By: Dr Ian Dunican
In recent years, sleep loss that leads to fatigue has been recognised as a critical factor affecting safety, performance, and decision-making in high-risk environments. Yet despite advances in sleep science, many organisations still manage fatigue with prescriptive rules based on hours worked rather than understanding how sleep loss and fatigue affect human performance. Today, biomathematical modelling (BMM) can provide a scientific, data-driven approach to predicting fatigue risk and optimising operational effectiveness.
What Are Biomathematical Fatigue Models?
Biomathematical fatigue models use sleep, circadian rhythm and performance research to estimate how factors such as sleep duration, wakefulness, workload and biological timing influence human performance. Unlike traditional fatigue management approaches that rely on prescriptive work-hour limits, biomathematical models are dynamic and predictive. Depending on the model, variables such as commute times, travel schedules, rest opportunities, shift patterns and geographical location can incorporate circadian changes.
This allows organisations to simulate schedules before implementation and identify potential fatigue risks before they occur. Rather than reacting to incidents or relying on subjective decision-making or assumptions, decision-makers can forecast periods of elevated fatigue risk and adjust work schedules accordingly. Biomathematical fatigue modelling is widely used across aviation, rail, mining, energy, defence and elite sport to support performance, safety and sustainable operations.
Treating Fatigue Like Any Other Operational Risk
Many organisations devote extensive effort to predicting production rates, fuel consumption, equipment reliability, logistics requirements, and workforce availability. Fatigue could be managed with the same level of rigour. Multiple BMM models, such as SAFTE (Sleep, Activity, Fatigue and Task Effectiveness) and FAID Quantum, provide leaders with data to estimate fatigue-related performance impacts before they become operational problems [1]. These data sets can identify when personnel are likely to experience reduced alertness, slower reaction times or impaired decision-making, enabling leaders to adjust rosters, task allocation, shift rotations and schedules. The result is a more informed approach to operational planning, where human performance becomes an integral part of the risk management process rather than an afterthought.
Research in Military Aviation
Recent research highlights the practical application of biomathematical fatigue modelling in military operational environments. A 2025 study involving 32 British military rotary-wing pilots examined the relationship between operational schedules, sleep behaviour and predicted fatigue. Researchers combined wearable actigraphy data with the SAFTE biomathematical model to estimate operational readiness and fatigue during approximately 200 flights conducted across a range of operational and deployed environments.
The study included personnel from all three branches of the United Kingdom Armed Forces and analysed both daytime and night-time operations. The findings showed that predicted effectiveness declined during night flying operations, particularly when flights concluded after 22:00. These results reinforce what scientists have long understood: the circadian system strongly influences performance, especially during night-time operations when biological alertness is naturally reduced [2]. Importantly, this research demonstrates how objective sleep data collected through wearable technologies can be combined with biomathematical models to provide meaningful operational insights.
Data to Deployment
The true value of biomathematical modelling lies not in generating fatigue scores but in informing practical decisions. Effective implementation requires organisations to establish evidence-based policies and operational practices that support human performance. Research consistently demonstrates that aligning work schedules with biological rhythms improves vigilance, decision-making and overall operational effectiveness while reducing fatigue-related errors. Biomathematical fatigue models represent a major step forward in human performance science. They enable organisations to forecast fatigue risk, optimise schedules and make more informed operational decisions using objective evidence rather than assumptions. When applied correctly, biomathematical modelling gives organisations a powerful tool to enhance safety, sustain operational capability, and protect the health and wellbeing of their people.
At Melius Consulting, we help organisations integrate biomathematical modelling, fatigue risk management systems and human performance science into practical operational solutions that improve both safety and performance.
Contact us: ian.dunican@melius.com.au or go to www.meliusconsulting.com.au
References
1. Steven R. Hursh, D.P.R., Michael L. Johnson, David R. Thorne, Gregory Belenky,Thomas J. Balkin, William F. Storm, James C. Miller and Douglas R. Eddy, Fatigue models for applied research in warfighting. Aviation Space Environmental Medicine, 2004(00(3 Suppl.):A000–00.).
2. Pelham, A., et al., Actigraphy-Driven Biomathematical Fatigue Modeling in British Military Rotary-Wing Pilots. Aerospace Medicine and Human Performance, 2025. 96(3): p. 206-211.