Manuscript Topics

While the use of mathematical modeling can yield transformative insights into disease dynamics and evolution, there remains a huge gulf to span between the mathematicians constructing these models and the clinicians charged with decision-making at a personalized level. In particular, it is common for modelers to develop complex, multi-scale models with high-dimensional parameter spaces in order to reflect as much of the reality of the system as possible. However, while these models may assist us in understanding the biological complexities that drive the system, they are of limited use to clinicians from the perspective of providing useful information about predicted treatment efficacy or disease evolution. The ability to collect informative data quantifying such objectives is often quite constrained; data collection is expensive, potentially invasive for patients, and may be subject to large amounts of measurement noise. When working with data subject to such limitations, it can be difficult or even impossible to calibrate complex mathematical models for use in a robust predictive framework; the amount of uncertainty generated by such a procedure may subsequently result in unreliable model predictions.

This special issue will focus on advances in connecting mathematical modeling to the achievement of clinical objectives using appropriate techniques in verification, validation, and uncertainty quantification (VVUQ). We seek contributions from researchers bridging the gap between complex mathematical models and limited data availability, to better enable mathematicians to provide support within the practical constraints of a clinical setting with modeling outcomes that are both informative and trustworthy.

Contributions may include (but are not limited to) the use of the following techniques to study applications of mathematical models in medicine:  
• Uncertainty quantification  
• Data-driven modeling  
• Reduced-order and/or surrogate modeling  
• Sensitivity and/or identifiability analysis  
• Predictive modeling  
• Multi-fidelity methods  
• Mixed-effects modeling  
• Model selection methods  
• Model discovery

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