Integrative Mathematical Models for Disease: Volume II

After the success of the first Research Topic "Mathematical Modeling of Diseases at Population-Level and Cellular-Level", we are really proud to release this second special collection dedicated to exploring this theme.

In the realm of public health and disease management, mathematical modeling has emerged as a pivotal tool for unraveling the intricate patterns of disease spread and the effectiveness of health interventions. At a population level, these models are crucial for predicting the trajectory of infectious diseases and assessing the impact of control strategies like vaccinations and quarantine measures. Moving to the cellular scale, modeling provides insights into individual cellular behaviors and interactions within various disease states, notably in cancer progression. The recent COVID-19 pandemic has underscored the indispensable role of mathematical modeling in shaping public health decisions and strategies, pointing to an urgent need for models that integrate both population and cellular perspectives to enrich our understanding of disease mechanics.

This Research Topic aims to advance the development and application of mathematical models that synthesize population and cellular-level data. The goal is to enhance our comprehension of disease transmission and progression, pinpoint key risk factors, assess intervention efficacy, and support the creation of innovative diagnostic and therapeutic approaches. At the population scale, models are set to refine our predictions of infectious disease spread and improve chronic disease management through cost-effective strategy evaluations. At the cellular level, the focus sharpens on the dynamics of cellular processes and their role in disease progression, aiding in the optimization of treatments such as tailored chemotherapy regimens.

To further refine these objectives, we are focused on a broad yet targeted exploration of mathematical modeling in disease dynamics:

- Dynamics and control of infectious and non-infectious diseases
- Meta-Analysis of Modeling Approaches
- Analysis of vector and food-borne diseases
- Public health implications from model insights
- Integration of data-driven modeling approaches
- Exploration of varying model types (deterministic, stochastic, agent-based)
- Detailed studies on virus dynamics and cellular interactions
- Advanced qualitative model analysis (stability, bifurcation, etc.)

This inclusive approach is designed to stimulate extensive research contributions that bridge theoretical modeling with practical health applications, fostering a deeper understanding of disease mechanics across scales.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Mathematical modeling, Dynamical systems, Communicable diseases, Non-communicable diseases, Infectious diseases, Non-infectious diseases

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.