Optimising the surveillance and control of communicable diseases: the effects of population heterogeneity on transmission dynamics

点击翻译按钮获取中文摘要

Outbreaks of infectious diseases in plant populations are responsible for devastating economic and environmental consequences. In many cases, detecting infection in a population soon after a pathogen has first invaded is crucial to the success of control methods. The design of effective surveillance strategies for the early detection of invasive plant pathogens is consequently a key challenge for global biosecurity. Unfortunately, monitoring programmes are often confounded by periods of asymptomatic infection, during which plants do not display visible symptoms. However, there is the potential to use 'sentinel' plants which display symptoms more quickly as early warning beacons. In this project, I will apply mathematical and computational techniques to determine scenarios under which sentinel-based surveillance strategies will provide a cost-effective approach to early detection monitoring, considering real-life plant pathogens as case studies.