Bayesian framework for bioburden density calculations to perform planetary protection probabilistic risk assessment

Abstract

The planetary protection discipline aims to minimize microbial contamination on spacecraft in order to prevent inadvertent contamination of other planetary bodies. This is known as forward planetary protection (PP). Planetary protection probabilistic risk assessment relies on two core methodologies: contamination probability event tree analysis and statistical parameter estimation. Planetary protection engineers combine several techniques to estimate the bioburden on spacecraft components. A direct assay to enumerate colony forming units (CFUs) is the preferred methodology; but given a similar processing environment, the bioburden of certain components is inferred using (1) a NASA-defined bioburden estimate based on the biological cleanliness of the manufacturing/assembly environment or (2) sampled data from a similar spacecraft component. This paper presents an empirical Bayesian framework to systematically treat bioburden estimation and its uncertainties on different spacecraft assembly levels, starting with measurement procedures for implementing various components into subsystems and the spacecraft as a whole. The Bayesian approach is effectively handles estimations and their uncertainties at different levels, producing reliable bioburden estimates for evaluating probability of contamination.