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Accepted Manuscript
The Risk of Staphylococcus Skin Infection During Space Travel and
Mitigation Strategies
Shuhao Xiao , Kasthuri J. Venkateswaran , Sunny C. Jiang
PII:
DOI:
Reference:
S2352-3522(18)30034-3
https://doi.org/10.1016/j.mran.2018.08.001
MRAN 61
To appear in:
Microbial Risk Analysis
Received date:
Revised date:
Accepted date:
1 June 2018
9 August 2018
9 August 2018
Please cite this article as: Shuhao Xiao , Kasthuri J. Venkateswaran , Sunny C. Jiang , The Risk of
Staphylococcus Skin Infection During Space Travel and Mitigation Strategies, Microbial Risk Analysis
(2018), doi: https://doi.org/10.1016/j.mran.2018.08.001
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service
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ACCEPTED MANUSCRIPT
Highlights
Staphylococcus and S. aureus are important bacterial contaminants on the space station
Skin infection risks from S. aureus among astronauts are likely during space travel
Skin cleaning is an important strategy to reduce S. aureus infection risks
Understanding of microbial virulence in space can improve risk prediction
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The Risk of Staphylococcus Skin Infection During Space Travel and
Mitigation Strategies
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Shuhao Xiao1,2, Kasthuri J. Venkateswaran3, Sunny C. Jiang1*
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Civil and Environmental Engineering, University of California, Irvine, California, USA
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Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of
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Public Health, Maryland, USA
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Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA.
*Correspondence: Sunny C. Jiang, 844E Engineering Tower, University of California, Irvine,
Revised July 8, 2018
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92697, Tel: 949-824-5527; Email: sjiang@uci.edu
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Running Title: Risk of skin infection during space travel
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Keywords: ISS microbial observatory; Staphylococcus aureus; skin infection; cleaning regimen;
microbial transfer rate; QMRA;
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Abstract
Among numerous challenges facing space travellers, microbial infection is one of the unknown
risks associated with human spaceflight. Prevention and control of microbial infections are of
critical concern during space missions. The objective of this research is to develop a quantitative
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microbial risk assessment (QMRA) to model the risk of Staphylococcus aureus skin infection
and mitigation strategies that may effectively reduce skin infection risks. QMRA was carried out
by incorporating the level of S. aureus contamination from International Space Station Microbial
Observatory Experiment, bacterial transfer rate to skin, growth pattern of S. aureus on skin,
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space travellers’ daily behaviour and dose-response model. The results demonstrate that a daily
skin cleaning regimen has a significant effect on reducing the skin infection risks. Once a day
skin cleaning reduces infection risk by 84.2% and twice a day skin cleaning can reduce the risk
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of skin infection by 96.1% during a seven-day space mission. Frequency of contact with
contaminated surfaces and time elapsed between cleaning events are the most important input
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parameters that contribute to the overall risk outcome. There are degrees of uncertainties
associated with the predicted outcomes when interpreted by itself due to the limitation of
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microbial data and the dose-response model that derived from a short-term clinical study on
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Earth. The comparative risk analysis as used in this study offers a scientific basis regarding the
effectiveness of interventions (skin cleaning regimens) in mitigating skin infection risks during
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spaceflight.
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1. Introduction
Space exploration has been an enduring fascination for humans. Since the success of the first
human spaceflight in 1961, astronauts have ventured out of the Earth’s orbit to explore the extraterrestrial world. With the advent of the International Space Station (ISS), a microgravity
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laboratory that orbits around the Earth, scientific research has been conducted to shed light on
the prospects of space exploration. Astronauts have reportedly spent as long as 534 days in the
ISS before returning to the Earth (NASA 2017). Although human missions to Mars have been
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the subject of science fiction, engineering, and scientific proposals since the 19th century,
SpaceX’s announcement to fly two private citizens on a trip around the Moon (SpaceX 2017) has
made the dream of space travel more attainable by the general public.
However, the risks of space travel are very real. Among numerous challenges facing
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space travellers, microbial infection is one of the unknown risks associated with prolonged
human spaceflight. Prevention and control of microbial infections among astronauts are of
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critical concern during space missions. Rigid medical screening processes, vaccination programs,
as well as comprehensive infection control education to mitigate the risk of microbial infections
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are currently in place for astronauts (Mermel 2013). Due to limited access to medical attention
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during a mission, any possibility of infection should be prevented at all costs to ensure health and
safety of the astronauts and to maximize the success rate of the space mission. This awareness is
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especially crucial as enhanced microbial virulence (Crabbe et al. 2011; Nickerson et al. 2004;
Rosenzweig et al. 2010; Wilson et al. 2007), resistance against antibiotics (Lapchine et al. 1986;
Planel et al. 1982; Tixador et al. 1981; Tixador et al. 1985; Tixador et al. 1994), and growth of
certain microbes in space (Kacena and Todd 1997; Kacena et al. 1997; Kacena et al. 1999a;
Kacena et al. 1999b; Klaus et al. 1997; Mattoni 1968) have been reported. Furthermore, the close
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proximity of crewmembers to one another in spacecraft potentially promotes the spread of
secondary infections (Gosce et al. 2014).
Microbial contamination of spacecraft and space stations were well known even before
the ISS era (Novikova 2004; Pierson et al. 1996). For instance, a 15-year monitoring program in
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the Russian’s Mir space station revealed the persistence and high prevalence of bacteria and
fungi on the interior surfaces and in the air (Novikova 2004). Among the bacterial genera
identified, the Staphylococcus genus—a resident flora on human skin and nasal cavities
(Wertheim et al. 2005)—was the most prevalent and found in 55.5% of the surface samples, 53.2%
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of the air samples, and 8.3% of the condensate samples in the space station. Staphylococcus
accounted for 95.5% of all microbial sequences identified in the habitable module in Mars500,
the long-term ground simulations experiment (Schwendner et al. 2017). Areas with high human
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activity were identified as hotspots for microbial accumulation. While most Staphylococcus spp.
are benign to the vital functioning of human skin, Staphylococcus aureus, an opportunistic
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pathogen, was also found in the Mir station (Novikova 2004), ISS (Checinska S. A 2016;
Venkateswaran 2017) as well as Mars500 modules. S. aureus can induce infections with
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symptoms ranging from soft tissues and skin infections (i.e. impetigo, boils, cellulitis) (Mayo
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Clinic 2014) to severe life-threatening diseases (i.e. bacteremia, metastatic infection, and sepsis).
S. aureus bacteremia incidence rates range from 20 to 50 cases/100,000 population per year with
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10% to 30% mortality rates among bacteremia cases (van Hal et al. 2012). This mortality rate
accounts for a greater number of deaths than for AIDS, tuberculosis, and viral hepatitis combined
(van Hal et al. 2012). S. aureus bacteremia is most frequently reported in health care settings
(Lowy 1998).
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The recent ISS-Microbial Observatory Experiment carried out a systematic survey of
microbial burden on ISS surfaces. Samples were collected using 9 x 9 polyester wipe under
aseptic conditions from eight designated surface locations on the spacecraft (Venkateswaran
2017). Samples were transferred back to Earth in sterile zip lock bags through three flights by
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SpaceX-5, 6, 8 rockets. Bacterial isolation was performed on two different nutrient media and
cultured isolates were identified by 16S rRNA gene (for details see reference (Venkateswaran
2017)). In comparison with previous studies (Novikova 2004; Pierson et al. 2012;
Venkateswaran et al. 2014), these data best represent the fraction of viable S. aureus among the
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total bacterial burden on ISS surfaces and offer a glimpse of the potential risk of skin infection
during space travel. An understanding of the risk propagation is the first step in developing
mitigation strategies, considering that skin infection among astronauts is the most commonly
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reported infection during spaceflight (Mermel 2013).
Hygiene practices have long been recognized as important strategies against spread of
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infectious diseases (Chiller et al. 2001; de Almeida e Borges et al. 2007; Larson 1999). Due to
the microgravity conditions and the limitation of water resources on the ISS, astronauts are
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trained to use ―towel baths‖ as hygiene practices instead of showering (Anderson 2014). The
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towel bath procedures require scrubbing of the body using a disinfectant-infused towel by hand
to achieve cleansing effects. As such, it is plausible that negligence in the conduct of towel bath
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may lead to build-up of microbes that can induce skin infection. Other factors such as the
frequency of hygiene maintenance procedures could be important determinants in preventing
skin infection. However, so far little is known about the quantitative risk or the effectiveness of
skin cleaning practice in the prevention of S. aureus skin infection during space travel.
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Here we present an analysis of S. aureus skin infection risk during spaceflight using a
Quantitative Microbial Risk Assessment (QMRA) model. We integrated the data of S. aureus
surface contamination on the space station obtained by ISS-Microbial Observatory Experiment
Team (Venkateswaran 2017), bacterial transfer rate to skin, the growth pattern on human skin,
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and space travellers’ daily behaviour to predict the dose of the opportunistic pathogen on the skin
surfaces of travellers. We then applied a S. aureus infection dose-response model to understand
the potential risk for a space traveller to acquire S. aureus infection.
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2. Material and Methods
The QMRA was conducted following the risk assessment framework outlined by the U.S.
National Academy of Sciences, which consists of 1) hazard identification, 2) exposure
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assessment, 3) dose–response assessment, and 4) risk characterization (National Research
Council 1983). The information flow through the model is outlined in Figure 1.
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2.1. Pathogen density on ISS surface
Legacy data and ISS-Microbial Observatory Experiment reveal that Staphylococcus is the most
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predominant bacterial genus found on ISS interior surfaces (Castro et al. 2004; Pierson et al.
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2012; Venkateswaran et al. 2014; Venkateswaran 2017). S. aureus, a well-known opportunistic
pathogen to humans, has also been frequently reported in previous studies. Considering the
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evidence that skin infection is the most frequently documented condition during space travel
(Mermel 2013), estimating its potential risk to the astronauts during space missions and
developing mitigation strategies has been of great interest. S. aureus can cause a variety of
diseases in humans (Tong et al. 2015); however, in this study, only the skin infection risk
induced by S. aureus was evaluated.
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Among different reports of S. aureus contamination, ISS-Microbial Observatory
Experiment data provide the only quantitative evaluation of microbial burden using both culturebased and culture independent approaches. In addition, the ISS-Microbial Observatory
Experiment also identifies the percentage of S. aureus among randomly selected bacterial
aureus
in %) from eight
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isolates in each sample. The distribution of S. aureus percentage (PS.
sampling locations on ISS over three sampling flights is combined with the empirical distribution
of total cultivable bacterial density (Ctotal CFU/100 cm2) (Venkateswaran 2017) counted on two
types of nutrient agars (shown in Figure 1 and 2, data were collected from (Venkateswaran 2017))
using Eq. 1.
CS. aureus = Ctotal × PS. aureus
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to compute the density distribution of S. aureus on ISS interior surface (CS. aureus CFU/100 cm2)
(1).
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All data from eight sampling locations within the ISS interior are pooled together to
generate the distribution curve. In case of 0 value, a very small value of 0.001 is used to replace
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0 because 0 measurement is not a true value and cannot be log transformed.
2.2. Exposure frequency and transfer rate of S. aureus to astronauts
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S. aureus can be transmitted to humans via multiple pathways including airborne transmission
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and direct physical contact with surfaces contaminated by the bacteria. Only the transfer of S.
aureus from contaminated surfaces to astronauts is considered here since this study only focused
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on the risk of skin infection. According to the study of Arinder et al. (Arinder et al. 2016), the
transfer rate of S. aureus from contaminated surfaces to artificial human skin varies with the type
of contaminated materials, with values ranging from 1.5% to 9.4%. The ISS is composed of
multiple narrow spaced cabins filled with cargo, electronics and experimental devices, with
surfaces covered by aluminium and Teflon (Frost 2014). The transfer rate from stainless steel
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contaminated surfaces to human skin by Arinder et al. (Arinder et al. 2016) is used since it is the
closest material that resembles the interior surfaces of the ISS. Hence, the amount of S. aureus
transferred from contaminated surfaces to human skin during each skin contact with surfaces
event is calculated using Eq. 2.
C = CS. aureus ×μ
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(2),
where the transfer rate μ is defined as the portion of the microorganisms transferred from
surfaces to human skin. A normal probability of distribution of transfer rates is applied based on
the mean and standard deviation. We assume a random frequency for a space traveller’s bare
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skin to contact with the interior of the spacecraft, ranging from 144 (one contact every ten
minutes) to 720 (one contact every two minutes) times per day, during their active hours. This
contact frequency is based on the observation that astronauts move around in the spacecraft by
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floating through spaces and using their hands and arms to guide themselves for the direction and
the speed of movement. There are frequent contacts of their hands and bare arms with ISS inner
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surfaces during the active hours. The parameters used in the model are summarized in Table 1.
2.3. Pathogen Growth Model
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S. aureus that are transferred from contaminated surfaces to human skin can grow and colonize
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the skin in the absence of disinfectants. To characterize the growth rate of S. aureus on human
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skin, we adopt the growth model proposed by Rose and Haas (Rose and Haas 1999) Eq. 3.
(3),
where C is the microorganism surface density (#/cm2); Cmax is the maximum microorganism
density; k1 is the initial inactivation rate constant (1/time); k2 is rate constant for the decrease in
inactivation (1/time); k3 is growth rate constant (cm2/#–time). The first term in Eq. 3 describes
the initial die-off rate of the bacteria on skin surface following the transfer perhaps due to natural
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decay, the competition with skin microbiome, or skin immunity. The second term defines the
subsequent growth of the remaining bacteria on the skin surface. Overall this equation yields a
growth curve with a sharp decline at the beginning, followed by a slow growth that eventually
reaches a steady state where the bacterial density is at a maximum plateau (i.e. Cmax). The best-fit
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model parameters values (k1, k2, k3 and Cmax) by Rose and Haas (Rose and Haas 1999) that use
the dose-response data reported by Singh et al. (Singh et al. 1971) are adopted in this study
(Table 1). This model is also used to incorporate the additional transfer of S. aureus to
astronaut’s skin with each exposure event.
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2.4. Removal of pathogens
The hygiene maintenance routine of space travellers (i.e., wet towel bath) is expected to remove
a certain portion of S. aureus from their skin. According to the study by Arinder et al. (Arinder et
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al. 2016), the bacteria density on human skin is reduced by approximately 2.0±0.4 log10 with
each cleaning procedure. In reflecting the cleansing effect of the hygiene routine, this value w is
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incorporated into the growth model as described in the previous section. Under this setting, the
procedure.
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bacterial density on skin surface experiences an instantaneous drop upon each cleaning
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2.5. Dose-response assessment
The risk of an astronaut to contract skin infection due to S. aureus is estimated using the
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exponential dose-response model developed by Rose and Haas (Rose and Haas 1999) (Eq. 4).
(4),
where π is the risk of infection, d is the dose of S. aureus on the astronaut’s skin, and r is the
best-fit parameter of the model based on the maximum likelihood fitting technique as described
by Rose and Haas (1999). It should be noted that the dose as used in this model is represented by
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the integration of time and bacterial density, which is the total area under the growth curve (AUC)
Eq. 5.
∫
(5).
2.6. Risk Characterization
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In characterizing the risk of S. aureus, three scenarios are examined to evaluate the efficiency of
hygiene practice for mitigating the risk of S. aureus. The first scenario simulates the worst-case
scenario where no hygiene procedure is conducted during a space flight mission. Although no
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skin cleaning is not a realistic scenario under normal operational condition, it is served as a
comparison with the normal routine, where the skin cleanings are conducted daily. This scenario
may occur for body parts that are hard to reach and neglected during towel bath; or when the
normal practice is disrupted. The second scenario examines the risk of cleaning only once per
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day, undertaken at a randomly chosen time point within 12 hours after waking up. The third
scenario examines the risk of cleaning two times per day, once within 4 hours after waking up
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and the other within 4 hours before sleeping. The daily activities of space travellers and their
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exposure to S. aureus through skin contact with contaminated surface and removal through
cleaning regimen are illustrated in Figure 2.
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2.7. Sensitivity Analysis
Partial Spearman’s Ranked Correlation Analysis was conducted to assess the impact of each
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input parameter on the risk output. The analysis was conducted for no-cleaning and cleaning
once a day scenarios at multiple timepoints starting from the end of day 1 to the end of day 7.
Due to the accumulative nature of the input-output relationship—the output at any given time
point is affected by all the values for each input parameter before and at that timepoint—, either
the mean value of the Monte Carlo simulated individual input parameter or summed value for
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each input parameter at the timepoint of interest (starting from time 0) was used to represent its
impact on the risk output. The input parameters include: a) cumulative frequency of skin contact
with environmental surfaces, b) mean (%) value of total bacterial transfer from environmental
surface to human skin, c) mean value of total bacterial density on environmental surface, d)
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mean portion (%) of total bacteria as S. aureus, e) cumulative log reduction of S. aureus due to
cleaning event(s), and f) cumulative time elapsed between each cleaning event.
2.8. Monte-Carlo Simulation
The algorithms described above were written and implemented using MATLAB R2015b (The
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MathWorks Inc., Natick, MA). Output results were computed for 1,000 iterations as MonteCarlo simulations by sampling input parameters (e.g. transfer rate, reduction efficiency by
cleaning, contact frequency) based on their respective probability distributions (Table 1). In
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justifying the adequacy of 1,000 iterations to represent the full output distribution spectrum,
simulations for 10,000 iterations were also run for a shorter time period (i.e. 2 days). The results
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show no significant difference in the outcomes in comparison with that of 1,000 iterations. The
growth curve and infection risk of the three scenarios were computed for a seven-day space
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mission.
3. Results
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The microbial burden from bacterial contamination on ISS interior surfaces varies widely from
one location to another, which was shown by the seven log10 differences in the normalized
cumulative probability of the total bacterial density (Figure 3). The portion of S. aureus among
total bacteria from different locations on ISS, as revealed by sequencing analysis of individual
colony randomly selected from the culture collection, also varies significantly (Figure 4).
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Therefore, the concentration of S. aureus at any given location and time on ISS surface is
a combined stochastic joint probability distribution of bacterial burden and portion of S. aureus
among total bacteria. Our risk estimate using Monte Carlo sampling of S. aureus distribution
indicated that if no skin cleaning regimen is practiced, S. aureus density on a traveller’s skin
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surface can grow exponentially upon initial and subsequent contacts with contaminated surfaces;
potentially reaching a density of 2.33×109 cells/cm2 at the end of a seven-day space mission
(Figure 5a). The risk of infection also accelerates quickly from near zero at the beginning of the
flight to 5.01×10-3 within the first day of the space mission, suggesting a 0.5% chance of a flight
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crew to develop skin infection. By the third day of the space mission, the median risk of S.
aureus infection approaches 74% (Figure 5d).
However, a daily skin cleaning regimen would significantly reduce the density of S.
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aureus on a space traveller’s skin surface and infection risk. When a skin cleaning event is
implemented randomly within the first 12 hours of waking up in a day, the S. aureus density on
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the skin surface displayed a form of wave motion over time. Approximately 2.0 log10 reduction
of S. aureus is observed after each skin cleaning event, followed by a slow increase of bacterial
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density due to the subsequent bacterial exposures from contaminated surfaces (Figure 5b). S.
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aureus growth on skin surface mainly occurs during the night when the traveller is sleeping. The
median bacterial density observed at the end of seven days is 3.8 log10 lower than the no-
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cleaning scenario, and never exceeds 3.75×105 cells/cm2, with a 90% confidence interval
between 1.94×104 and 1.34×106 cells/cm2. The variability of bacterial density on different
locations of the ISS interior surfaces contributes to ranges of bacterial density on skin. This
variability is further amplified by S. aureus transfer frequency and transfer rate to traveller’s skin
(Figure 5b). The median risk of S. aureus infection has reduced by 91.9% on the day three of the
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space travel and 84.2% on the day seven of the space mission in comparison with the nocleaning scenario. This result indicates that the progression of the infection rate has been slowed
significantly with daily skin cleaning; the median risk of skin infection do not exceed 0.16 at the
end of the seven days (Figure 5e).
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Twice per day skin cleaning regimen—once in the morning hours and once in the
evening hours—further reduces bacterial accumulation on the skin surface. Although S. aureus
density rises during sleeping hours due to uninterrupted growth of the bacterium, the final
density of S. aureus is 4.5 log10 lower than the no-cleaning scenario at the end of the seven-day
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space mission (Figure 5c). The risk of infection is reduced by an additional 75% in comparison
with once a day cleaning regimen, and is reduced by 96.1% in comparison with the no-cleaning
scenario, with an overall median risk of less than 3.92×10-2 or 3.9% at the end of the seven days
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(Figure 5f).
Sensitive analyses were used to determine the critical role of six model input parameters:
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a) cumulative frequency of skin contact with random surfaces in ISS; b) mean % of bacterial
transfer during each contact; c) mean value of total bacterial density on the ISS surfaces; d)
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mean % of S. aureus among total bacteria; e) cumulative bacterial reduction through cleaning
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events; and f) cumulative time elapsed between each cleaning, on the risk outcome. The results
show that of the six input parameters, the cumulative frequency of bare skin contact with random
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surfaces is the most influential input parameter in determining the risk outcomes assuming the
no-cleaning scenario, with strong positive correlation ranging from 0.40 to 0.70 throughout the
timepoints considered (Figure 6a). Both the contact rate and cumulative time elapsed between
cleaning events contribute significantly to the risk outcomes when the clean regimen is
implemented (Figure 6b). The cumulative time elapsed between cleaning events shows a strong
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negative correlation with the risk that ranges from -0.85 to -0.45. Therefore, frequent
environmental contacts leads to a higher risk, while cleaning event that occurs later into the day
leads to a lower risk because it reduces the uninterrupted growth at night.
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4. Discussion
The outcomes of this study offer a glimpse of the potential microbial infection risk of skin during
space travel. This study should be considered as a screening QMRA since it is a first, best step
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towards risk interpretation given the limitation of data related to the unique setting of ISS and
our imperfect understanding of specific skin infection under such unique conditions.
Nevertheless, the effectiveness of adopting a skin cleaning regimen in the prevention of S.
aureus skin infection is clearly indicated by the outcomes of our analysis. The results show skin
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cleaning once a day reduced the risk by 84.2%; increasing skin cleaning from once a day to twice
a day reduces the infection risk by 96.1% in comparison with the no-cleaning scenario.
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It is also important to note, although the bacterial density on skin surface is reduced
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immediately following each cleaning event, the risk is not reduced. The bacterial dose on skin
surface that is represented by the integral of time and bacterial density will continue to increase
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with time. The risk reduction is only achieved in relative terms due to retardation of bacterial
growth after each cleaning event. It is uncertain if the assumptions and the does-response model
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applied in this study would be applicable for much longer flight duration, such as a human
mission to Mars, due to the lack of long-term studies of S. aureus dose-response relationship.
The model used in the current study is developed based on a seven-day clinical study, in which
the skin infection (appearance of erythematous rash) is attributed to the amplification of S.
aureus on the human subjects’ skin over time (Singh et al. 1971). The clinical study indicates
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that the organisms were confined to the surface and did not proliferate within the living portion
of the skin (Singh et al. 1971). Whether or not this type of superficial infection can induce
invasive infection, many human, microbial and environmental factors are critically important but
are not yet fully understood. Additional data collection and model fitting are necessary to gain
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better understanding of the risk propagation during long-term space travel.
Furthermore, skin cleaning is assumed as the only mechanism of reducing the bacteria on
human skin. In reality, there are other mechanisms of bacterial removal including skin shedding,
secretion of antimicrobial peptides in sweat (Schittek et al. 2001), and defence by skin microflora
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(Cogen et al. 2008; Findley and Grice 2014). Recent studies have indicated that skin microbiome
enhances human immunity and offers microbial defence against infection (Naik et al. 2015;
Zhang et al. 2015). These mechanisms can further reduce the level of S. aureus on skin surface,
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but are not included in our model due to the lack of the appropriate quantitative data.
Contrary to the benefit of skin cleaning in prevention of infection, there is also a volume
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of literature indicating the negative impact of frequent skin cleaning and hand washing,
especially with sanitizers (Larson 1999; Larson et al. 1998). The washing procedures change the
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skin native microflora, which leave opportunities for colonization by foreign microbes (de
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Almeida e Borges et al. 2007). For example, a previous study indicates nurses who wash hands
frequently and wear gloves were twice as likely to be colonized with Staphylococcus hominis, S.
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aureus, and their hands had a greater number of bacterial species (Larson et al. 1998). The towel
bath practice with disinfectant-infused towel may also cause dryness of the skin leading to
minute cracks, which may provide an avenue for S. aureus to enter bypass the epidermis. Direct
investigations of skin condition and skin microbiome are necessary to make a definitive
conclusion on the effect of skin cleaning.
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Of course, the accuracy of the outcomes from a model is largely dependent on the data
input. Among them, the data for microbial contamination in the spacecraft are one of the
limitations due to the difficulties to obtain data from space station. We adopted microbial data
from the most recent ISS-Microbial Observatory Experiment to estimate the proportion of S.
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aureus among all bacteria on the ISS interior surface (Venkateswaran 2017) because it is the
only study provides the quantitative data on portion of viable S. aureus. Although other reports
did not identify bacteria to the species level or give quantitative results of S. aureus other than its
presence, many indicated the high portion of Staphylococcus detected in space station and
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postulated skin shedding under microgravity was the main cause (Schwendner et al. 2017). The
understanding of skin shedding and the precision of the S. aureus concentration on space station
can be improved through additional data collection using new molecular biological and genome
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sequencing technologies that would unambiguously identify/quantify viable S. aureus on the
spacecraft surfaces. Our decision to adopt cultivable S. aureus data as the model input is also to
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align with the condition of dose-response model that is developed based on cultured S. aureus in
clinical studies (Singh et al. 1971).
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Despite the uncertainties in the concentration of S. aureus on ISS interior surfaces where
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astronauts work and live, the sensitivity analyses indicate that the level of risk is influenced
mostly by the frequency of bare skin contact with contaminated surfaces, assuming that no skin
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clean regimen is implemented. This outcome indicates wearing protective clothing that covers
arms and legs could reduce the frequency and rate of bacteria transferred from the surfaces to
bare skin. Although this approach is not quantitatively assessed in this study, it stands as a
potential effective measure against skin infection when no skin cleaning can be implemented.
When skin cleaning is implemented, the risk outcome is also highly sensitive to time elapsed
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from previous cleaning, which again emphasizes the critical risk mitigation effect of personal
hygiene.
The uncertainty of the risk estimation of skin infection could also derive from our
incomplete understanding of the behaviour of S. aureus under the microgravity conditions in
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space (Castro et al. 2011; Crucian et al. 2008; Vukanti et al. 2012). The effect of microgravity
was not incorporated in the current risk assessment model because of the uncertainty of those
factors. In fact, there are contradictory reports of the microbial virulence: enhanced virulence
was reported in some (Yamaguchi et al. 2014) but the opposite was also observed in the other
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(Rosado et al. 2010). Moreover, recent reports on the ―confusion‖ of human immunity T cells
under microgravity conditions implies a heightened risk of microbial infection during space
travel (Crucian et al. 2015). Weakening of human immunity during space travel could cause
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much severe consequences from S. aureus infection, which could result in diseases such as
metastatic infection, and sepsis that are more commonly observed under health care settings.
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Furthermore, there are diverse other bacteria isolated from ISS beside S. aureus including those
can cause opportunistic infections to further increase the microbial risk during space travel.
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Continuing research in this venue will, thus, improve the risk estimation. Therefore, the
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outcomes of this study provide a screening level quantitative estimate of S. aureus risk infection
during space travel based on the best current knowledge.
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5. Conclusions

Data mining of ISS microbial observatory results indicates bacterial contamination on
ISS interior surfaces varies widely from one location to another but Staphylococcus and S.
aureus are important bacterial contaminants on the ISS surfaces.
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
Skin cleaning regimes are an important strategy to reduce S. aureus colonization on skin
surface and skin infection.

Although there are degrees of uncertainties in the risk outcome due to the limitation on
data and models, the sensitivity analysis indicate contact rate with contaminated surfaces

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and time elapsed between each cleaning are important factors influence the risk outcome.
To further improve the accuracy of skin infection risk prediction, research to understand
microbial virulence under microgravity condition, changes in human immunity during
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space travel and burden of microbial pathogens are needed.
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Acknowledgements
Funding for this research was partially provided by 2012 Space Biology NNH12ZTT001N grant
no. 19-12829-26 under Task Order NNN13D111T award to K.J.V. and UCInspire Program to
S.J.. Data analysis and risk assessment was carried out by S.X. at UCI under the supervision of
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S.J. ISS Microbial Observatory data was collected at the Jet Propulsion Laboratory, California
Institute of Technology, under a contract with NASA. We would like to thank astronaut Captain
Terry Virts and J. Williams for collecting samples aboard the ISS, and the Implementation Team
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at NASA Ames Research Center including project scientist Dr. Fathi Karouia for coordinating
this effort. Government sponsorship acknowledged.
Competing Interests
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Authors declare no competing interests.
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Table 1. Description of the parameters used in the risk assessment.
Description
Unit
Symbol
Bacterial density on ISS surfaces
cfu/m2
Portion of S. aureus among total
cultivable bacteria on the ISS
surface
Crew members’ behaviour
Point
estimate
Probability
distribution
Reference
Ctotal
Empirical
cfu/cfu
PS. aureus
Empirical
(Venkateswaran
2017)
(Venkateswaran
2017)
NA
ncontact
NA
tcontact
NA
tcleaning
Bacteria transfer model
μ
Transfer rate from ISS surface
(stainless steel) to crews' skin
Reduction of S. aureus after
cleaning
Bacterial growth model
logcfu
Maximum microorganism density
cfu/cm2
Nmax
8,930,893
1/min
k1
0.105
1/min
k2
0.0383
cm2/days·cfu
k3
3.2136×10-7
r
7.63×10-8
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Initial inactivation rate constant
Rate constant for decrease in
inactivation
Growth rate constant
w
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%
144-720
Uniform
0-12h
Uniform
0-12h(once);
0-4h and 8-12h
(twice)
Uniform
This study
5.3±1.1
Normal
2.0±0.4
Normal
(Arinder et al.
2016)
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Contact frequency per person per
day (pppd)
Possible period of exposure during
the day*
Possible period of cleaning during
the day*
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S. aureus density on ISS
This study
This study
(Rose and Haas
1999)
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Dose-response relationship (exponential function)
cm2/days·cfu
(Rose and Haas
1999)
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Best-fit infectivity factor
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Start
S. aureus
density on ISS
Frac on of S.
aureus
Repe ve exposure
during ac vity hours
Total cultured
bact. density
No cleaning
Growth
Cleaning
Risk
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Dose
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Exposure
Figure 1. The flow of information in the QMRA.
4h
8h
Wake up
Daily r outine
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Cleaning r outine
20h
24h
Wake up
Sleeping hour s
Cleaning at any timepoint
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Cleaning
Cleaning
Random exposur e and bacter ial gr owth with inter r uption fr om
Uninter r upted Bacter ial gr owth
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Bacter ial gr owth
and exposur e
scenar ios
16h
Activities hour s
Once per day
Twice per day
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Figure 2. Flight crew daily activity that influence the exposure assessment.
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Figure 3. Bacterial distribution on ISS interior surfaces retrieved by surface wipe and culture-
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based assay (using data collected by reference (Venkateswaran 2017)).
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Figure 4. Bacteria identified among randomly selected isolates from eight sample locations on
ISS and the percentage of Staphylococcus aureus among total bacteria expressed as empirical
cumulative probability distribution (using data collected by reference (Venkateswaran 2017)).
The details of sampling locations in ISS are presented in (Venkateswaran 2017).
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Figure 5. Comparison of skin surface S. aureus density (a, b, c) and the risk of skin infection (d,
e, f) during a seven-day space mission with no cleaning (a, d), or skin cleaning at once per day (b,
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e), or twice per day (c, f). The blue curve denotes the median value, green region denotes the
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interquartile range, yellow region denotes the 10th to 90th percentile range, and the red denotes
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the outliers.
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Figure 6. Sensitivity analysis of relative contribution of input parameters to the risk outcome
under no-cleaning and skin cleaning once per day scenarios.
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Risk of skin infection on ISS
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Graphical abstract
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