A Modular Bayesian Salmonella Source Attribution Model for Sparse Data.

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source-specific effects and the salmonella subtype-specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring.

A Bayesian statistical temporal-prevalence-concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch-specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within-batch and between-batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.

BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards.

BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github.

Evaluating Escherichia coli O157 control in finnish primary production.

Enterohemorrhagic Escherichia coli (EHEC) has become a threat in the modern cattle sector because of its adverse impact on human health. Systems have been developed to reduce the risk of EHEC infection associated with the beef production chain. In Finland, the risk management of EHEC is mainly targeted at primary production, which is controlled by a national program. The prevalence of E. coli O157 in slaughter animals and herds appears to have remained relatively low over the years (0.2 to 1.2% and 0.3 to 1.5%, respectively). The effectiveness of the Finnish EHEC control program (FECP) was analyzed with a Bayesian statistical model based on the results from 2006 through 2010. According to the model, the estimated true prevalence of EHEC in slaughter animals was at its highest in 2007 (95% credible interval [CI], 0.94 to 1.85% of animals), and the estimated true prevalence in herds was its highest in 2007 (95% CI, 1.28 to 2.55% of herds). However, the estimated probability of the FECP detecting an EHEC-positive slaughter animal or herd was 0.52 to 0.58% and 4.74 to 6.49%, respectively. The inability to detect EHEC-positive animals was partly due to animal-based random sampling, which ignores herd-level testing and therefore emphasizes the testing of slaughter animals from herds that send more animals to slaughter. Some slaughterhouses collected samples incorrectly as a consequence of an incorrectly implemented FECP. Farmers may also have questionable reasons for choosing to send animals to be slaughtered in small abattoirs, in which testing is less likely, to avoid suspicion of EHEC or other zoonotic infections.