Search

Huanglongbing (HLB) is the most destructive citrus pathosystem worldwide. Previously known primarily from Asia and Africa, it was introduced into the Western Hemisphere in 2004. All infected commercial citrus industries continue to decline owing to inadequate current control methods. HLB increase and regional spatial spread, related to vector populations, are rapid compared with other arboreal pathosystems. Disease dynamics result from multiple simultaneous spatial processes, suggesting that psyllid vector transmission is a continuum from local area to very long distance. Evolutionarily, HLB appears to have originated as an insect endosymbiont that has moved into plants. Lack of exposure of citrus to the pathogen prior to approximately 100 years ago did not provide sufficient time for development of resistance. A prolonged incubation period and regional dispersal make eradication nonviable. Multiple asymptomatic infections per symptomatic tree, incomplete systemic distribution within trees, and prolonged incubation period make detection difficult and greatly complicate disease control.

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.