Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty

Modeling strain variability in Campylobacter jejuni thermal inactivation by quantifying the number of strains required

There is a limited understanding of the survival responses of Campylobacter jejuni during thermal processing, which must be investigated for appropriate risk assessment and processing. Therefore, we aimed to elucidate the survival response of C. jejuni and develop a predictive model considering strain variability and uncertainty, which are important for quantitative microbial risk assessment (QMRA) or risk-based processing control measures. We employed the most probable curve (MPC) method to consider the uncertainty in cell concentrations. Further, the multivariate normal (MVN) distribution served as a model for strain variability in bacterial survival behavior. The prediction curves from the MVN successfully captured the parameter variability of the most probable curves of each strain. More than ten reference strains effectively described the strain variability in parameters using the MVN distribution. The findings indicated that, with sufficient strain data, the MVN could estimate the strain variability, including unknown strains. The multi-level model for strain variability can potentially become a specialized tool for QMRA and risk-based processing controls. The combined approach of MPC and MVN provides valuable insights into strain variability, emphasizing the importance of accounting for variability and uncertainty in predictive models for QMRA and risk-based processing control measures.

Transmission rates of veterinary and clinically important antibiotic resistant Escherichia coli: A meta- ANALYSIS

The transmission rate per hour between hosts is a key parameter for simulating transmission dynamics of antibiotic-resistant bacteria, and might differ for antibiotic resistance genes, animal species, and antibiotic usage. We conducted a Bayesian meta-analysis of resistant Escherichia coli (E. coli) transmission in broilers and piglets to obtain insight in factors determining the transmission rate, infectious period, and reproduction ratio. We included blaCTX-M-1, blaCTX-M-2, blaOXA-162, catA1, mcr-1, and fluoroquinolone resistant E. coli. The Maximum a Posteriori (MAP) transmission rate in broilers without antibiotic treatment ranged from 0.4∙10-3 to 2.5∙10-3 depending on type of broiler (SPF vs conventional) and inoculation strains. For piglets, the MAP in groups without antibiotic treatment were between 0.7∙10-3 and 0.8∙10-3, increasing to 0.9∙10-3 in the group with antibiotic treatment. In groups without antibiotic treatment, the transmission rate of resistant E. coli in broilers was almost twice the transmission rate in piglets. Amoxicillin increased the transmission rate of E. coli carrying blaCTX-M-2 by three-fold. The MAP infectious period of resistant E. coli in piglets with and without antibiotics is between 971 and 1065 hours (40 - 43 days). The MAP infectious period of resistant E. coli in broiler without antibiotics is between 475 and 2306 hours (20 - 96 days). The MAP infectious period of resistant E. coli in broiler with antibiotics is between 2702 and 3462 hours (113 - 144 days) which means a lifelong colonization. The MAP basic reproduction ratio in piglets of infection with resistant E. coli when using antibiotics is 27.70, which is higher than MAP in piglets without antibiotics between 15.65 and 18.19. The MAP basic reproduction ratio in broilers ranges between 3.46 and 92.38. We consider three possible explanations for our finding that in the absence of antibiotics the transmission rate is higher among broilers than among piglets: i) due to the gut microbiome of animals, ii) fitness costs of bacteria, and iii) differences in experimental set-up between the studies. Regarding infectious period and reproduction ratio, the effect of the resistance gene, antibiotic treatment, and animal species are inconclusive due to limited data.

Comparing the transmission of carbapenemase-producing and extended-spectrum beta-lactamase-producing Escherichia coli between broiler chickens

The emergence of carbapenemase-producing Enterobacteriaceae (CPE) is a threat to public health, because of their resistance to clinically important carbapenem antibiotics. The emergence of CPE in meat-producing animals is particularly worrying because consumption of meat contaminated with resistant bacteria comparable to CPE, such as extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae, contributed to colonization in humans worldwide. Currently, no data on the transmission of CPE in livestock is available. We performed a transmission experiment to quantify the transmission of CPE between broilers to fill this knowledge gap and to compare the transmission rates of CPE and other antibiotic-resistant E. coli. A total of 180 Ross 308 broiler chickens were distributed over 12 pens on the day of hatch (day 0). On day 5, half of the 10 remaining chickens in each pen were orally inoculated with 5·102 colony-forming units of CPE, ESBL, or chloramphenicol-resistant E. coli (catA1). To evaluate the effect of antibiotic treatment, amoxicillin was given twice daily in drinking water in 6 of the 12 pens from days 2-6. Cloacal swabs of all animals were taken to determine the number of infectious broilers. We used a Bayesian hierarchical model to quantify the transmission of the E. coli strains. E. coli can survive in the environment and serve as a reservoir. Therefore, the susceptible-infectious transmission model was adapted to account for the transmission of resistant bacteria from the environment. In addition, the caecal microbiome was analyzed on day 5 and at the end of the experiment on day 14 to assess the relationship between the caecal microbiome and the transmission rates. The transmission rates of CPE were 52 - 68 per cent lower compared to ESBL and catA1, but it is not clear if these differences were caused by differences between the resistance genes or by other differences between the E. coli strains. Differences between the groups in transmission rates and microbiome diversity did not correspond to each other, indicating that differences in transmission rates were probably not caused by major differences in the community structure in the caecal microbiome. Amoxicillin treatment from day 2-6 increased the transmission rate more than three-fold in all inoculums. It also increased alpha-diversity compared to untreated animals on day 5, but not on day 14, suggesting only a temporary effect. Future research could incorporate more complex transmission models with different species of resistant bacteria into the Bayesian hierarchical model.

Implications of rapid population growth on survey design and HIV estimates in the Rakai Community Cohort Study (RCCS), Uganda

OBJECTIVE

Since rapid population growth challenges longitudinal population-based HIV cohorts in Africa to maintain coverage of their target populations, this study evaluated whether the exclusion of some residents due to growing population size biases key HIV metrics like prevalence and population-level viremia.

DESIGN, SETTING AND PARTICIPANTS

Data were obtained from the Rakai Community Cohort Study (RCCS) in south central Uganda, an open population-based cohort which began excluding some residents of newly constructed household structures within its surveillance boundaries in 2008. The study includes adults aged 15-49 years who were censused from 2019 to 2020.

MEASURES

We fit ensemble machine learning models to RCCS census and survey data to predict HIV seroprevalence and viremia (prevalence of those with viral load >1000 copies/mL) in the excluded population and evaluated whether their inclusion would change overall estimates.

RESULTS

Of the 24 729 census-eligible residents, 2920 (12%) residents were excluded from the RCCS because they were living in new households. The predicted seroprevalence for these excluded residents was 10.8% (95% CI: 9.6% to 11.8%)-somewhat lower than 11.7% (95% CI: 11.2% to 12.3%) in the observed sample. Predicted seroprevalence for younger excluded residents aged 15-24 years was 4.9% (95% CI: 3.6% to 6.1%)-significantly higher than that in the observed sample for the same age group (2.6% (95% CI: 2.2% to 3.1%)), while predicted seroprevalence for older excluded residents aged 25-49 years was 15.0% (95% CI: 13.3% to 16.4%)-significantly lower than their counterparts in the observed sample (17.2% (95% CI: 16.4% to 18.1%)). Over all ages, the predicted prevalence of viremia in excluded residents (3.7% (95% CI: 3.0% to 4.5%)) was significantly higher than that in the observed sample (1.7% (95% CI: 1.5% to 1.9%)), resulting in a higher overall population-level viremia estimate of 2.1% (95% CI: 1.8% to 2.4%).

CONCLUSIONS

Exclusion of residents in new households may modestly bias HIV viremia estimates and some age-specific seroprevalence estimates in the RCCS. Overall, HIV seroprevalence estimates were not significantly affected.

The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty

A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the "true" microbial concentration according to the model, the "actual" concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume. The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis.