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

风险评估是一个迭代过程，旨在通过五个一般步骤量化与一系列暴露条件有关的危害：问题制定、危害识别、剂量反应评估、暴露评估和风险定性。动物毒理学和人类观察性研究被用来描述毒素风险，但这些方法无法充分产生一个功能，可以自信地描述现实世界的条件和结果。这些问题包括：（1）从动物外推到人类;（2）高剂量和低剂量反应之间关系的形态;（3）普通、有弹性和易受影响的个体之间反应的差异;（4）毒素之间相互作用对剂量-反应关系的影响。统计模型可以通过形式化关于复杂和非线性关系的假设以及模型中组件之间的相互作用来支持研究人员解释数据。数学建模以及定量、真实世界和实验数据的整合对于调查生物和生态假设至关重要;然而，仍然普遍缺乏系统的方法来评估人类接触非生物毒素的影响，限制了研究人员根据真实世界数据估计与环境毒素相关的个人健康风险的能力。我们的目标是研究贝叶斯方法的方法学研究非生物毒素和健康结果之间的剂量反应关系，考虑到复杂的暴露数据，相互作用和结果的发展。这旨在建立在贝叶斯推理和MCMC模拟研究的发展基础上，进一步表征剂量-反应关系和相关的不确定性，为每个研究个体建立具有大量输入的复杂关系模型。目前推导剂量-反应关系的方法的局限性破坏了环境危害人类健康风险模型的适用性。输入的复杂性和影响易感性的因素的高维性产生的问题超出了解决机械通知功能的范围。即使是最常见的污染物之间的相互作用也会包含一个组合问题，用传统的测试方法需要几十年才能解决，并且考虑到一个人每天与成千上万的非生物和生物接触，以及个人的易感性，这一挑战的艰巨性是如此之大，以至于目前的方法不太可能能够充分描述真正的风险。此外，随着生物系统复杂性的增加，模型参数化的问题变得更加重要和更具挑战性。鉴于影响个人易感性的各种投入以及个人之间接触和反应之间的复杂相互作用，建模框架必须能够详细了解有助于健康结果的生态和生物过程。要在参数化这类复杂模型方面取得进展，就需要进一步研究，以制定能够说明各种数据来源和结果的方法和技术。

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.