NASA Langley Research Center, Hampton, VA, United States.
NASA Langley Research Center, Hampton, VA, United States.
Life Sci Space Res (Amst). 2021 Nov;31:14-28. doi: 10.1016/j.lssr.2021.07.002. Epub 2021 Jul 17.
A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty as well as model-form uncertainty associated with differing theoretical or empirical formalisms. Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future models, improve uncertainty quantification, and reduce the impact of subjective bias. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met for future space exploration missions, such as human missions to Mars. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets. Closer agreement is seen at the median ± one sigma due to the inherent similarities in available models. Identification of potential new models, epidemiological data, and methods for statistical correlation between predictive ensemble members are discussed. Alternate ways of communicating risk and acceptable uncertainty with respect to NASA's current permissible exposure limits are explored.
一种新的 NASA 空间辐射风险建模方法成功地将当前 NASA 概率癌症风险模型扩展到一个能够考虑子模型参数不确定性以及与不同理论或经验公式相关的模型形式不确定性的集合框架中。集合方法已经广泛应用于天气预报、传染病暴发模型以及某些陆地辐射防护应用中，以更好地了解不确定性如何影响风险决策。将集合方法应用于空间辐射风险预测有潜力有效地纳入新的研究结果，允许纳入未来的模型，提高不确定性量化，并减少主观偏见的影响。此外，如果未来的太空探索任务（如人类对火星的任务）无法满足当前的健康/医疗标准，则跨多个预测模型的集合进行风险预测可以为利益相关者提供有关风险接受的额外信息。在这项工作中，提出了实施辐射质量、剂量和剂量率效应因子、超额风险和潜伏期多个子模型的集合风险预测。讨论了用于考虑单个模型偏差的集合模型权重和相关性的初始共识方法。在这些分析中，集合预测与 NASA 当前用于评估宇航员可允许任务持续时间的癌症风险预测模型的结果相比表现良好。然而，在 95%置信水平上限处获得了大范围的预测风险值，模型必须外推到可用的生物数据集之外。由于可用模型的固有相似性，在中位数±一个标准差处看到了更紧密的一致性。讨论了识别潜在的新模型、流行病学数据以及预测集合成员之间统计相关性的方法。探讨了与 NASA 当前可允许暴露限值相关的风险和可接受不确定性的替代沟通方式。
