Early detection and rapid response are crucial to avoid severe epidemics of exotic pathogens. However, most detection methods (molecular, serological, chemical) are logistically limited for large-scale survey of outbreaks due to intrinsic sampling issues and laboratory throughput. Evaluation of 10 canines trained for detection of a severe exotic phytobacterial arboreal pathogen, Candidatus Liberibacter asiaticus ( C Las), demonstrated 0.9905 accuracy, 0.8579 sensitivity, and 0.9961 specificity. In a longitudinal study, cryptic C Las infections that remained subclinical visually were detected within 2 wk postinfection compared with 1 to 32 mo for qPCR. When allowed to interrogate a diverse range of in vivo pathogens infecting an international citrus pathogen collection, canines only reacted to Liberibacter pathogens of citrus and not to other bacterial, viral, or spiroplasma pathogens. Canines trained to detect C Las-infected citrus also alerted on C Las-infected tobacco and periwinkle, C Las-bearing psyllid insect vectors, and C Las cocultured with other bacteria but at C Las titers below the level of molecular detection. All of these observations suggest that canines can detect C Las directly rather than only host volatiles produced by the infection. Detection in orchards and residential properties was real time, ∼2 s per tree. Spatiotemporal epidemic simulations demonstrated that control of pathogen prevalence was possible and economically sustainable when canine detection was followed by intervention (i.e., culling infected individuals), whereas current methods of molecular (qPCR) and visual detection failed to contribute to the suppression of an exponential trajectory of infection.

Emerging infectious diseases (EIDs) of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest-risk sites and that the optimal strategy differs depending on not only patterns of pathogen entry and spread but also the choice of detection method. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid ‘putting all your eggs in one basket’. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

Aphid vector species population composition is known to affect the spatial patterns of citrus tristeza virus (CTV) and the changes in these patterns over time. However, the biological processes that are associated with virus spread have not been well defined. The spatiotemporal dynamics of CTV were examined using data collected from research plots in the Dominican Republic and Costa Rica, where the brown citrus aphid (BCA), Toxoptera citricida, was the predominant species, and in Florida, where the BCA was absent and the melon aphid, Aphis gossypii, was the predominant vector. Data were analyzed using a spatiotemporal stochastic model for disease spread, and parameter values were evaluated using Markov chain Monte Carlo stochastic integration methods. Where the melon aphid was the dominant species, the model parameter likelihood values supported the hypothesis that the disease was spread through a combination of random background transmission (transmission originating from inoculum sources outside the plot) and a local interaction (transmission from inoculum sources within the plot) operating over short distances. Conversely, when BCA was present, results often suggested a local short-range transmission interaction that was not restricted to nearest-neighbor interactions and that the presence of background infection was not necessary to explain the observations.

We describe a series of operational questions posed during the state-wide response in California to the arrival of the invasive citrus disease Huanglongbing. The response is coordinated by an elected committee from the citrus industry and operates in collaboration with the California Department of Food and Agriculture, which gives it regulatory authority to enforce the removal of infected trees. The paper reviews how surveillance for disease and resource allocation between detection and delimitation have been addressed, based on epidemiological principles. In addition, we describe how epidemiological analyses have been used to support rule-making to enact costly but beneficial regulations and we highlight two recurring themes in the programme support work: (i) data are often insufficient for quantitative analyses of questions and (ii) modellers and decision-makers alike may be forced to accept the need to make decisions on the basis of simple or incomplete analyses that are subject to considerable uncertainty. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.