Biology subjects

Cunniffe, N. J.

Publications and source records attributed to Cunniffe, N. J..

16 recordsLinked to original sources

bioRxiv2024

Developing epidemiological preparedness for a plant disease invasion: modelling citrus huanglongbingin the European Union

Huanglongbing (HLB; citrus greening) is the most damaging disease of citrus worldwide. While citrus production in the USA and Brazil have been affected for decades, HLB has not been detected in the European Union (EU). However, psyllid vectors have already invaded and spread in Portugal and Spain, and in 2023 the psyllid species known to vector HLB in the Americas was first reported within the EU. We develop a landscape-scale, epidemiological model, accounting for heterogeneous citrus cultivation and vector dispersal, as well as climate and disease management. We use our model to predict HLB dynamics following introduction into high-density citrus areas in Spain, assessing detection and control strategies. Even with significant visual surveillance, we predict any epidemic will be widespread on first detection, with eradication unlikely. Introducing increased inspection and roguing following first detection, particularly if coupled with intensive insecticide use, could potentially sustain citrus production for some time. However, this may require chemical application rates that are not permissible in the EU. Disease management strategies targeting asymptomatic infection will likely lead to more successful outcomes. Our work highlights modelling as a key component of developing epidemiological preparedness for a pathogen invasion that is, at least somewhat, predictable in advance.

ecology↗

bioRxiv2024

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

The resources available for managing disease epidemics - whether in animals, plants or humans - are limited by a range of practical and financial constraints. This motivates study of where these resources should be allocated to maximise their impact. Optimal control has been widely explored for optimising management of epidemics. The most common approach assumes a deterministic, continuous model based on differential equations to approximate the epidemic dynamics. However, real systems are stochastic and so a range of outcomes are possible for any given epidemic situation and choice of control. The deterministic models are also known to be poor approximations in cases where the number of infected hosts is low - either globally or within a subset of the population - and these cases are highly relevant in the context of control. Hence, this work explores the effectiveness of disease management strategies derived using optimal control theory when applied to a more realistic, stochastic form of disease model. We demonstrate that solutions to the deterministic optimal control are not optimal in cases where the disease is eradicated or close to eradication. The range of potential outcomes in the stochastic models means that optimising the deterministic case will not reliably eradicate disease, even when it is possible. For effective eradication, the rate of control must be higher than optimal control as applied to the deterministic model would predict. Using Model Predictive Control, in which the optimisation is performed repeatedly as the system evolves to correct for deviations from the optimal control predictions, improves performance but does not fix the underlying issue and the level of control calculated at each repeated optimisation is still insufficient. To demonstrate this, we present several very simple heuristics to identify which sites to control which can outperform the strategies calculated by MPC when the control budget is sufficient for eradication to be possible. Our illustration uses examples based on stochastic simulation of the spatial spread of plant disease but similar issues would be expected in any deterministic model where infection is driven close to zero or targeted to remain below some critical value.

plant biology↗

bioRxiv2022

Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative

Increasing fungicide dose tends to lead to better short-term control of plant diseases. However, high doses select more rapidly for fungicide resistant strains, reducing long-term disease control. When resistance is qualitative and complete - i.e. resistant strains are unaffected by the chemical and resistance requires only a single genetic change - using the lowest possible dose ensuring sufficient control is well-known as the optimal resistance management strategy. However, partial resistance (where resistant strains are still partially suppressed by the fungicide) and quantitative resistance (where a range of resistant strains are present) remain ill-understood. Here we use a model of quantitative fungicide resistance (parameterised for the economically-important fungal pathogen Zymoseptoria tritici) which handles qualitative partial resistance as a special case. We show that - for both qualitative partial resistance and quantitative resistance - although low doses are optimal for resistance management, for some model parameterisations the benefit does not outweigh the improvement in control from increasing doses. Via a machine learning approach (a gradient-boosted trees model combined with Shapley values to facilitate interpretability) we interpret the effect of parameters controlling pathogen mutation and characterising the fungicide, in addition to the timescale of interest.

evolutionary biology↗

bioRxiv2022

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 in new locations, which requires effective surveillance through the implementation of appropriate monitoring programs. 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 programs 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.

plant biology↗

bioRxiv2021

It ain't what you do, it's what drives why you do it: how the basis of growers' decision making affects the effectiveness of disease control

While the spread of plant disease depends strongly on biological factors driving transmission, it also has a human dimension. Disease control depends on decisions made by individual growers, who are in turn influenced by a broad range of factors. Despite this, human behaviour has rarely been included in plant epidemic models. Considering Cassava Brown Streak Disease, we model how the perceived increase in profit due to disease management influences participation in clean seed systems (CSS). Our models are rooted in game theory, with growers making strategic decisions based on the expected profitability of different control strategies. We find that both the information used by growers to assess profitability and the perception of economic and epidemiological parameters influence long-term participation in the CSS. Over-estimation of infection risk leads to lower participation in the CSS, as growers perceive that paying for the CSS will be futile. Additionally, even though good disease management can be achieved through the implementation of CSS, and a scenario where all controllers use the CSS is achievable when growers base their decision on the average of their entire strategy, CBSD is rarely eliminated from the system. These results are robust to stochastic and spatial effects. Our work highlights the importance of including human behaviour in plant disease models, but also the significance of how that behaviour is included. 1 Author SummaryModels of plant disease epidemics rarely account for the behaviour of growers undertaking management decisions. However, such behaviour is likely to have a large impact on disease spread. Growers may choose to participate in a control scheme based on the perceived economic advantages, acting to maximise their own profit. Yet if many growers participate in a control scheme, their participation will lower the probability of others becoming infected and consequently disincentivise them from participating themselves. How these dynamics play out will alter the course of the epidemic. We incorporate these economic considerations into an epidemic model of Cassava Brown Streak Disease using two broad approaches, which vary in the amount of information provided to growers. We also consider the effect of grower misperception of economic and epidemiological parameters. Our work shows that both the inclusion of grower behaviour, and its means of inclusion, affect disease dynamics, and highlights the importance of including grower decision-making in plant epidemic models.

ecology↗

bioRxiv2021

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

Epidemics can particularly threaten certain sub-populations. For example, for 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 characterise 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.

ecology↗

bioRxiv2021

Epidemiological and ecological consequences of virus manipulation of host and vector in plant virus transmission

Many plant viruses are transmitted by insect vectors. Transmission can be described as persistent or non-persistent depending on rates of acquisition, retention, and inoculation of virus. Much experimental evidence has accumulated indicating vectors can prefer to settle and/or feed on infected versus noninfected host plants. For persistent transmission, vector preference can also be conditional, depending on the vectors own infection status. Since viruses can alter host plant quality as a resource for feeding, infection potentially also affects vector population dynamics. Here we use mathematical modelling to develop a theoretical framework addressing the effects of vector preferences for landing, settling and feeding - as well as potential effects of infection on vector population density - on plant virus epidemics. We explore the consequences of preferences that depend on the host (infected or healthy) and vector (viruliferous or nonviruliferous) phenotypes, and how this is affected by the form of transmission, persistent or non-persistent. We show how different components of vector preference have characteristic effects on both the basic reproduction number and the final incidence of disease. We also show how vector preference can induce bistability, in which the virus is able to persist even when it cannot invade from very low densities. Feedbacks between plant infection status, vector population dynamics and virus transmission potentially lead to very complex dynamics, including sustained oscillations. Our work is supported by an interactive interface https://plantdiseasevectorpreference.herokuapp.com/. Our model reiterates the importance of coupling virus infection to vector behaviour, life history and population dynamics to fully understand plant virus epidemics. AUTHOR SUMMARYPlant virus diseases - which cause devastating epidemics in plant populations worldwide - are most often transmitted by insect vectors. Recent experimental evidence indicates how vectors do not choose between plants at random, but instead can be affected by whether plants are infected (or not). Virus infection can cause plants to "smell" different, because they produce different combinations of volatile chemicals, or "taste" different, due to chemical changes in infected tissues. Vector reproduction rates can also be affected when colonising infected versus uninfected plants. Potential effects on epidemic spread through a population of plants are not yet entirely understood. There are also interactions with the mode of virus transmission. Some viruses can be transmitted after only a brief probe by a vector, whereas others are only picked up after an extended feed on an infected plant. Furthermore there are differences in how long vectors remain able to transmit the virus. This ranges from a matter of minutes, right up to the entire lifetime of the insect, depending on the plant-virus-vector combination under consideration. Here we use mathematical modelling to synthesise all this complexity into a coherent theoretical framework. We illustrate our model via an online interface https://plantdiseasevectorpreference.herokuapp.com/.

ecology↗

bioRxiv2020

Small scale variability in soil moisture drives infection of vulnerable juniper populations by invasive forest pathogen

The oomycete plant pathogen, Phytophthora austrocedri, is an aggressive killer of cypress trees causing severe mortality of Chilean cedar (Austrocedrus chilensis) in Argentina since the 1940s and now common juniper (Juniperus communis s.l.) in the UK. Rapid mortality of key UK juniper populations was first observed in the early 2000s; the causal agent of mortality was confirmed as P. austrocedri in 2012 and the pathogen has now been widely detected - but is not ubiquitous - in juniper populations across Scotland and England. Although juniper has a broad distribution across the northern hemisphere, the UK incidence of P. austrocedri remains the only confirmed infection of juniper populations globally. Juniper is an important species for biodiversity, so it is imperative to understand the abiotic and biotic drivers of emergent P. austrocedri infection to inform detection, containment and conservation strategies to manage juniper populations across the full extent of its range. As management of UK juniper populations is primarily conducted at a local level, we investigated field scale drivers of disease - in three, geographically separate populations with different infection histories. Variation in the proportion of juniper showing symptoms - discoloured or dead foliage - was measured using stratified sampling across along key environmental gradients within each 100-hectare population, including juniper density identified from aerial imagery. Potential predictors of infection included altitude, slope, distance to nearest watercourse, soil moisture (mean percentage volumetric water content), area of red deer browsing damage and area of commonly associated vascular plant species. We assessed support in the data for alternative models explaining the spatial distribution of P. austrocedri symptoms using full subset covariate selection and Deviance Information Criteria (DIC). Despite differences in environmental gradients and infection histories between populations, area of juniper symptomatic for P. austrocedri increased with waterlogging, increasing with soil moisture in sites where soils had higher peat or clay contents, and decreasing with proximity to watercourses where sites had shallower, sandier soils. These results are consistent with key drivers identified at both local and landscape scale in Chilean cedar. Our approach enables identification of site-specific disease management strategies including prioritisation of inspections in microsites with high soil moisture and promoting conservation measures such as creation of sites for natural regeneration in drier microsites to minimise pathogen spread and maximise the resilience of existing juniper populations.

ecology↗

bioRxiv2019

Spatiotemporal dynamics and modelling support the case for area-wide management of citrus greasy spot in a Brazilian smallholder farming region

Citrus greasy spot (CGS), caused by Zasmidium citri, induces premature defoliation and yield loss in Citrus spp. CGS epidemiology is well understood in areas of high humidity such as Florida (USA), but remains unaddressed in Brazil, despite differing climatic conditions and disease management practices. We characterize the spatiotemporal dynamics of CGS in the Reconcavo of Bahia, Brazil, focusing on four hierarchical levels (quadrant, plant, grove and region). A survey conducted in 19 municipalities showed that disease is found throughout the entire region with a prevalence (i.e. proportion of affected sampling units) of 100% in groves and plants, and never lower than 70% on leaves. Index of dispersion (D) values suggest the spatial pattern of symptomatic units lies somewhere between random and regular. This was confirmed by the parameters of the binary power law for plants and their quadrants (log(A)<0 and b<1). Variability in disease severity at different plant heights (0.7 m, 1.3 m and 2.0 m) was tested, but no consistent differences were observed. We introduce a simple compartmental model synthesising the epidemiology of the disease, in order to motivate and guide further research. The data we have collected allow such a model to be parameterised, albeit with some ambiguity over the proportion of new infections that result from inoculum produced within the grove vs. external sources of infection. By extending our model to include two populations of growers - those who control and those who do not - coupled by the spread of airborne inoculum, we investigate likely performance of the type of cultural controls that would be accessible to citrus growers in Northeastern Brazil. Our model shows that control via removal of the key source of inoculum - i.e. fallen leaves - can be very effective. However, successful control is likely to require area-wide strategies, in which a large proportion of growers actively manage disease.

ecology↗

bioRxiv2019

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

Emerging infectious diseases 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. We use the example of the economically important arboreal disease huanglongbing to demonstrate how our approach outperforms conventional methods of targeted surveillance.

ecology↗

bioRxiv2019

An eco-physiological model coupling plant growth and aphid population dynamics

Empirical studies have shown that particular irrigation/fertilization regimes can control pest populations in agroecosystems. This appears to promise that the ecological concept of bottom-up control can be applied to pest management. However, a conceptual framework is necessary to develop a mechanistic basis for empirical evidence. Here we couple a mechanistic plant growth model with a pest population model. We demonstrate its utility by applying it to the peach - green aphid system. Aphids are herbivores which feed on the plant phloem, deplete plants resources and (potentially) transmit viral diseases. The model reproduces system properties observed in field studies and shows under which conditions the diametrically-opposed plant vigour and plant stress hypotheses find support. We show that the effect of fertilization/irrigation on the pest population cannot be simply reduced as positive or negative. In fact, the magnitude and direction of any effect depends on the precise level of fertilization/irrigation and on the date of observation. We show that a new synthesis of experimental data can emerge by embedding a mechanistic plant growth model, widely studied in agronomy, in a consumer-resource modelling framework, widely studied in ecology. The future challenge is to use this insight to inform practical decision making by farmers and growers.

plant biology↗

bioRxiv2019

Ongoing surveillance protects tanoak whilst conserving biodiversity: applying optimal control theory to a spatial simulation model of sudden oak death

The sudden oak death epidemic in California is spreading uncontrollably. Large-scale eradication has been impossible for some time. However, small-scale disease management could still slow disease spread. Although empirical evidence suggests localised control could potentially be successful, mathematical models have said little about such management. By approximating a detailed, spatially-explicit simulation model of sudden oak death with a simpler, mathematically-tractable model, we demonstrate how optimal control theory can be used to unambiguously characterise effective time-dependent disease management strategies. We focus on protection of tanoak, a tree species which is culturally and ecologically important, but also highly susceptible to sudden oak death. We identify management strategies to protect tanoak in a newly-invaded forest stand, whilst also conserving biodiversity. We find that thinning of bay laurel is essential early in the epidemic. We apply model predictive control, a feedback strategy in which both the approximating model and the control are repeatedly updated as the epidemic progresses. Adapting optimal control strategies in this way is vital for effective disease management. This feedback strategy is robust to parameter uncertainty, limiting loss of tanoak in the worst-case scenarios. However, the methodology requires ongoing surveillance to re-optimise the approximating model. This introduces an optimal level of surveillance to balance the high costs of intensive surveys against improved management resulting from better estimates of disease progress. Our study shows how detailed simulation models can be coupled with optimal control theory and model predictive control to find effective control strategies for sudden oak death. We demonstrate that control strategies for sudden oak death must depend on local management goals, and that success relies on adaptive strategies that are updated via ongoing disease surveillance. The broad framework allowing the use of optimal control theory on complex simulation models is applicable to a wide range of systems.

plant biology↗

bioRxiv2019

Co-infections by non-interacting pathogens are not independent and require new tests of interaction

If pathogen species, strains or clones do not interact, intuition suggests the proportion of co-infected hosts should be the product of the individual prevalences. Independence consequently underpins the wide range of methods for detecting pathogen interactions from cross-sectional survey data. However, the very simplest of epidemiological models challenge the underlying assumption of statistical independence. Even if pathogens do not interact, death of co-infected hosts causes net prevalences of individual pathogens to decrease simultaneously. The induced positive correlation between prevalences means the proportion of co-infected hosts is expected to be higher than multiplication would suggest. By modeling the dynamics of multiple non-interacting pathogens, we develop a pair of novel tests of interaction that properly account for non-independence. Our tests allow us to reinterpret data from previous studies including pathogens of humans, plants, and animals. Our work demonstrates how methods to identify interactions between pathogens can be updated using simple epidemic models.

epidemiology↗

bioRxiv2019

When does spatial diversification usefully maximise the durability of crop disease resistance?

1Maximising the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialised. The factors that interact with and determine the magnitude of this spatial effect are not currently well understood however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant cultivars, and the way in which this effect depends on the parameters governing the pathosystem of interest. Our multi-season semi-discrete epidemiological model tracks spatial spread of wild-type and resistance breaking pathogen strains, and incorporates a localised reservoir of inoculum, as well as the effects of within and between field transmission. The pathogen dispersal characteristics, any fitness cost(s) of the resistance breaking trait, the efficacy of host resistance, and the length of the timeframe of interest, all influence the strength of the spatial diversification effect. These interactions, which are often complex and non-linear in nature, produce substantial variation in the predicted yield gain from the use of a spatial diversification strategy. This in turn allows us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modelling and pathosystem specific evaluation. These results highlight the importance of studying the effect of genetics on landscape scale spatial dynamics within host-pathogen disease systems.

epidemiology↗

bioRxiv2018

Applying optimal control theory to complex epidemiological models to inform real-world disease management

Mathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially-explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach - optimal control theory - allows the very best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of plants, animals and humans.

epidemiology↗

bioRxiv2017

Using epidemiological principles to explain fungicide resistance management strategies: why do mixtures outperform alternations?

O_LIWhether fungicide resistance management is optimised by spraying chemicals with different modes of action as a mixture (i.e. simultaneously) or in alternation (i.e. sequentially) has been studied by experimenters and modellers for decades, largely inconclusively.\nC_LIO_LIWe use previously-parameterised and validated mathematical models of wheat septoria leaf blotch and grapevine powdery mildew to test which strategy provides better resistance management, using the total yield before fungicide-resistance causes disease control to become economically-ineffective (\"lifetime yield\") to measure effectiveness.\nC_LIO_LILifetime yield is optimised by spraying as much low-risk fungicide as is permitted, combined with slightly more high-risk fungicide than needed for acceptable initial disease control, applying these fungicides as a mixture. This is invariant to model parameterisation and structure, as well as the pathosystem in question. However if comparison focuses on other metrics, for example lifetime yield at full label dose, either mixtures or alternation can be optimal.\nC_LIO_LIOur work shows how epidemiological principles can explain the evolution of fungicide resistance, and highlights a theoretical framework to address the question of whether mixtures or alternation provide better resistance management. Our work also demonstrates that precisely how spray strategies are compared must be given extremely careful consideration.\nC_LI

epidemiology↗