Bayesian inference in complex risk assessment with application to health hazards from abiotic toxin exposure

Risk assessment is an iterative process that seeks to quantify the hazards associated with a range of exposure conditions on human health across five general steps: problem formulation, hazard identification, dose-response assessment, exposure assessment, and risk characterization. Animal toxicology and human observational studies are used to characterize toxin risk, but these methods fail to adequately produce a function that can confidently describe real-world conditions and outcomes. Among the concerns are 1) extrapolation from animals to humans; 2) the shape of the relationship between high-dose and low-dose response; 3) the difference in response between average, resilient, and susceptible individuals; and 4) the impact of interactions among toxins on the dose-response relationship. Statistical models can support researchers in interpreting data by formalizing hypotheses about complex and nonlinear relationships, as well as the interactions between components within a model. The integration of mathematical modelling, and quantitative, real-world and experimental data is essential to investigating biological and ecological hypotheses; however, a general scarcity of a systematic method for evaluating the impact, among humans, from abiotic toxin exposures remains, limiting researchers' capacity to estimate individual health risk associated with environmental toxins that are informed by real-world data. We aim to study Bayesian methods for the development of a methodology for examining the dose-response relationship between abiotic toxins and health outcomes, considering complex exposure data, interactions, and outcomes. This seeks to build on the developments in the study of Bayesian inference and MCMC simulations to further characterize the dose-response relationship and the associated uncertainty in modelling such a complex relationship with large amounts of inputs for each individual of study. The limitations in the current method of deriving a dose-response relationship undermine the applicability of human health risk modelling from environmental hazards. The complexity of inputs and the high dimensionality of factors influencing susceptibility produce a problem beyond the scope of solving a mechanistically-informed function. The interactions among even the most common pollutants would contain a combinatorial problem that would take decades to solve with conventional methods of testing, and given the thousands of abiotic and biotic exposures a person interacts with every day, alongside an individual's susceptibility profile, the enormity of this challenge is so great that it is unlikely that current methods will be able to adequately characterize true risk. Furthermore, the issue of parameterizing a model becomes more important and more challenging as the complexity of biological systems increase. Given the range of inputs that influence individual susceptibility and the complex interplay between exposure and response between individuals, modelling frameworks must be able to account for a detailed understanding of the ecological and biological processes that contribute to health outcomes. Development in the space of parameterizing such complex models requires further study to produce methodologies and techniques that can account for diverse sources of data and outcomes.