No abstract

A Bayesian approach was developed by Hald et al.((1)) to estimate the contribution of different food sources to the burden of human salmonellosis in Denmark. This article describes the development of several modifications that can be used to adapt the model to different countries and pathogens. Our modified Hald model has several advantages over the original approach, which include the introduction of uncertainty in the estimates of source prevalence and an improved strategy for identifiability. We have applied our modified model to the two major food-borne zoonoses in New Zealand, namely, campylobacteriosis and salmonellosis. Major challenges were the data quality for salmonellosis and the inclusion of environmental sources of campylobacteriosis. We conclude that by modifying the Hald model we have improved its identifiability, made it more applicable to countries with less intensive surveillance, and feasible for other pathogens, in particular with respect to the inclusion of nonfood sources. The wider application and better understanding of this approach is of particular importance due to the value of the model for decision making and risk management.

No abstract

No abstract

A critical step in determining soil-to-atmosphere nitrous oxide (N 2 O) exchange using non-steady-state chambers is converting collected gas concentration versus time data to flux values using a flux calculation (FC) scheme. It is well documented that different FC schemes can produce different flux estimates for a given set of data. Available schemes differ in their theoretical basis, computational requirements, and performance in terms of both accuracy and precision. Nonlinear schemes tend to increase accuracy compared with linear regression but can also decrease precision. The chamber bias correction method can be used if soil physical data are available, but this introduces additional sources of error. Here, the essential theoretical and practical aspects of the most commonly used FC schemes are described as a basis for their selection and use. A gold standard approach for application and selection of FC schemes is presented, as well as alternative approaches based on availability of soil physical property data and intensity of sample collection during each chamber deployment. Additional criteria for scheme selection are provided in the form of an error analysis tool that quantifies performance with respect to both accuracy and precision based on chamber dimensions and sampling duration, soil properties, and analytical measurement precision. Example error analyses are presented for hypothetical conditions illustrating how such analysis can be used to guide FC scheme selection, estimate bias, and inform design of chambers and sampling regimes.