Surveillance to Inform Control of Emerging Plant Diseases: An Epidemiological Perspective

Abstract

The rise in emerging pathogens and strains has led to increased calls for more effective surveillance in plant health. We show how epidemiological insights about the dynamics of disease spread can improve the targeting of when and where to sample. We outline some relatively simple but powerful statistical approaches to inform surveillance and describe how they can be adapted to include epidemiological information. This enables us to address questions such as: Following the first report of an invading pathogen, what is the likely incidence of disease? If no cases of disease have been found, how certain can we be that the disease was not simply missed by chance? We illustrate the use of spatially explicit stochastic models to optimize targeting of surveillance and control resources. Finally, we discuss how modern detection and diagnostic technologies as well as information from passive surveillance networks (e.g., citizen science) can be integrated into surveillance strategies.

Sampling for disease absence—deriving informed monitoring from epidemic traits

“…When it comes to disease management, surveillance programs have two different objec-2 tives: establishing disease absence in host populations, or ensuring an early detection 3 of any disease outbreak (Parnell et al, 2017). Early detection is essential to disease 4 control mitigation, timely reactions generally being more successful and less detrimental 5 for the host population (Cunniffe et al, 2016).…”

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

“…We focus our attention in the current report on surveillance for early detection, rather than other surveillance aims such as prevalence estimation or spatial delimitation 36 (although our method can be adapted to a variety of surveillance aims, which will be explored in future work). This allows us to concentrate on early stage pathogen spread, when spread dynamics are more predictable and thus easier to model (although we appreciate that there may be uncertainty in parameter estimates in these stages 37 ).…”

Foundational and Translational Research Opportunities to Improve Plant Health

“…Sampling is a major problem. Recent work has shown that adaptive sampling can improve the efficiency of management of some diseases (Parry 2014; Parnell 2014, 2017); however, effective control requires detailed, intensive sampling of host populations which may not be showing symptoms (Cunniffe et al 2016). The distinction between severe, explosive invasions and minor outbreaks which require less expensive intervention is challenging (Thompson et al 2016).…”