (PTI 1, 2 and 4, left to right) and confirmation status (unconfirmed breakdowns: lime green, circles, confirmed breakdowns: magenta squares). Uncertainty in each (mean) target observation (thick lines) is illustrated by an envelope (thin lines) of ±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 .

Figure 3. Herd level measures of efficiency of transmission and clearance of infection for SORI and SOR models.

(A) Predictive distribution for the within-herd reproduction ratio (R₀), plotted as a shaded density strip where the intensity of shading is proportional to the probability that R₀ takes a given value . We calculate where measures the strength of density dependence, is the estimated transmission parameter and the per capita rate of turnover of the herd sampled from an empirical distribution and . Both models estimate that transmission is non-linearly density dependent with point estimates for the invasion threshold (R₀ = 1) of 71 (8–674,95% CI) for the SORI model and 12 (1–833, 95% CI) for the SOR model. (B) The effective sensitivity of the SICCT test within our study population of herds measured under the standard (solid line) and severe (dashed line) interpretations and relative to the gold standard of confirmation with visible lesions (dotted line). (C,D) The infectious burden remaining after movement restrictions are lifted can be characterized in terms of the number of herds with at least one infectious animal remaining when movement restrictions are lifted (C) and the expected distribution of animals on those herds (D). Predictive distributions for (B,C,D) are calculated by simulating from the empirical distribution of herds taken from our study population, representing the national distribution of herds.

Figure 4. Impact of herd-level interventions on probability of recurrence within 24 months.

Change in the probability of a herd experiencing a recurrent breakdown after application of a ‘perfect’ test (left column) or perfect isolation (right column). The perfect test is assumed to have 100% sensitivity and specificity and no occult period. Perfect isolation corresponds to setting the extrinsic infectious pressure to zero at the end of a breakdown (). Plotted values correspond to the average % difference in the probability of recurrence relative to the fitted SORI (Panel A) and SOR models (Panel B). Separate series are plotted for herds in PTI 1 (lime green circles), 2 (magenta squares) & 4 (sky blue diamonds). Predictive distributions illustrating the variability in these point (mean) estimates are presented in supplementary figure S13 .