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Record W4409823973 · doi:10.1101/2025.04.23.650113

COVID-19 in Space: Possible Health Risks and Preparedness Guidelines

2025· preprint· en· W4409823973 on OpenAlexafffund
Ishan Vashishat, Barnabe D. Assogba

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsKwantlen Polytechnic UniversityDouglas College
FundersKwantlen Polytechnic University
KeywordsCoronavirus disease 2019 (COVID-19)Preparedness2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Environmental healthSpace (punctuation)Environmental scienceBusinessMedicineMedical emergencyVirologyOutbreakComputer sciencePolitical sciencePathologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Background The COVID-19 pandemic of 2020 resulted in over 705 million infections and more than 7 million deaths worldwide. The virus primarily spreads through aerosol droplets released during breathing, coughing, or sneezing, leading to symptoms ranging from mild fever and cough to severe outcomes, including death. Given the high risk associated with COVID-19, understanding its behaviour in diverse geographical and environmental conditions is critical. Space exploration and tourism represent an emerging industry, projected to reach a market value of $1.8 trillion. With numerous space missions planned by space agencies such as NASA, SpaceX, and ISRO, it is vital to address potential health risks for astronauts and space tourists. Objective With the expansion of human exploration into space, there is an urgent need to assess the risks posed by COVID-19 in extraterrestrial environments. This study reviews existing literature on airborne infections in space, identifies key knowledge gaps, and enhances preparedness for potential COVID-19 outbreaks during space missions. Methods A systematic literature review was conducted to identify studies examining airborne infectious diseases in space and their health effects under microgravity. Databases searched included PubMed and NASA’s Open Data Portal. To compare these findings with Earth-based data, additional systematic reviews were performed to analyze the known effects of these diseases on Earth, using Pathogen Safety Data Sheets. A separate systematic review was conducted using PubMed to explore similarities between COVID-19 and the selected airborne infectious diseases. Using a comparative approach, disease effects observed on Earth and in space were analyzed to predict COVID-19’s potential behavior in microgravity. Existing guidelines for managing airborne diseases in space and on Earth were reviewed and compared to develop a set of preparedness recommendations for COVID-19 in space. Results The airborne infectious diseases occurring in space found in this study include Aspergillus fumigatus, Beauveria bassiana , Epstein-Barr Virus (EBV), Escherichia coli, Klebsiella pneumoniae infections , Pseudomonas aeruginosa , Roseolovirus (Human Herpesvirus 6 & 7), Salmonella Typhimurium infection , Serratia marcescens infection , Staphylococcus aureus, Staphylococcus epidermidis , and Varicella-Zoster Virus (VZV). The relationship between the aforementioned diseases and COVID-19 was used in regard to theorizing the effects of COVID-19 in space. Six Tentative effects of COVID-19 in a microgravity environment could be theorized in this study. Along with that, recommendations to improve the current space travel health guidelines have also been referred to. Conclusion The results of this study will change the course of human space exploration by assisting in the protection of space travelers and guiding the development of new designs for spacecraft that include extra safety features.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.123
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.123
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.008
Bibliometrics0.0080.004
Science and technology studies0.0020.003
Scholarly communication0.0050.007
Open science0.0060.005
Research integrity0.0110.007
Insufficient payload (model declined to judge)0.0130.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.363
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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