Development of a novel machine learning-based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose-response modeling framework

Research gaps and priorities for quantitative microbial risk assessment (QMRA)

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Advanced data analytics and "omics" techniques to control enteric foodborne pathogens

Enteric pathogens, particularly bacterial pathogens, are associated with millions of cases of foodborne illness in the U.S. and worldwide, necessitating the identification and development of methods to control and minimize their impact on public health. Predictive modeling and quantitative microbial risk assessment are two such methods that analyze data on microbial behavior, particularly as a response to changes in the food matrix, to predict and control the presence and prevalence of these pathogens in food. However, a number of these bacterial enteric pathogens, including Escherichia coli, Listeria monocytogenes, and Salmonella enterica, have inherent genetic and phenotypic differences among their subtypes and variants. This has led to an increasing reliance on "omics" technologies, including genomics, proteomics, transcriptomics, and metabolomics, to identify and characterize pathogenic microorganisms and their behavior in food systems. With this exponential increase in available data on these enteric pathogens, comes a need for the development of novel strategies to analyze this data. Advanced data analysis/analytics is a means to extract value from these large data sources, and is considered the core of data processing. In the past few years, advanced data analytics methods such as machine learning and artificial intelligence have been increasingly used to extract meaningful, actionable knowledge from these data sources to help mitigate food safety issues caused by enteric pathogens. This chapter reviews the latest in research into the use of advanced data analytics, particularly machine learning, to analyze "omics" data of enteric bacterial pathogens, and identifies potential future uses of these techniques in mitigating the risk of these pathogens on public health.

Machine learning to predict the relationship between Vibrio spp. concentrations in seawater and oysters and prevalent environmental conditions

Vibrio parahaemolyticus and Vibrio vulnificus are bacteria with a significant public health impact. Identifying factors impacting their presence and concentrations in food sources could enable the identification of significant risk factors and prevent incidences of foodborne illness. In recent years, machine learning has shown promise in modeling microbial presence based on prevalent external and internal variables, such as environmental variables and gene presence/absence, respectively, particularly with the generation and availability of large amounts and diverse sources of data. Such analyses can prove useful in predicting microbial behavior in food systems, particularly under the influence of the constant changes in environmental variables. In this study, we tested the efficacy of six machine learning regression models (random forest, support vector machine, elastic net, neural network, k-nearest neighbors, and extreme gradient boosting) in predicting the relationship between environmental variables and total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater and oysters. In general, environmental variables were found to be reliable predictors of total and pathogenic V. parahaemolyticus and V. vulnificus concentrations in seawater, and pathogenic V. parahaemolyticus in oysters (Acceptable Prediction Zone >70 %) when analyzed using our machine learning models. SHapley Additive exPlanations, which was used to identify variables influencing Vibrio concentrations, identified chlorophyll a content, seawater salinity, seawater temperature, and turbidity as influential variables. It is important to note that different strains were differentially impacted by the same environmental variable, indicating the need for further research to study the causes and potential mechanisms of these variations. In conclusion, environmental variables could be important predictors of Vibrio growth and behavior in seafood. Moreover, the models developed in this study could prove invaluable in assessing and managing the risks associated with V. parahaemolyticus and V. vulnificus, particularly in the face of a changing environment.