Optimising and Communicating Options for the Control of Invasive Plant Disease When There Is Epidemiological Uncertainty

Abstract

Although local eradication is routinely attempted following introduction of disease into a new region, failure is commonplace. Epidemiological principles governing the design of successful control are not well-understood. We analyse factors underlying the effectiveness of reactive eradication of localised outbreaks of invading plant disease, using citrus canker in Florida as a case study, although our results are largely generic, and apply to other plant pathogens (as we show via our second case study, citrus greening). We demonstrate how to optimise control via removal of hosts surrounding detected infection (i.e. localised culling) using a spatially-explicit, stochastic epidemiological model. We show how to define optimal culling strategies that take account of stochasticity in disease spread, and how the effectiveness of disease control depends on epidemiological parameters determining pathogen infectivity, symptom emergence and spread, the initial level of infection, and the logistics and implementation of detection and control. We also consider how optimal culling strategies are conditioned on the levels of risk acceptance/aversion of decision makers, and show how to extend the analyses to account for potential larger-scale impacts of a small-scale outbreak. Control of local outbreaks by culling can be very effective, particularly when started quickly, but the optimum strategy and its performance are strongly dependent on epidemiological parameters (particularly those controlling dispersal and the extent of any cryptic infection, i.e. infectious hosts prior to symptoms), the logistics of detection and control, and the level of local and global risk that is deemed to be acceptable. A version of the model we developed to illustrate our methodology and results to an audience of stakeholders, including policy makers, regulators and growers, is available online as an interactive, user-friendly interface at http://www.webidemics.com/. This version of our model allows the complex epidemiological principles that underlie our results to be communicated to a non-specialist audience.

Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California

“…Nevertheless, optimal scenarios can be derived for the median response, for example, the treatment radius that minimizes the impact of disease. Although optimal control radii have been derived using models at small spatial scales (7,33), this is the first demonstration, to our knowledge, that the idea extends to landscape-scale control of plant disease within a fixed budget. Impact is assessed by totaling the area lost from disease and from removal of healthy trees around infected sites.…”

“…Impact is assessed by totaling the area lost from disease and from removal of healthy trees around infected sites. However, the metric could readily be extended to a range of objective functions with different weightings for the individual components depending on perceived costs and benefits by different stakeholders (7,34). Treatment radii can also be adjusted to allow for different degrees of risk aversion (35), for example, selecting the radius that corresponds to the 5th percentile (high risk aversion) through to the 95th percentile (low risk aversion) (compare Fig.…”

“…Early detection and timely removal of affected trees from a small number of newly infected sites soon after initial introduction(s) can prevent epidemics (7). Routine detection and control of emerging pathogens of woodland trees, however, are prone to significant logistical and epidemiological constraints.…”

Trade-off between disease resistance and crop yield: a landscape-scale mathematical modelling perspective

“…In this paper, we model the yield in the presence of uncertainty as the average of the yield when the outbreak epidemic does or does not occur. However, farmers and policy-makers tend to prefer control strategies that minimize the probability of losses rather than those that maximize the probability of gains [36,37]. Our model could be combined with game theoretic approaches to analyze these outcomes.…”

Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery

“…The model is spatially explicit in that we keep track of the spatial coordinates of each individual host unit and account for distance-dependent transmission processes. We use an SIR model formulation [17,18] whereby a host can be either susceptible (S), infected (I ) or removed (R). The rate of transition of individual susceptible host S to infected I is a function of the distance of that host to other hosts that are already in the I state:…”