Data-Driven Mathematical and Statistical Methods for Infectious Disease Dynamics and Public Health Decision-Making

Infectious diseases continue to pose significant challenges to public health systems worldwide, particularly in settings where resources, surveillance systems, and data availability remain limited. In such contexts, there is a critical need for robust, data-driven, and methodologically innovative approaches that can support effective decision-making and improve health outcomes.

While mathematical and statistical models have traditionally been used to study infectious disease dynamics, many existing studies rely on standard frameworks with limited methodological novelty and insufficient integration of real-world data. This Research Topic seeks to move beyond these limitations by promoting contributions that combine original methodological developments, context-specific empirical data, and clear relevance for public health decision-making.

Special attention will be given to studies grounded in low- and middle-income settings, where modeling efforts can have the greatest impact on informing interventions, optimizing resource allocation, and addressing structural and epidemiological complexities. Contributions are expected to demonstrate how advanced quantitative methods can be adapted to real-world constraints, including data scarcity, under-reporting, and heterogeneous transmission environments.

By fostering interdisciplinary collaboration and encouraging the integration of mathematical modeling, statistical inference, and applied epidemiology, this Research Topic aims to advance innovative, data-informed tools that can meaningfully support public health responses and strengthen decision-making processes across diverse settings.

To gather further insights within this dynamic area of research, we welcome articles addressing, but not limited to, the following themes:
- Mathematical modeling of infectious disease transmission;
- Stochastic models and uncertainty analysis;
- Statistical inference and data-driven epidemiology;
- Modeling public health interventions;
- Climate, environment, and vector ecology;
- Socioeconomic and demographic determinants of disease spread;
- Surveillance systems and early warning models;
- Emerging and re-emerging infectious diseases;
- Policy translation and decision-support tools;
- Strengthening mathematical epidemiology capacity in Africa.

We invite contributions in various formats, including original research articles, reviews, methods papers, and perspective pieces.

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
• FAIR² DATA Direct Submission
• General Commentary
• ... 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
• FAIR² DATA Direct Submission
• 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: infectious diseases, mathematical and statistical modeling, effects of temperature and rainfall/humidity, deterministic compartmental models, age-structured models, Bayesian inference, numerical simulation

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.