Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

Abstract

To comply with the international planetary protection (PP) policy set forth by the Committee on Space Research (COSPAR) and National Aeronautics and Space Administration (NASA) Agency level requirements, spacecraft destined to biologically sensitive planetary bodies should “conduct exploration of them so as to avoid their harmful contamination”. Analysis, testing, and inspection are the standard forward verification activities used to demonstrate compliance with biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA-approved PP methods for culture-based assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the Mariner Mars 1971 Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimates factors in the following variables: (1) observed bioburden count; (2) representative volume processed; (3) sampling device efficiencies; and (4) sampled surface area. Notably, to account for potential errors in the approach, a 0 observed count is changed to a NASA policy derived count of 1. The data generated by spacecraft bioburden verification campaigns in the past have resulted in $> \pmb{80}{\%}$ of wipes and $> \pmb{90}{\%}$ of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a game-changer for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, employing an empirical Bayes (EB) statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of flight hardware surfaces. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for EB analyses. Several non-informative priors are currently under investigation to assess fitness, including the use of a non-informative prior distribution bounded by the currently utilized NASA specification values for a basis of risk to account for unknowns during the spacecraft integration and test process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g., vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using the EB approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.