Development of integrated surveillance systems for the management of tuberculosis in New Zealand wildlife

Abstract

Disease surveillance for the management of bovine tuberculosis (TB) in New Zealand has focussed, to a large extent, on the development of tools specific for monitoring Mycobacterium bovis infection in wildlife. Diagnostic techniques have been modified progressively over 30 years of surveillance of TB in wildlife, from initial characterisation of gross TB lesions in a variety of wildlife, through development of sensitive culture techniques to identify viable mycobacteria, to molecular identification of individual M. bovis strains. Of key importance in disease surveillance has been the elucidation of the roles that different wildlife species play in the transmission of infection, specifically defining brushtail possums (Trichosurus vulpecula) as true maintenance hosts compared to those that are predominantly spillover hosts, but which may serve as useful sentinel species to indicate TB persistence. Epidemiological modelling has played a major role in TB surveillance, initially providing the theoretical support for large-scale possum population control and setting targets at which control effort should be deployed to ensure disease eradication. As TB prevalence in livestock and wildlife declined throughout the 2000s, more varied field tools were developed to gather surveillance data from the diminishing possum populations, and to provide information on changing TB prevalence. Accordingly, ever more precise (but disparate) surveillance information began to be integrated into multi-faceted decision-assist models to support TB management decisions, particularly to provide informed parameters at which control effort could be halted, culminating in the Proof of Freedom modelling framework that now allows an area to be declared TB-free within chosen confidence limits. As New Zealand moves from large-scale TB control to regional eradication of disease in the coming years, further integrative models will need to be developed to support management decisions, based on combined field data of possum and TB prevalence, sentinel information, risk assessment in relation to financial benefits, and changing political and environmental needs.

Cost-based optimization of the stopping threshold for local disease surveillance during progressive eradication of tuberculosis from New Zealand wildlife

“…In cases where eradication of an animal disease has been undertaken, the disease in question has often been characterized by highly visible epidemics in livestock and/or wildlife (James, ; Klepac, Metcalf, McLean, & Hampson, ); by comparison, M. bovis infection in possums is characterized by a form of tuberculosis involving a long disease course and low overall prevalence among infected populations (Nugent, Buddle et al., ), making it difficult and expensive to detect (especially where possum densities have been reduced to low levels by previous population control). The quantitative Proof of Freedom framework (Anderson et al., , ) was developed specifically by our group to determine the likelihood of successful eradication of M. bovis from local possum populations, part of which involves the use of a probabilistic stopping threshold; but until now this has utilized a set stopping threshold without a clear basis for optimization. To extend this, we therefore assumed a variable stopping threshold could be applied, and then provided some insight into factors that should be considered when setting variable stopping thresholds for differing scenarios.…”

“…Our simulations further suggested that where disease surveillance is inexpensive (e.g., because the cost per surveillance unit is low and/or because the surveillance sensitivity per unit is high), then the optimal stopping threshold will be higher rather than lower—in other words—it would be cheaper, on average, to minimize the risk of failure (i.e., making a false declaration of freedom from M. bovis infection) than it would be to remedy any such failure. Although we compared only two alternative surveillance tools here (leg‐hold traps alone, or DDs (chew‐cards) with follow‐up trapping), there are other options for surveillance of residual M. bovis infection in possums in New Zealand—most notably—the use of wildlife spillover hosts as sentinels for persistent infection (e.g., feral pigs and ferrets and wild deer; Nugent, ; Anderson et al., ). In particular, feral pigs are especially sensitive detectors of persistent M. bovis infection in possums (Nugent, Gortazar, & Knowles, ; Nugent, Whitford, & Young, ; Nugent, Yockney, Whitford, & Cross, ), so where they are abundant and inexpensive to procure—a high stopping threshold would be optimal.…”

“…Detection devices (DDs) are low‐cost, non‐capture devices that identify the presence of a possum or possums in the vicinity, most commonly by recording biting activity on a palatable (but non‐edible) medium; chew‐cards, for example, record possum incisor indentations into a fluted cardboard sheet that has been impregnated with a lure (Sweetapple & Nugent, ). Trapping is usually by leg‐hold traps; possums captured in a survey are killed and subjected to a detailed necropsy examination, focussing on predilection‐site tissues for the presence of gross tuberculous lesions (and—in many cases—follow‐up bacteriological culture to identify M. bovis in the absence of gross lesions; Warburton & Livingstone, ; Anderson et al., ). If M. bovis infection is detected, further control is implemented.…”

“…This modelling uses as its basis the individually based spatially explicit simulation model of Possum‐TB dynamics described by Ramsey and Efford (), in conjunction with expert opinion. Next, once the probability of eradication for any given VCZ reaches a specified level, the control‐only phase ceases and a surveillance phase, often spanning several years, is initiated. This phase involves surveillance of possums (mostly via necropsy surveys) and other wildlife hosts (deer, pigs and ferrets) to quantify the probability of true absence of M. bovis infection from wildlife in the VCZ (Nugent, Buddle et al., ). Consequently, the “prior” probability that an area is free of M. bovis PFree prior provides the starting point in a quantitative Bayesian belief‐updating framework for determining whether the VCZ is “statistically free” of M. bovis infection in possums, termed the Proof of Freedom framework (Anderson et al., ). Once further surveillance data have been gathered, the prior is re‐evaluated (to account for the passage of time and incidental control on the probability of disease persistence) and then updated with empirical surveillance data from surveys in which no M. bovis infection was detected to calculate a “posterior” probability PFree post (Anderson et al., ). Finally, this latter PFree post is used by decision‐makers (in conjunction with other considerations) to declare a VCZ to be free of recurrent M. bovis infection (and to cease active possum control and wildlife disease surveillance) if it exceeds a specified probability, termed the “stopping threshold.” To date, this stopping threshold has been set at a pre‐determined level of 0.95, which in principle implies that areas are declared free while they still have a 1‐in‐20 chance of retaining infected wildlife.…”

Monitoring, imperfect detection, and risk optimization of a Tasmanian devil insurance population

“…There are some published examples in which imperfect‐detection modeling has been used to underpin the management of a cryptic wildlife disease (Anderson et al. , ).…”