Abstract1. Many alien taxa are known to cause socio-economic impacts by affecting the different constituents of human well-being (security; material and non-material assets; health; social, spiritual and cultural relations; freedom of choice and action).

Plant diseases threaten both food security and the botanical diversity of natural ecosystems. Substantial research effort is focused on pathogen detection and control, with detailed risk management available for many plant diseases. Risk can be assessed using analytical techniques that account for disease pressure both spatially and temporally. We suggest that such technical assessments of disease risk may not provide an adequate guide to the strategies undertaken by growers and government to manage plant disease. Instead, risk-management strategies need to account more fully for intuitive and normative responses that act to balance conflicting interests between stakeholder organizations concerned with plant diseases within the managed and natural environments. Modes of effective engagement between policy makers and stakeholders are explored in the paper, together with an assessment of such engagement in two case studies of contemporary non-indigenous diseases in one food and in one non-food sector. Finally, a model is proposed for greater integration of stakeholders in policy decisions.

Near real-time epidemic forecasting approaches are needed to respond to the increasing number of infectious disease outbreaks. In this paper, we retrospectively assess the performance of simple phenomenological models that incorporate early sub-exponential growth dynamics to generate short-term forecasts of the 2001 foot-and-mouth disease epidemic in the UK. For this purpose, we employed the generalized-growth model (GGM) for pre-peak predictions and the generalized-Richards model (GRM) for post-peak predictions. The epidemic exhibits a growth-decelerating pattern as the relative growth rate declines inversely with time. The uncertainty of the parameter estimates narrows down and becomes more precise using an increasing amount of data of the epidemic growth phase. Indeed, using only the first 10–15 days of the epidemic, the scaling of growth parameter (p) displays wide uncertainty with the confidence interval for p ranging from values ~ 0.5 to 1.0, indicating that less than 15 epidemic days of data are not sufficient to discriminate between sub-exponential (i.e., p < 1) and exponential growth dynamics (i.e., p = 1). By contrast, using 20, 25, or 30 days of epidemic data, it is possible to recover estimates of p around 0.6 and the confidence interval is substantially below the exponential growth regime. Local and national bans on the movement of livestock and a nationwide cull of infected and contiguous premises likely contributed to the decelerating trajectory of the epidemic. The GGM and GRM provided useful 10-day forecasts of the epidemic before and after the peak of the epidemic, respectively. Short-term forecasts improved as the model was calibrated with an increasing length of the epidemic growth phase. Phenomenological models incorporating generalized-growth dynamics are useful tools to generate short-term forecasts of epidemic growth in near real time, particularly in the context of limited epidemiological data as well as information about transmission mechanisms and the effects of control interventions.

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BackgroundAntimicrobial resistance is becoming a major threat to public health and there is much current activity to ameliorate that threat. However, the relative contributions that potential sources of antimicrobial resistant (AMR) bacteria represent are not well established. Over-prescription of antimicrobials by clinicians is one source of selection for AMR bacteria/genes, but antimicrobials are used in greater quantities in food production. These bacteria/genes can then reach humans via food, the environment, or other means. Scope and approachSummarised in this review are potential transmission routes of AMR bacteria/genes from agricultural production to human infections. The situation is complicated, and it is difficult to 2 compare studies because of different methodologies and definitions of resistance being used. Data and examples to illustrate each transmission route are provided where available. Key findings and conclusionsQuantitative data for defined organism/phenotype/gene combinations for exposure assessment are rare. Another problem is the identification of indistinguishable AMR bacteria in foods and human cases, which is invariably taken to show that food consumption is a source of infections. However, these data do not show the direction in which the flow of the organism/gene occurred nor do they rule out another source(s), and such data are scant. Case control studies could identify food exposures associated with particular organism/gene infections. The construction of models representing potential transmission pathways may help to reveal their relative contributions.However, the data may not be available to support these models. The lack of coherent data hampers the development of effective policy.