Optimising risk-based surveillance for early detection of invasive plant pathogens

Performance of Outbreak Management Plans for Emerging Plant Diseases: The Case of Almond Leaf Scorch Caused by Xylella fastidiosa in Mainland Spain

Outbreak response to quarantine pathogens and pests in the European Union (EU) is regulated by the EU Plant Health Law, but the performance of outbreak management plans in terms of their effectiveness and efficiency has been quantified only to a limited extent. As a case study, the disease dynamics of almond leaf scorch, caused by Xylella fastidiosa, in the affected area of Alicante, Spain, were approximated using an individual-based spatial epidemiological model. The emergence of this outbreak was dated based on phylogenetic studies, and official surveys were used to delimit the current extent of the disease. Different survey strategies and disease control measures were compared to determine their effectiveness and efficiency for outbreak management in relation to a baseline scenario without interventions. One-step and two-step survey approaches were compared with different confidence levels, buffer zone sizes, and eradication radii, including those set by the EU legislation for X. fastidiosa. The effect of disease control interventions was also considered by decreasing the transmission rate in the buffer zone. All outbreak management plans reduced the number of infected trees (effectiveness), but large differences were observed in the number of susceptible trees not eradicated (efficiency). The two-step survey approach, high confidence level, and the reduction in the transmission rate increased the efficiency. Only the outbreak management plans with the two-step survey approach removed infected trees completely, but they required greater survey efforts. Although control measures reduced disease spread, surveillance was the key factor in the effectiveness and efficiency of the outbreak management plans. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Citizen science is a vital partnership for invasive alien species management and research

Invasive alien species (IAS) adversely impact biodiversity, ecosystem functions, and socio-economics. Citizen science can be an effective tool for IAS surveillance, management, and research, providing large datasets over wide spatial extents and long time periods, with public participants generating knowledge that supports action. We demonstrate how citizen science has contributed knowledge across the biological invasion process, especially for early detection and distribution mapping. However, we recommend that citizen science could be used more for assessing impacts and evaluating the success of IAS management. Citizen science does have limitations, and we explore solutions to two key challenges: ensuring data accuracy and dealing with uneven spatial coverage of potential recorders (which limits the dataset's "fit for purpose"). Greater co-development of citizen science with public stakeholders will help us better realize its potential across the biological invasion process and across ecosystems globally while meeting the needs of participants, local communities, scientists, and decision-makers.

Climate change impacts on plant pathogens, food security and paths forward

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

Bayesian inference for spatio-temporal stochastic transmission of plant disease in the presence of roguing: A case study to characterise the dispersal of Flavescence dorée

Estimating the distance at which pathogens disperse from one season to the next is crucial for designing efficient control strategies for invasive plant pathogens and a major milestone in the reduction of pesticide use in agriculture. However, we still lack such estimates for many diseases, especially for insect-vectored pathogens, such as Flavescence dorée (FD). FD is a quarantine disease threatening European vineyards. Its management is based on mandatory insecticide treatments and the removal of infected plants identified during annual surveys. This paper introduces a general statistical framework to model the epidemiological dynamics of FD in a mechanistic manner that can take into account missing hosts in surveyed fields (resulting from infected plant removals). We parameterized the model using Markov chain Monte Carlo (MCMC) and data augmentation from surveillance data gathered in Bordeaux vineyards. The data mainly consist of two snapshot maps of the infectious status of all the plants in three adjacent fields during two consecutive years. We demonstrate that heavy-tailed dispersal kernels best fit the spread of FD and that on average, 50% (resp. 80%) of new infection occurs within 10.5 m (resp. 22.2 m) of the source plant. These values are in agreement with estimates of the flying capacity of Scaphoideus titanus, the leafhopper vector of FD, reported in the literature using mark-capture techniques. Simulations of simple removal scenarios using the fitted model suggest that cryptic infection hampered FD management. Future efforts should explore whether strategies relying on reactive host removal can improve FD management.

What Can Be Learned by a Synoptic Review of Plant Disease Epidemics and Outbreaks Published in 2021?

A synoptic review of plant disease epidemics and outbreaks was made using two complementary approaches. The first approach involved reviewing scientific literature published in 2021, in which quantitative data related to new plant disease epidemics or outbreaks were obtained via surveys or similar methodologies. The second approach involved retrieving new records added in 2021 to the CABI Distribution Database, which contains over a million global geographic records of organisms from over 50,000 species. The literature review retrieved 186 articles, describing studies in 62 categories (pathogen species/species complexes) across more than 40 host species on six continents. Pathogen species with more than five articles were Bursaphelenchus xylophilus, 'Candidatus Liberibacter asiaticus', cassava mosaic viruses, citrus tristeza virus, Erwinia amylovora, Fusarium spp. complexes, F. oxysporum f. sp. cubense, Magnaporthe oryzae, maize lethal necrosis co-infecting viruses, Meloidogyne spp. complexes, Pseudomonas syringae pvs., Puccinia striiformis f. sp. tritici, Xylella fastidiosa, and Zymoseptoria tritici. Automated searches of the CABI Distribution Database identified 617 distribution records new in 2021 of 283 plant pathogens. A further manual review of these records confirmed 15 pathogens reported in new locations: apple hammerhead viroid, apple rubbery wood viruses, Aphelenchoides besseyi, Biscogniauxia mediterranea, 'Ca. Liberibacter asiaticus', citrus tristeza virus, Colletotrichum siamense, cucurbit chlorotic yellows virus, Erwinia rhapontici, Erysiphe corylacearum, F. oxysporum f. sp. cubense Tropical race 4, Globodera rostochiensis, Nothophoma quercina, potato spindle tuber viroid, and tomato brown rugose fruit virus. Of these, four pathogens had at least 25% of all records reported in 2021. We assessed two of these pathogens-tomato brown rugose fruit virus and cucurbit chlorotic yellows virus-to be actively emerging in/spreading to new locations. Although three important pathogens-'Ca. Liberibacter asiaticus', citrus tristeza virus, and F. oxysporum f. sp. cubense-were represented in the results of both our literature review and our interrogation of the CABI Distribution Database, in general, our dual approaches revealed distinct sets of plant disease outbreaks and new records, with little overlap. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Assessing the effectiveness of environmental sampling for surveillance of foot-and-mouth disease virus in a cattle herd

The survival of foot-and-mouth disease virus (FMDV) in the environment provides an opportunity for indirect transmission, both within and between farms. However it also presents the possibility of surveillance and detection via environmental sampling. This study assesses the effectiveness of environmental sampling strategies in the event of an outbreak, using a previous model for transmission of FMDV in a cattle herd that had been parameterized using data from transmission experiments and outbreaks. We show that environmental sampling can be an effective means of detecting FMDV in a herd, but it requires multiple samples to be taken on multiple occasions. In addition, environmental sampling can potentially detect FMDV in a herd more quickly than clinical inspection. For example, taking 10 samples every 3 days results in a mean time to detection of 6 days, which is lower than the mean time to detection estimated for the 2001 UK epidemic (8 days). We also show how environmental sampling could be used in a herd considered to be at risk as an alternative to pre-emptive culling. However, because of the time taken for virus to accumulate at the start of an outbreak, a reasonable level of confidence (> 99%) that an at-risk herd is indeed free from infection is unlikely to be achieved in less than 1 week.

Using 'sentinel' plants to improve early detection of invasive plant pathogens

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.

A framework for the design, implementation, and evaluation of output-based surveillance systems against zoonotic threats

Output-based standards set a prescribed target to be achieved by a surveillance system, but they leave the selection of surveillance parameters, such as test type and population to be sampled, to the responsible party in the surveillance area. This allows proportionate legislative surveillance specifications to be imposed over a range of unique geographies. This flexibility makes output-based standards useful in the context of zoonotic threat surveillance, particularly where animal pathogens act as risk indicators for human health or where multiple surveillance streams cover human, animal, and food safety sectors. Yet, these systems are also heavily reliant on the appropriate choice of surveillance options to fit the disease context and the constraints of the organization implementing the surveillance system. Here we describe a framework to assist with designing, implementing, and evaluating output-based surveillance systems showing the effectiveness of a diverse range of activities through a case study example. Despite not all activities being relevant to practitioners in every context, this framework aims to provide a useful toolbox to encourage holistic and stakeholder-focused approaches to the establishment and maintenance of productive output-based surveillance systems.

Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen, where nodes can be infected more than once, has been less well characterized. Here, we analyze detailed records of the transportation of cattle among farms in Turkey to characterize the global and local attributes of the directed—weighted shipments network between 2007-2012. We then study the correlations between network properties and the likelihood of infection with, or exposure to, foot-and-mouth disease (FMD) over the same time period using recorded outbreaks. The shipments network shows a complex combination of features (local and global) that have not been previously reported in other networks of shipments; i.e. small-worldness, scale-freeness, modular structure, among others. We find that nodes that were either infected or at high risk of infection with FMD (within one link from an infected farm) had disproportionately higher degree, were more central (eigenvector centrality and coreness), and were more likely to be net recipients of shipments compared to those that were always more than 2 links away from an infected farm. High in-degree (i.e. many shipments received) was the best univariate predictor of infection. Low in-coreness (i.e. peripheral nodes) was the best univariate predictor of nodes always more than 2 links away from an infected farm. These results are robust across the three different serotypes of FMD observed in Turkey and during periods of low-endemic prevalence and high-prevalence outbreaks.

Contact network epidemiology has been extensively used in the context of infectious diseases, primarily focusing on epidemic diseases. In this paper we use detailed recorded data about cattle exchange between farms in Turkey from 2007 to 2012, to build, analyze and characterize the directed-weighted complex network of shipments of cattle. Additionally, using outbreaks data about recorded cases of foot-and-mouth disease (FMD) in Turkey, we assess the correlation between the “farm’s” position in the network (importance) and the risk of being infected with FMD, which has been endemic in Turkey for a long time. We find some network measures that are more likely to identify high-risk and low-risk farms (in-degree and in-coreness, respectively) when proposing strategies for surveillance or containment of an infectious disease.

Epidemiologically-based strategies for the detection of emerging plant pathogens

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.

A direct comparison of methods for assessing the threat from emerging infectious diseases in seasonally varying environments

Seasonal variations in environmental conditions lead to changing infectious disease epidemic risks at different times of year. The probability that early cases initiate a major epidemic depends on the season in which the pathogen enters the population. The instantaneous epidemic risk (IER) can be tracked. This quantity is straightforward to calculate, and corresponds to the probability of a major epidemic starting from a single case introduced at time t=t₀, assuming that environmental conditions remain identical from that time onwards (i.e. for all t≥t₀). However, the threat when a pathogen enters the population in fact depends on changes in environmental conditions occurring within the timescale of the initial phase of the outbreak. For that reason, we compare the IER with a different metric: the case epidemic risk (CER). The CER corresponds to the probability of a major epidemic starting from a single case entering the population at time t=t₀, accounting for changes in environmental conditions after that time. We show how the IER and CER can be calculated using different epidemiological models (the stochastic Susceptible-Infectious-Removed model and a stochastic host-vector model that is parameterised using temperature data for Miami) in which transmission parameters vary temporally. While the IER is always easy to calculate numerically, the adaptable method we provide for calculating the CER for the host-vector model can also be applied easily and solved using widely available software tools. In line with previous research, we demonstrate that if a pathogen is likely to either invade the population or fade out on a fast timescale compared to changes in environmental conditions, the IER closely matches the CER. However, if this is not the case, the IER and the CER can be significantly different, and so the CER should be used. This demonstrates the need to consider future changes in environmental conditions carefully when assessing the risk posed by emerging pathogens.

Risk-based serological survey of bluetongue and the first evidence of bluetongue virus serotype 26 circulation in Tunisia

BACKGROUND

Bluetongue (BT), a vector-borne disease of wild and domestic ruminants, is responsible for severe economic losses in flocks. To reduce this impact, a surveillance and control plan was implemented in Tunisia. However, the epidemiological situation of BT remains incompletely understood, especially for the circulating serotypes.

OBJECTIVE

The aim of this survey was to determine the seroprevalence, to identify the circulating serotypes and to identify the associated risk factors for bluetongue virus (BTV) circulation in Tunisia using risk-based sampling (RBS).

METHODS

A total of 3314 blood samples were randomly collected from 67 sectors using risk-based sampling and screened by competitive enzyme-linked immunosorbent assays (c-ELISAs). Out of the 1330 positive samples, 200 samples were analysed by serum neutralization test (SNT) to identify circulating BTV serotypes.

RESULTS

Of 3314 sera, 1330 were c-ELISA-positive (40.1%) for antibodies against the BTV structural protein VP7. The result of SNT showed the presence of BTV-1, BTV-2, BTV-3, BTV-4 and, for the first time in Tunisia, BTV-26. The logistic regression model revealed that older animals had nearly two times the odds of being infected with BTV compared to younger animals. Flocks with a history of BT were almost 1.5 times more likely to be at risk for contracting BTV infection. The flock size, housing indoors and intensive production system were significant protective factors.

CONCLUSIONS

High seroprevalence of BTV among sheep was highlighted in Tunisia. The neutralization test showed the presence of the following BTV serotypes: BTV-1, BTV-2, BTV-3, BTV-4 and, for the first time in Tunisia, BTV-26. Age, production system and flock size were important variables associated with BTV infection in sheep. This finding is crucial, as it will allow the adjustment of the BT control programme in Tunisia.

Optimal strategies to protect a sub-population at risk due to an established epidemic

Epidemics can particularly threaten certain sub-populations. For example, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the elderly are often preferentially protected. For diseases of plants and animals, certain sub-populations can drive mitigation because they are intrinsically more valuable for ecological, economic, socio-cultural or political reasons. Here, we use optimal control theory to identify strategies to optimally protect a ‘high-value’ sub-population when there is a limited budget and epidemiological uncertainty. We use protection of the Redwood National Park in California in the face of the large ongoing state-wide epidemic of sudden oak death (caused by Phytophthora ramorum) as a case study. We concentrate on whether control should be focused entirely within the National Park itself, or whether treatment of the growing epidemic in the surrounding ‘buffer region’ can instead be more profitable. We find that, depending on rates of infection and the size of the ongoing epidemic, focusing control on the high-value region is often optimal. However, priority should sometimes switch from the buffer region to the high-value region only as the local outbreak grows. We characterize how the timing of any switch depends on epidemiological and logistic parameters, and test robustness to systematic misspecification of these factors due to imperfect prior knowledge.

Tracking the outbreak: an optimized sequential adaptive strategy for Xylella fastidiosa delimiting surveys

The EU plant health legislation enforces the implementation of intensive surveillance programs for quarantine pests. After an outbreak, surveys are implemented to delimit the extent of the infested zone and to manage disease control. Surveillance in agricultural and natural environments can be enhanced by increasing the survey efforts. Budget constraints often limit inspection and sampling intensities, thus making it necessary to adapt and optimize surveillance strategies. A sequential adaptive delimiting survey involving a three-phase and a two-phase design with increasing spatial resolution was developed and implemented for the Xylella fastidiosa demarcated area in Alicante, Spain. Inspection and sampling intensities were optimized using simulation-based methods. Sampling intensity thresholds were evaluated by quantifying their effect on the estimation of X. fastidiosa incidence. This strategy made it possible to sequence inspection and sampling taking into account increasing spatial resolutions, and to adapt the inspection and sampling intensities according to the information obtained in the previous, coarser, spatial resolution. The proposed strategy was able to efficiently delimit the extent of Xylella fastidiosa, while improving on the efficiency and maintaining the efficacy of the official survey campaign. From a methodological perspective, our approach provides new insights into alternative delimiting designs and new reference sampling intensity values.

Optimal invasive species surveillance in the real world: practical advances from research

When alien species make incursions into novel environments, early detection through surveillance is critical to minimizing their impacts and preserving the possibility of timely eradication. However, incipient populations can be difficult to detect, and usually, there are limited resources for surveillance or other response activities. Modern optimization techniques enable surveillance planning that accounts for the biology and expected behavior of an invasive species while exploring multiple scenarios to identify the most cost-effective options. Nevertheless, most optimization models omit some real-world limitations faced by practitioners during multi-day surveillance campaigns, such as daily working time constraints, the time and cost to access survey sites and personnel work schedules. Consequently, surveillance managers must rely on their own judgments to handle these logistical details, and default to their experience during implementation. This is sensible, but their decisions may fail to address all relevant factors and may not be cost-effective. A better planning strategy is to determine optimal routing to survey sites while accounting for common daily logistical constraints. Adding site access and other logistical constraints imposes restrictions on the scope and extent of the surveillance effort, yielding costlier but more realistic expectations of the surveillance outcomes than in a theoretical planning case.