Machine Learning Applications in Biothreat Modeling: A Review of Bacillus anthracis Dispersion Prediction

Bioterrorism refers to the intentional release of viruses, bacteria, toxins, or fungi to induce panic, mass casualties, or severe economic disruption. Among potential biological threats, Bacillus anthracis, the causative agent of anthrax, has long been regarded as one of the most critical bioterrorism agents. This Gram-positive, spore-forming bacterium exists as vegetative cells and highly resilient spores capable of surviving for decades under extreme environmental conditions such as heat, radiation, desiccation, and pH fluctuations. Human infections typically arise from contact with contaminated animals or animal products, while airborne spores can be dispersed through wind or transported via contaminated surfaces. The increasing volume and complexity of biological datasets have accelerated the integration of machine learning (ML) into microbiology, enabling the development of predictive and mechanistic models for infectious agents. ML algorithms have been employed to simulate and forecast the spatiotemporal dynamics of B. anthracis under environmental and climate change scenarios, allowing identification of future high-risk outbreak regions. Furthermore, advanced deep-learning approaches such as convolutional neural networks combined with holographic imaging have demonstrated rapid, label-free detection of anthrax spores. Collectively, existing research highlights the growing potential of artificial intelligence and machine learning as powerful tools for early detection, surveillance, and risk assessment of B. anthracis, particularly in bioterrorism-related contexts. These technologies offer promising opportunities to enhance preparedness strategies and mitigate the threats posed by biological agents.