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

2026-06-24

AI Summary

This study addresses the risk of latent tuberculosis reactivation and transmission during Mars colonization missions, driven by cosmic radiation and confined habitats. It presents the first stochastic epidemiological model integrating host immunity, radiation exposure, pathogen dynamics, and habitat-specific factors. The authors formulate infection control as a partially observable Markov decision process (POMDP) and develop an adaptive intervention framework combining agent-based simulation, stochastic differential equations, and proximal policy optimization (PPO). Results demonstrate that active tuberculosis can emerge endogenously on Mars even in the absence of initial infectious cases. The proposed reinforcement learning–driven adaptive strategy significantly reduces infection burden and mortality while avoiding excessive interventions.

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.

Problem

• latent tuberculosis
• Mars colony
• radiation exposure
• stochastic epidemiological model
• reactivation risk

Innovation

• stochastic epidemiological model
• latent tuberculosis reactivation
• radiation-immune coupling
• partially observable Markov decision process
• reinforcement learning for public health