pith. sign in

arxiv: 2606.25728 · v1 · pith:PD2APYKPnew · submitted 2026-06-24 · 💻 cs.IR

A Stochastic Epidemiological Model of Latent Tuberculosis in a Radiation Exposed Mars Colony

Teddy Lazebnik This is my paper

Pith reviewed 2026-06-25 19:09 UTC · model grok-4.3

classification 💻 cs.IR
keywords latent tuberculosisMars colonystochastic modelradiation effectsimmune competencereactivationadaptive controlepidemiological simulation
0
0 comments X

The pith

A stochastic model shows latent tuberculosis can reactivate endogenously in a Mars colony due to radiation effects on immunity, even without initial infectious cases.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a stochastic model that connects galactic cosmic radiation exposure to reduced immune competence, which then elevates the chance that latent tuberculosis infections reactivate into active, transmissible disease inside a closed habitat. Simulations demonstrate that active cases and outbreaks can arise spontaneously from a latent reservoir even when the population starts with no infectious individuals. The model also casts the choice of isolation and medication as a sequential decision task solved either by fixed rules or by a reinforcement-learning policy, and finds that the adaptive policy lowers total cases and deaths while avoiding excess intervention. This addresses a concrete operational risk for sustained human presence on Mars, where medical autonomy is limited and environmental stressors are chronic.

Core claim

We develop a stochastic host-radiation-pathogen-habitat model of latent tuberculosis reactivation in a Mars colony. The model links galactic cosmic radiation to immune competence, immune competence to latent-tuberculosis reactivation, and reactivation to airborne transmission in a closed habitat. We also formulate countermeasure allocation as a partially observable sequential decision problem in which isolation and medication are selected by fixed baselines or by a proximal policy optimization policy trained on an agent-based simulator. Our simulations show that active tuberculosis can emerge endogenously despite no initial infectious cases, and that risk is most sensitive to latent reservoi

What carries the argument

Stochastic epidemiological model that couples galactic cosmic radiation dose to immune competence and then to latent TB reactivation probability, embedded in an agent-based simulator with a proximal policy optimization agent for adaptive isolation and treatment decisions.

If this is right

  • Active tuberculosis can appear endogenously from a latent reservoir even when no individuals start infectious.
  • Outbreak probability is most sensitive to the size of the latent TB reservoir, the radiation-immune coupling strength, and reactivation sensitivity.
  • Adaptive policies for isolation and medication outperform fixed baselines by lowering infectious burden and mortality with fewer interventions.
  • The framework permits pre-launch stress testing of screening, monitoring, shielding, and treatment strategies under Mars-specific conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Pre-mission treatment to shrink the latent TB reservoir could materially lower in-mission outbreak risk.
  • Analogous models could be applied to other latent infections or radiation-sensitive conditions in long-duration spaceflight.
  • Terrestrial validation of the radiation-to-reactivation relationships would increase confidence in mission planning use.
  • The adaptive control approach could extend to other scarce-resource medical decisions in isolated habitats.

Load-bearing premise

The specific functional relationships that convert radiation dose into reduced immune competence and then into higher latent TB reactivation rates are taken as given without independent calibration or validation data.

What would settle it

Direct empirical measurements of TB reactivation rates in humans or animal models under chronic radiation exposure levels matching Mars transit and surface conditions would test the accuracy of the modeled couplings.

Figures

Figures reproduced from arXiv: 2606.25728 by Teddy Lazebnik.

Figure 1
Figure 1. Figure 1: A schematic view of the transition between epidemiological states. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A schematic view of the experimental design. [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Uncontrolled baseline trajectories. The colony starts with latent tuberculosis carriers but no progressive [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the time to the first active tuberculosis case under the uncontrolled baseline scenario. The [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Policy trajectories for infectious cases, deaths, cumulative active incidence, and control actions. [PITH_FULL_IMAGE:figures/full_fig_p024_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: One-at-a-time sensitivity heatmaps. Color indicates the percentage change relative to the baseline [PITH_FULL_IMAGE:figures/full_fig_p025_6.png] view at source ↗
read the original abstract

Plans to establish a sustained human presence on Mars have moved from speculative ambition toward concrete engineering programmes, making the biological consequences of settlement an increasingly practical question. A Mars colony would place a small, closed population in an environment combining chronic radiation, altered immunity, constrained medical autonomy, and engineered indoor air. Latent infections are especially important because clinically silent carriers may become sources of transmissible disease when host control deteriorates. In this study, we develop a stochastic host-radiation-pathogen-habitat model of latent tuberculosis reactivation in a Mars colony. The model links galactic cosmic radiation to immune competence, immune competence to latent-tuberculosis reactivation, and reactivation to airborne transmission in a closed habitat. We also formulate countermeasure allocation as a partially observable sequential decision problem in which isolation and medication are selected by fixed baselines or by a proximal policy optimization policy trained on an agent-based simulator. Our simulations show that active tuberculosis can emerge endogenously despite no initial infectious cases, and that risk is most sensitive to latent reservoir size, radiation-immune coupling and reactivation sensitivity. Adaptive control reduced infectious burden and mortality while limiting unnecessary intervention. This framework supports mission-specific stress testing of screening, monitoring, shielding and treatment strategies before launch.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript develops a stochastic host-radiation-pathogen-habitat model coupling galactic cosmic radiation to immune competence, immune competence to latent TB reactivation probability, and reactivation to airborne transmission within a closed Mars colony. Agent-based simulations are used to show endogenous emergence of active TB from latent reservoirs with no initial infectious cases, to rank sensitivities to latent reservoir size, radiation-immune coupling strength, and reactivation sensitivity, and to demonstrate that a proximal policy optimization controller trained on the simulator reduces infectious burden and mortality relative to fixed baselines while limiting unnecessary interventions.

Significance. If the core functional mappings can be independently justified, the framework offers a concrete tool for pre-mission stress-testing of screening, shielding, and treatment policies under combined radiation and epidemiological stress. The integration of stochastic simulation with partially observable sequential decision-making via reinforcement learning is a constructive methodological step for closed-environment biosecurity problems.

major comments (3)
  1. [Model Formulation] Model description (implicit in abstract and methods): the functional forms that map galactic cosmic radiation dose to reduced immune competence and then to elevated latent-TB reactivation probability are introduced as given without derivation from empirical radiation-immunology data, TB reactivation studies, or cross-validation against known terrestrial or analog conditions. Because the reported sensitivity rankings and the endogenous-emergence result are driven by these mappings, any mismatch with actual biology would invalidate the central claims.
  2. [Simulation Results] Simulation results section: no parameter sources, ranges, or sensitivity-analysis protocol (e.g., partial-rank correlation coefficients, Sobol indices) are supplied, nor is any validation against real TB incidence or radiation-immune datasets described. Consequently the statement that risk is “most sensitive to latent reservoir size, radiation-immune coupling and reactivation sensitivity” cannot be verified from the supplied text.
  3. [Adaptive Control Experiments] Adaptive-control experiments: the claim that the PPO policy outperforms fixed baselines rests on the same uncalibrated couplings; without a robustness check that varies the radiation-immune and reactivation functions, it is unclear whether the reported reduction in burden and mortality generalizes beyond the specific assumed forms.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by a single sentence stating the mathematical form of the radiation-immune and reactivation mappings or by citing the supplementary equations that define them.
  2. [Model Formulation] Notation for immune competence and reactivation probability should be introduced once with explicit symbols rather than repeated descriptive phrases.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive critique. The comments correctly identify gaps in justification, documentation, and robustness testing that must be addressed for the claims to be verifiable. We outline point-by-point revisions below.

read point-by-point responses

  1. Referee: [Model Formulation] Model description (implicit in abstract and methods): the functional forms that map galactic cosmic radiation dose to reduced immune competence and then to elevated latent-TB reactivation probability are introduced as given without derivation from empirical radiation-immunology data, TB reactivation studies, or cross-validation against known terrestrial or analog conditions. Because the reported sensitivity rankings and the endogenous-emergence result are driven by these mappings, any mismatch with actual biology would invalidate the central claims.

    Authors: We agree the functional forms require explicit derivation. The revised manuscript will add a dedicated subsection in Methods that cites specific radiation-immunology and TB latency studies, states the exact functional forms chosen, and discusses the terrestrial-to-Mars extrapolations and their uncertainties. revision: yes

  2. Referee: [Simulation Results] Simulation results section: no parameter sources, ranges, or sensitivity-analysis protocol (e.g., partial-rank correlation coefficients, Sobol indices) are supplied, nor is any validation against real TB incidence or radiation-immune datasets described. Consequently the statement that risk is “most sensitive to latent reservoir size, radiation-immune coupling and reactivation sensitivity” cannot be verified from the supplied text.

    Authors: We accept that parameter provenance and sensitivity protocol are absent. The revision will include a table of all parameter sources and ranges, a global sensitivity analysis using Sobol indices, and direct comparisons of model outputs against published terrestrial TB incidence and radiation-immune datasets. revision: yes

  3. Referee: [Adaptive Control Experiments] Adaptive-control experiments: the claim that the PPO policy outperforms fixed baselines rests on the same uncalibrated couplings; without a robustness check that varies the radiation-immune and reactivation functions, it is unclear whether the reported reduction in burden and mortality generalizes beyond the specific assumed forms.

    Authors: We agree that robustness to the assumed couplings must be shown. The revised experiments section will add a sensitivity sweep over the radiation-immune and reactivation parameters and report PPO performance statistics across those variations. revision: yes

Circularity Check

0 steps flagged

No circularity detected; model is simulation-based with explicit assumptions

full rationale

The paper constructs a stochastic epidemiological model by specifying functional links from galactic cosmic radiation to immune competence and from immune competence to latent-TB reactivation probability. These mappings are introduced as model components rather than derived via equations or self-citations that reduce the outputs to the inputs by construction. Simulations then generate endogenous emergence, sensitivity rankings, and control outcomes from the defined model. No quoted step shows a fitted parameter renamed as a prediction, a self-definitional loop, or a load-bearing self-citation chain. The derivation chain is the forward simulation itself and remains independent of the reported results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; therefore the ledger cannot enumerate specific free parameters, axioms, or invented entities with citations to sections or equations.

pith-pipeline@v0.9.1-grok · 5735 in / 1056 out tokens · 22159 ms · 2026-06-25T19:09:26.188103+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

76 extracted references · 1 linked inside Pith

  1. [1]

    Scott, Matthew J

    Ebrahim Afshinnekoo, Ryan T. Scott, Matthew J. MacKay, Eloise Pariset, Egle Cekanaviciute, Richard Barker, Simon Gilroy, Duane Hassane, Scott M. Smith, Sara R. Zwart, et al. Fundamental biological features of spaceflight: Advancing the field to enable deep-space exploration.Cell, 183(5):1162–1184.e1, 2020

    2020

  2. [2]

    Wiley Online Library, 2022

    V olker Hessel, Jana Stoudemire, Hideaki Miyamoto, and ID Fisk.In-Space Manufacturing and Resources. Wiley Online Library, 2022

    2022

  3. [3]

    Towards sustainable horizons: A comprehensive blueprint for mars colonization.Heliyon, 10(4), 2024

    Florian Neukart. Towards sustainable horizons: A comprehensive blueprint for mars colonization.Heliyon, 10(4), 2024. Draft: June 25, 2026 19

    2024

  4. [4]

    Infections in long-duration space missions.The Lancet Microbe, 5(9), 2024

    Daniel Cowen, Rulan Zhang, and Matthieu Komorowski. Infections in long-duration space missions.The Lancet Microbe, 5(9), 2024

    2024

  5. [5]

    Infectious diseases and the use of antimicrobials on space missions.Space: Science & Technology, 4:0205, 2024

    Alice RC e Castro-Costa, Rodrigo Siqueira-Batista, Fab ´ıola A Alcˆantara, Tha´ıs Russomano, Marlise A San- tos, Isadora de C e Silva, and Oswaldo M Del Cima. Infectious diseases and the use of antimicrobials on space missions.Space: Science & Technology, 4:0205, 2024

    2024

  6. [6]

    Evidence supporting the management of medical conditions during long-duration spaceflight: Protocol for a scoping review.JMIR Research Protocols, 10(3):e24323, 2021

    Kim-Anh Tran, Neal William Pollock, Caroline Rh ´eaume, Payal Sonya Razdan, F´elix-Antoine Fortier, Lara Dutil-Fafard, Camille Morin, David Pierre-Marie Monnot, Maxime Huot-Lavoie, Philippe Simard-Sauriol, et al. Evidence supporting the management of medical conditions during long-duration spaceflight: Protocol for a scoping review.JMIR Research Protocols...

    2021

  7. [7]

    Effects of simulated martian environmental stressors on specific human pathogen–immune system interactions.Mbio, 16(9):e01099–25, 2025

    Tommaso Zaccaria, ¨Ozlem Bulut, Anaisa V Ferreira, Margo Dona, Jeroen D Langereis, Rob J Mesman, Joppe Wesseling, Laura van Niftrik, Mihai G Netea, Petra Rettberg, et al. Effects of simulated martian environmental stressors on specific human pathogen–immune system interactions.Mbio, 16(9):e01099–25, 2025

    2025

  8. [8]

    From earth to mars: a perspective on exploiting biomineralization for martian construction.Frontiers in Microbiology, 16:1645014, 2025

    Shiva Khoshtinat, Jared Long-Fox, and Seyed Mohammad Javad Hosseini. From earth to mars: a perspective on exploiting biomineralization for martian construction.Frontiers in Microbiology, 16:1645014, 2025

    2025

  9. [9]

    Perras, Gregor Gorkiewicz, Gabriele Berg, and Christine Moissl-Eichinger

    Maximilian Mora, Alexander Mahnert, Karin Koskinen, Michelle-Ruth Pausan, Lisa Oberauner-Wappis, Regina Krause, Alexandra K. Perras, Gregor Gorkiewicz, Gabriele Berg, and Christine Moissl-Eichinger. Microorganisms in confined habitats: Microbial monitoring and control of intensive care units, operating rooms, cleanrooms and the international space station...

    2016

  10. [10]

    High resolution spatio-temporal model for room-level airborne pandemic spread.Mathematics, 11(2):426, 2023

    Teddy Lazebnik and Ariel Alexi. High resolution spatio-temporal model for room-level airborne pandemic spread.Mathematics, 11(2):426, 2023

    2023

  11. [11]

    The trade-off between airborne pandemic control and energy consumption using air ventilation solutions.Sensors, 22(22):8594, 2022

    Ariel Alexi, Ariel Rosenfeld, and Teddy Lazebnik. The trade-off between airborne pandemic control and energy consumption using air ventilation solutions.Sensors, 22(22):8594, 2022

    2022

  12. [12]

    Slaba, Charles M

    Tony C. Slaba, Charles M. Werneth, Cary Zeitlin, et al. Validated space radiation exposure predictions from earth to mars during artemis-i.npj Microgravity, 11:6, 2025

    2025

  13. [13]

    L. W. Townsend, J. H. Adams, S. R. Blattnig, M. S. Clowdsley, D. J. Fry, I. Jun, C. D. McLeod, J. I. Minow, D. F. Moore, J. W. Norbury, R. B. Norman, D. V . Reames, N. A. Schwadron, E. J. Semones, R. C. Singleterry, T. C. Slaba, C. M. Werneth, and M. A. Xapsos. Solar particle event storm shelter requirements for missions beyond low earth orbit.Life Scienc...

    2018

  14. [14]

    Hassler, Cary Zeitlin, Robert F

    Donald M. Hassler, Cary Zeitlin, Robert F. Wimmer-Schweingruber, Bent Ehresmann, Scot Rafkin, Jen- nifer L. Eigenbrode, David E. Brinza, G ¨unther Weigle, Stephan B ¨ottcher, Eckart B ¨ohm, et al. Mars’ surface radiation environment measured with the mars science laboratory’s curiosity rover.Science, 343(6169):1244797, 2014

    2014

  15. [15]

    Ann R. Kennedy. Biological effects of space radiation and development of effective countermeasures.Life Sciences in Space Research, 1:10–43, 2014

    2014

  16. [16]

    Hassler, Francis A

    Cary Zeitlin, Donald M. Hassler, Francis A. Cucinotta, Bent Ehresmann, Robert F. Wimmer-Schweingruber, David E. Brinza, Sang Y . Kang, G¨unther Weigle, Stephan B ¨ottcher, Eckart B ¨ohm, et al. Measurements of energetic particle radiation in transit to mars on the mars science laboratory.Science, 340(6136):1080–1084, 2013

    2013

  17. [17]

    Winer, Hongying Du, JangKeun Kim, et al

    Daniel A. Winer, Hongying Du, JangKeun Kim, et al. Astroimmunology: The effects of spaceflight and its associated stressors on the immune system.Nature Reviews Immunology, 26:189–212, 2026

    2026

  18. [18]

    NASA-STD-3001 Technical Brief: Microbiology in Space Overview

    NASA Office of the Chief Health and Medical Officer. NASA-STD-3001 Technical Brief: Microbiology in Space Overview. Technical brief, 2024. Draft: June 25, 2026 20

    2024

  19. [19]

    Spaceflight-induced immune system dysregulation and microgravity- associated alterations in microbial virulence – infectious disease risk for astronauts

    NASA Human Research Program. Spaceflight-induced immune system dysregulation and microgravity- associated alterations in microbial virulence – infectious disease risk for astronauts. Human Research Roadmap task page, 2026

    2026

  20. [20]

    Blue, Robert A

    Rebecca S. Blue, Robert A. Mulcahy, Amy J. Kreykes, Robert Haddon, James M. Pattarini, Benjamin D. Johansen, and Rahul Suresh. Infectious disease outcomes of nasa’s health stabilization program.npj Micro- gravity, 2026

    2026

  21. [21]

    Antonsen, Jerry G

    Erik L. Antonsen, Jerry G. Myers, Lynn Boley, John Arellano, Eric Kerstman, Binaifer Kadwa, Daniel M. Buckland, and Mary Van Baalen. Estimating medical risk in human spaceflight.npj Microgravity, 8(1), 2022

    2022

  22. [22]

    Prelich, Clara M

    Matthew T. Prelich, Clara M. Gasiewski, Lauren McIntyre, Binaifer Kadwa, John Arellano, and Jerry G. Myers. Assessment of model outcomes between the integrated medical model (imm) and the medical ex- tensible dynamic probabilistic risk assessment tool (medprat). Technical Report NASA/TM-20240012058, National Aeronautics and Space Administration, 2024

    2024

  23. [23]

    Menzies, Emory Wolf, David Connors, Meghan Bellerose, Alyssa N

    Nicolas A. Menzies, Emory Wolf, David Connors, Meghan Bellerose, Alyssa N. Sbarra, Ted Cohen, An- drew N. Hill, Reza Yaesoubi, Kara Galer, Peter J. White, Ibrahim Abubakar, and Joshua A. Salomon. Pro- gression from latent infection to active disease in dynamic tuberculosis transmission models: A systematic review of the validity of modelling assumptions.T...

    2018

  24. [24]

    Stepanov, Quyen Tran, Jason M

    Aleksandra Checinska Sielaff, Camilla Urbaniak, Ganesh Babu Malli Mohan, Victor G. Stepanov, Quyen Tran, Jason M. Wood, Jeremiah Minich, Daniel McDonald, Tasha Mayer, Rob Knight, et al. Characteriza- tion of the total and viable bacterial and fungal communities associated with the international space station surfaces.Microbiome, 7:50, 2019

    2019

  25. [25]

    V oorhies, C

    Alexander A. V oorhies, C. Mark Ott, Satish Mehta, Duane L. Pierson, Brian E. Crucian, Alan Feiveson, Cherie M. Oubre, Manolito Torralba, Kevin Moncera, Yi Zhang, et al. Study of the impact of long-duration space missions at the international space station on the astronaut microbiome.Scientific Reports, 9:9911, 2019

    2019

  26. [26]

    Mehta, Brian E

    Satish K. Mehta, Brian E. Crucian, Raymond P. Stowe, Richard J. Simpson, C. Mark Ott, Clarence F. Sams, and Duane L. Pierson. Latent virus reactivation in astronauts on the international space station.npj Micro- gravity, 3:11, 2017

    2017

  27. [27]

    Rooney, Brian E

    Bridgette V . Rooney, Brian E. Crucian, Duane L. Pierson, Mark L. Laudenslager, and Satish K. Mehta. Her- pes virus reactivation in astronauts during spaceflight and its application on earth.Frontiers in Microbiology, 10:16, 2019

    2019

  28. [28]

    Latent tuberculosis infection: Updated and consolidated guidelines for program- matic management, 2018

    World Health Organization. Latent tuberculosis infection: Updated and consolidated guidelines for program- matic management, 2018

    2018

  29. [29]

    Clinical overview of latent tuberculosis infection

    Centers for Disease Control and Prevention. Clinical overview of latent tuberculosis infection. Web page, 2024

    2024

  30. [30]

    Tuberculosis: Causes and how it spreads

    Centers for Disease Control and Prevention. Tuberculosis: Causes and how it spreads. Web page, 2025

    2025

  31. [31]

    Behr, David Dowdy, Keertan Dheda, Maziar Divangahi, Catharina C

    Madhukar Pai, Marcel A. Behr, David Dowdy, Keertan Dheda, Maziar Divangahi, Catharina C. Boehme, Ann Ginsberg, Soumya Swaminathan, Melvin Spigelman, Haileyesus Getahun, Dick Menzies, and Mario Raviglione. Tuberculosis.Nature Reviews Disease Primers, 2:16076, 2016

    2016

  32. [32]

    Behr, Paul H

    Marcel A. Behr, Paul H. Edelstein, and Lalita Ramakrishnan. Quantifying the rates of late reactivation tuberculosis: A systematic review.The Lancet Infectious Diseases, 2021. Draft: June 25, 2026 21

    2021

  33. [33]

    Rangaka, Katherine A

    Molebogeng X. Rangaka, Katherine A. Wilkinson, Judith R. Glynn, Dena Ling, Dick Menzies, Judith Mwansa-Kambafwile, Katherine Fielding, Robert J. Wilkinson, and Madhukar Pai. Predictive value of interferon-γrelease assays for incident active tuberculosis: A systematic review and meta-analysis.The Lancet Infectious Diseases, 12(1):45–55, 2012

    2012

  34. [34]

    Garrett-Bakelman, Manjula Darshi, Stefan J

    Francine E. Garrett-Bakelman, Manjula Darshi, Stefan J. Green, Ruben C. Gur, Ling Lin, Brandon R. Ma- cias, Miles J. McKenna, Cem Meydan, Tejaswini Mishra, Jad Nasrini, et al. The nasa twins study: A multidimensional analysis of a year-long human spaceflight.Science, 364(6436):eaau8650, 2019

    2019

  35. [35]

    Tierney, Eliah G

    Braden T. Tierney, Eliah G. Overbey, et al. Longitudinal multi-omics analysis of host microbiome architec- ture and immune responses during short-term spaceflight.Nature Microbiology, 2024

    2024

  36. [36]

    Tierney, Eliah G

    JangKeun Kim, Braden T. Tierney, Eliah G. Overbey, Ezequiel Dantas, Matias Fuentealba, Jiwoon Park, S. Anand Narayanan, Fei Wu, Deena Najjar, Christopher R. Chin, et al. Single-cell multi-ome and immune profiles of the inspiration4 crew reveal conserved, cell-type, and sex-specific responses to spaceflight.Nature Communications, 2024

    2024

  37. [37]

    Salido, Haoqi Nina Zhao, Daniel McDonald, Helena Mannochio-Russo, Simone Zuffa, Re- nee E

    Rodolfo A. Salido, Haoqi Nina Zhao, Daniel McDonald, Helena Mannochio-Russo, Simone Zuffa, Re- nee E. Oles, Allegra T. Aron, Yasin El Abiead, Sawyer Farmer, Antonio Gonz ´alez, et al. The international space station has a unique and extreme microbial and chemical environment driven by use patterns.Cell, 188(7):2022–2041.e23, 2025

    2022

  38. [38]

    Singh et al

    Nitin K. Singh et al. Genomic, functional, and metabolic enhancements in multidrug-resistantEnterobacter bugandensisfacilitating its persistence and succession in the international space station.Microbiome, 2024

    2024

  39. [39]

    Broddrick

    Iris Irby and Jared T. Broddrick. Microbial adaptation to spaceflight is correlated with bacteriophage-encoded functions.Nature Communications, 15(1), 2024

    2024

  40. [40]

    Hardy, Corey A

    John G. Hardy, Corey A. Theriot, Thomas Oswald, and Gilles Cl ´ement. Spaceflight standard measures is a multidisciplinary study that systematically monitors risks to astronaut health and performance.npj Microgravity, 2025

    2025

  41. [41]

    Spaceflight and medical microbiology: Possible implications for standard infection diag- nostics and therapy.Life, 15(11):1757, 2025

    Alessa Lalinka Boschert, Stefan Leuko, Carolin Luisa Kr ¨amer, Katharina Siems, Yen-Tran Ly-Sauerbrey, and Franca Arndt. Spaceflight and medical microbiology: Possible implications for standard infection diag- nostics and therapy.Life, 15(11):1757, 2025

    2025

  42. [42]

    Kermack and Anderson G

    William O. Kermack and Anderson G. McKendrick. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, 115(772):700–721, 1927

    1927

  43. [43]

    Computational applications of extended SIR models: A review focused on airborne pan- demics.Ecological Modelling, 483:110422, 2023

    Teddy Lazebnik. Computational applications of extended SIR models: A review focused on airborne pan- demics.Ecological Modelling, 483:110422, 2023

    2023

  44. [44]

    Hethcote

    Herbert W. Hethcote. The mathematics of infectious diseases.SIAM Review, 42(4):599–653, 2000

    2000

  45. [45]

    Springer, New York, 2019

    Fred Brauer, Carlos Castillo-Chavez, and Zhilan Feng.Mathematical Models in Epidemiology. Springer, New York, 2019

    2019

  46. [46]

    Pandemic management by a spatio- temporal mathematical model.International Journal of Nonlinear Sciences and Numerical Simulation, 24(6):2307–2324, 2023

    Teddy Lazebnik, Svetlana Bunimovich-Mendrazitsky, and Labib Shami. Pandemic management by a spatio- temporal mathematical model.International Journal of Nonlinear Sciences and Numerical Simulation, 24(6):2307–2324, 2023

    2023

  47. [47]

    Sanzari, X

    Jenine K. Sanzari, X. Steven Wan, Amy Muehlmatt, Liyong Lin, and Ann R. Kennedy. Comparison of changes over time in leukocyte counts in yucatan minipigs irradiated with simulated solar particle event-like radiation.Life Sciences in Space Research, 4:11–16, 2015

    2015

  48. [48]

    David R. Cox. Regression models and life-tables.Journal of the Royal Statistical Society: Series B, 34(2):187–202, 1972. Draft: June 25, 2026 22

    1972

  49. [49]

    Tuberculosis infection control

    Centers for Disease Control and Prevention. Tuberculosis infection control. Web page, 2023

    2023

  50. [50]

    Who consolidated guidelines on tuberculosis: Module 4: Treatment and care, 2025

    World Health Organization. Who consolidated guidelines on tuberculosis: Module 4: Treatment and care, 2025

    2025

  51. [51]

    Spatio-temporal influence of non- pharmaceutical interventions policies on pandemic dynamics and the economy: the case of COVID-19

    Teddy Lazebnik, Labib Shami, and Svetlana Bunimovich-Mendrazitsky. Spatio-temporal influence of non- pharmaceutical interventions policies on pandemic dynamics and the economy: the case of COVID-19. Economic Research-Ekonomska Istrazivanja, 35(1):1833–1861, 2022

    2022

  52. [52]

    A hybrid mathematical model for an optimal border closure policy during a pandemic.International Journal of Applied Mathematics and Computer Science, 33(4):583–601, 2023

    Teddy Lazebnik, Labib Shami, and Svetlana Bunimovich-Mendrazitsky. A hybrid mathematical model for an optimal border closure policy during a pandemic.International Journal of Applied Mathematics and Computer Science, 33(4):583–601, 2023

    2023

  53. [53]

    Teddy Lazebnik. Data-driven hospitals staff and resources allocation using agent-based simulation and deep reinforcement learning.Engineering Applications of Artificial Intelligence, 126:106783, 2023

    2023

  54. [54]

    Keeling and Pejman Rohani.Modeling Infectious Diseases in Humans and Animals

    Matthew J. Keeling and Pejman Rohani.Modeling Infectious Diseases in Humans and Animals. Princeton University Press, Princeton, NJ, 2008

    2008

  55. [55]

    Optimal control in epidemiology.Annals of Operations Research, 251(1–2):55–71, 2017

    Oluwaseun Sharomi and Tufail Malik. Optimal control in epidemiology.Annals of Operations Research, 251(1–2):55–71, 2017

    2017

  56. [56]

    Workman.Optimal Control Applied to Biological Models

    Suzanne Lenhart and John T. Workman.Optimal Control Applied to Biological Models. Chapman and Hall/CRC, Boca Raton, FL, 2007

    2007

  57. [57]

    Odo Diekmann, J. A. P. Heesterbeek, and M. G. Roberts. The construction of next-generation matrices for compartmental epidemic models.Journal of the Royal Society Interface, 7(47):873–885, 2010

    2010

  58. [58]

    Adi Shuchami and Teddy Lazebnik. Spatio-temporal SIR model of pandemic spread during warfare with optimal dual-use health care system administration using deep reinforcement learning.Disaster Medicine and Public Health Preparedness, 19:e197, 2025

    2025

  59. [59]

    Agent-based modeling: Methods and techniques for simulating human systems.Proceedings of the National Academy of Sciences, 99:7280–7287, 2002

    Eric Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems.Proceedings of the National Academy of Sciences, 99:7280–7287, 2002

    2002

  60. [60]

    Littman, and Anthony R

    Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra. Planning and acting in partially observable stochastic domains.Artificial Intelligence, 101(1–2):99–134, 1998

    1998

  61. [61]

    Sutton and Andrew G

    Richard S. Sutton and Andrew G. Barto.Reinforcement Learning: An Introduction. MIT Press, 2 edition, 2018

    2018

  62. [62]

    Proximal policy optimiza- tion algorithms.arXiv preprint arXiv:1707.06347, 2017

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimiza- tion algorithms.arXiv preprint arXiv:1707.06347, 2017

    Pith/arXiv arXiv 2017

  63. [63]

    A security games inspired approach for distributed control of pandemic spread.Advanced Theory and Simulations, 6(2):2200631, 2023

    Ariel Alexi, Ariel Rosenfeld, and Teddy Lazebnik. A security games inspired approach for distributed control of pandemic spread.Advanced Theory and Simulations, 6(2):2200631, 2023

    2023

  64. [64]

    Y . Ma, C. R. Horsburgh, L. F. White, and H. E. Jenkins. Quantifying tb transmission: A systematic re- view of reproduction number and serial interval estimates for tuberculosis.Epidemiology and Infection, 146(12):1478–1494, 2018

    2018

  65. [65]

    Treatment for drug-susceptible tuberculosis disease

    Centers for Disease Control and Prevention. Treatment for drug-susceptible tuberculosis disease. Web page, 2025

    2025

  66. [66]

    Global tuberculosis report 2024, 2024

    World Health Organization. Global tuberculosis report 2024, 2024

    2024

  67. [67]

    Linda J. S. Allen. A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis. Infectious Disease Modelling, 2(2):128–142, 2017. Draft: June 25, 2026 23

    2017

  68. [68]

    Stable-baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Re- search, 22(268):1–8, 2021

    Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. Stable-baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Re- search, 22(268):1–8, 2021

    2021

  69. [69]

    Lloyd-Smith, Sebastian J

    James O. Lloyd-Smith, Sebastian J. Schreiber, P. Ekkehard Kopp, and Wayne M. Getz. Superspreading and the effect of individual variation on disease emergence.Nature, 438(7066):355–359, 2005

    2005

  70. [70]

    Challenges of real-world reinforcement learn- ing: Definitions, benchmarks and analysis.Machine Learning, 110:2419–2468, 2021

    Gabriel Dulac-Arnold, Daniel Mankowitz, and Todd Hester. Challenges of real-world reinforcement learn- ing: Definitions, benchmarks and analysis.Machine Learning, 110:2419–2468, 2021

    2021

  71. [71]

    Crucian, Alexander Chouk `er, Richard J

    Brian E. Crucian, Alexander Chouk `er, Richard J. Simpson, Satish Mehta, Gail Marshall, Scott M. Smith, Sara R. Zwart, Martina Heer, Sergey Ponomarev, Alexandra Whitmire, Jean-Pol Frippiat, Grace L. Dou- glas, Hernan Lorenzi, Judith-Irina Buchheim, Georgios Makedonas, Geoffrey S. Ginsburg, and C. Mark Ott. Immune system dysregulation during spaceflight: P...

    2018

  72. [72]

    Edward A. Nardell. Transmission and institutional infection control of tuberculosis.Cold Spring Harbor Perspectives in Medicine, 6(2):a018192, 2016

    2016

  73. [73]

    A. K. Deol, N. Shaikh, K. Middelkoop, M. Mohlamonyane, R. G. White, and N. McCreesh. Importance of ventilation and occupancy to Mycobacterium tuberculosis transmission rates in congregate settings.BMC Public Health, 22:1772, 2022

    2022

  74. [74]

    The effects of reward misspecification: Mapping and mitigating misaligned models

    Alexander Pan, Kush Bhatia, and Jacob Steinhardt. The effects of reward misspecification: Mapping and mitigating misaligned models. InInternational Conference on Learning Representations, 2022

    2022

  75. [75]

    Reinforcement learning for policymaking in epidemic con- trol: A scoping review.PLOS One, 21(6):e0351176, 2026

    Oleksandr Bolshov and Dmytro Chumachenko. Reinforcement learning for policymaking in epidemic con- trol: A scoping review.PLOS One, 21(6):e0351176, 2026

    2026

  76. [76]

    Module 4: treatment – drug- resistant tuberculosis treatment, 2022 update

    World Health Organization.WHO consolidated guidelines on tuberculosis. Module 4: treatment – drug- resistant tuberculosis treatment, 2022 update. World Health Organization, Geneva, 2022. Draft: June 25, 2026 24 0.00 1.10 2.21 3.31 4.42 5.52 6.63 7.73 8.84 9.94 11.05I combined_fixed isolation_only medication_only no_intervention ppo_learned 0.0 0.3 0.6 0.9...

    2022