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

Abstract

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.

Surveillance Testing for Rapid Detection of Outbreaks in Facilities

“…Although they suggest some general surveillance principles based on their observations, they do not provide detailed suggestions. Mastin et al [22] examine the spread of plant pathogens. Their paper is close in spirit to our work, in that they focus of optimization of surveillance nodes by building a stochastic optimization model, using Monte Carlo samples.…”

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

“…For instance, an analysis of the potential spread of the pathogen Xylella fastidiosa in France [ 65 ] identified the primary drivers of infection and incorporated them into a network simulation model, which provided a basis for comparing the performance of several surveillance strategies. Another study of the citrus disease huanglongbing (caused by Candidatus Liberibacter asiaticus) in the United States [ 66 ] evaluated the distribution of surveillance resources as an optimization problem. The researchers found that the sensitivity of available detection methods affected the optimal arrangement of survey sites.…”

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

“…However, rapid responses are not always possible. Pathogens can become established in a host population without the diseases they cause being identified, particularly when effective surveillance systems are not in place [12][13][14][15] or if there is a long incubation period before symptoms [16,17]. Attempts to eliminate or eradicate are also not always successful [18].…”