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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.

Diseases in humans, animals and plants remain an important challenge in our society. Effective control of invasive pathogens often requires coordinated concerted action of a large group of stakeholders. Both epidemiological and human behavioural factors influence the outcome of a disease control campaign. In mathematical models that are frequently used to guide such campaigns, human behaviour is often ill-represented, if at all. Existing models of human, animal and plant disease that do incorporate participation or compliance are often driven by pay-offs or direct observations of the disease state. It is however very well known that opinion is an important driving factor of human decision making. Here we consider the case study of Citrus Huanglongbing disease (HLB), which is an acute bacterial disease that threatens the sustainability of citrus production across the world. We show how by coupling an epidemiological model of this invasive disease with an opinion dynamics model we are able to answer the question: What makes or breaks the effectiveness of a disease control campaign? Frequent contact between stakeholders and advisors is shown to increase the probability of successful control. More surprisingly, we show that informing stakeholders about the effectiveness of control methods is of much greater importance than prematurely increasing their perceptions of the risk of infection. We discuss the overarching consequences of this finding and the effect on human as well as plant disease epidemics.

The global spread of pathogens poses an increasing threat to health, ecosystems and agriculture worldwide. As early detection of new incursions is key to effective control, new diagnostic tests that can detect pathogen presence shortly after initial infection hold great potential for detection of infection in individual hosts. However, these tests may be too expensive to be implemented at the sampling intensities required for early detection of a new epidemic at the population level. To evaluate the trade-off between earlier and/or more reliable detection and higher deployment costs, we need to consider the impacts of test performance, test cost and pathogen epidemiology. Regarding test performance, the period before new infections can be first detected and the probability of detecting them are of particular importance. We propose a generic framework that can be easily used to evaluate a variety of different detection methods and identify important characteristics of the pathogen and the detection method to consider when planning early detection surveillance. We demonstrate the application of our method using the plant pathogen Phytophthora ramorum in the UK, and find that visual inspec-tion for this pathogen is a more cost-effective strategy for early detection surveillance than an early detection diagnostic test. 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’.

Following the discovery of a quarantine plant pest or disease, delimitation is urgently conducted to define the boundaries of the infested area, typically through surveys that detect the presence or absence of the pest. Swift and accurate delimitation is crucial after a pest or pathogen enters a new region for containment or eradication. Delimiting an area that is too small allows the pest to spread uncontrollably, while delimited areas that are too large can lead to excessive economic costs, making eradication cost-prohibitive. Despite its significance, there is a lack of comprehensive reviews on delimiting strategies and their effectiveness in managing plant pests; many current practices are ad-hoc and not scientifically based. In this study, we used an individual-based model to simulate the spread of Huanglongbing (citrus greening), a priority EU pest, and evaluated three delimiting strategies across various host distribution landscapes. We found that an adaptive strategy was most effective, especially when tailored to the polycyclic nature of the pest. This underscored the need for specific delimiting approaches based on the epidemiological characteristics of the target pest.

Emerging pests and pathogens of plants are a major threat to natural and managed ecosystems worldwide. Whilst it is well accepted that surveillance activities are key to both the early detection of new incursions and the ability to identify pest-free areas, the performance of these activities must be evaluated to ensure they are fit for purpose. This requires consideration of the number of potential hosts inspected or tested as well as the epidemiology of the pathogen and the detection method used. In the case of plant pathogens, one particular concern is whether the visual inspection of plant hosts for signs of disease is able to detect the presence of these pathogens at low prevalences, given that it takes time for these symptoms to develop. One such pathogen is the ST53 strain of the vector-borne bacterial pathogen Xylella fastidiosa in olive hosts, which was first identified in southern Italy in 2013. Additionally, X. fastidiosa ST53 in olive has a rapid rate of spread, which could also have important implications for surveillance. In the current study, we evaluate how well visual surveillance would be expected to perform for this pathogen and investigate whether molecular testing of either tree hosts or insect vectors offer feasible alternatives. Our results identify the main constraints to each of these strategies and can be used to inform and improve both current and future surveillance activities.

Invasive plant pests and pathogens cause substantial environmental and economic damage. Visual inspection remains a central tenet of plant health surveys, but its sensitivity (probability of correctly identifying the presence of a pest) and specificity (probability of correctly identifying the absence of a pest) are not routinely quantified. As knowing sensitivity and specificity of visual inspection is critical for effective contingency planning and outbreak management, we address this deficiency using empirical data and statistical analyses. Twenty-three citizen scientist surveyors assessed up to 175 labelled oak trees for three symptoms of acute oak decline. The same trees were also assessed by an expert who has monitored these individual trees annually for over a decade. The sensitivity and specificity of surveyors was calculated using the expert data as the ‘gold-standard’ (i.e., assuming perfect sensitivity and specificity). The utility of an approach using Bayesian modelling to estimate the sensitivity and specificity of visual inspection in the absence of a rarely available ‘gold-standard’ dataset was then examined with simulated plant health survey datasets. There was large variation in sensitivity and specificity between surveyors and between different symptoms, although the sensitivity of detecting a symptom was positively related to the frequency of the symptom on a tree. By leveraging surveyor observations of two symptoms from a minimum of 80 trees on two sites, with reliable prior knowledge of sites with a higher (~0.6) and lower (~0.3) true disease prevalence we show that sensitivity and specificity can be estimated without ‘gold-standard’ data using Bayesian modelling. We highlight that sensitivity and specificity will depend on the symptoms of a pest or disease, the individual surveyor, and the survey protocol. This has consequences for how surveys are designed to detect and monitor outbreaks, as well as the interpretation of survey data that is used to inform outbreak management.

Reducing reliance on pesticides is an important global challenge. With increasing constraints on their use, in recent years there has been a declining trend in pesticide use for arable crops in the UK. But with increasing disease pressures and global demand for food, there is a greater need for effective measures of pest and disease control. These circumstances highlight the need for widespread adoption of sustainable alternative control measures. Integrated Pest Management (IPM) is one such solution, comprising a set of management strategies which focus on the long-term prevention, detection and control of pests, weeds and diseases. While many of these methods are acknowledged to offer effective control, their implementation has thus far been limited in practice. As a case study we considered Septoria tritici blotch (STB) ( Zymoseptoria tritici ), an economically important disease of wheat. We used epidemiological modelling techniques to investigate the potential of different IPM control strategies (crop residue burial, delayed sowing, variety mixtures and biocontrols). Combining existing data with a deterministic, compartmental infectious disease model of STB transmission, we simulated the implementation of an IPM regime into the STB disease system. We investigated the outcomes on disease prevalence and crop yield when comparing conventional and IPM control regimes. In a single field, for the individual implementation of IPM measures we found the starkest difference in potential yield outcomes between delayed sowing and biocontrols (greatest yields), and crop residue burial and variety mixtures (lowest yields). We also found that the collective use of IPM measures has the potential to offer individual growers comparable control to a standard fungicide regime. For a multi-field setting, representing a community of crop growers, a high proportion of growers using IPM can reduce the level of external infection incurred by the growers who maintain a fungicide regime.

The human genome contains approximately 800 G protein-coupled receptors (GPCRs), all characterized by a common 7-transmembrane domain architecture. Here, we show that PKD1, an 11-transmembrane protein with a noncanonical transient receptor potential (TRP) channel architecture, functions as a GPCR with unique biochemical properties. PKD1 acts as a WNT-activated receptor, directly coupling to heterotrimeric Gα subunits to inhibit cellular cAMP accumulation. While PKD1 contains both ligand-binding and G protein recruitment sites, PKD2, an associating TRP channel subunit, chaperones PKD1 to the plasma membrane to operate as a GPCR. This represents a striking departure from classical GPCR architecture and expands the functional repertoire of the TRP channel family. Given that mutations in either PKD1 or PKD2 are linked to autosomal dominant polycystic kidney disease, a multisystemic disorder marked by elevated cAMP levels, our results provide molecular insights into disease pathogenesis and highlight potential new therapeutic avenues for this debilitating and costly condition.

The spread of pathogens into new environments poses a considerable threat to human, animal, and plant health, and by extension, human and animal wellbeing, ecosystem function, and agricultural productivity, worldwide. Early detection through effective surveillance is a key strategy to reduce the risk of their establishment. Whilst it is well established that statistical and economic considerations are of vital importance when planning surveillance efforts, it is also important to consider epidemiological characteristics of the pathogen in question-including heterogeneities within the epidemiological system itself. One of the most pronounced realisations of this heterogeneity is seen in the case of vector-borne pathogens, which spread between 'hosts' and 'vectors'-with each group possessing distinct epidemiological characteristics. As a result, an important question when planning surveillance for emerging vector-borne pathogens is where to place sampling resources in order to detect the pathogen as early as possible. We answer this question by developing a statistical function which describes the probability distributions of the prevalences of infection at first detection in both hosts and vectors. We also show how this method can be adapted in order to maximise the probability of early detection of an emerging pathogen within imposed sample size and/or cost constraints, and demonstrate its application using two simple models of vector-borne citrus pathogens. Under the assumption of a linear cost function, we find that sampling costs are generally minimised when either hosts or vectors, but not both, are sampled.

Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens, which requires effective surveillance through the implementation of appropriate monitoring programmes. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. ‘Sentinel’ plants–alternative susceptible host species that display visible symptoms of infection more rapidly–could be introduced to at-risk populations and included in monitoring programmes to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.