Infectious diseases remain a major public health challenge worldwide, prompting continuous advancement in mathematical modeling techniques, analytical methods, and data science to address these threats. As novel pathogens emerge and existing diseases evolve, gaining a deep understanding of their transmission dynamics is essential. Mathematical models and data-driven analyses are increasingly central to efforts aimed at predicting disease spread, evaluating the effectiveness of intervention strategies, and informing public health policy. The field is currently enriched by a wide array of approaches, including deterministic and stochastic models, that leverage analytical techniques, computational simulations, model calibration, and parameter estimation to investigate key questions related to transmission patterns, outbreak drivers, and population-level impacts.

Despite significant advances, important gaps remain in the field of infectious disease modeling. Recent studies have highlighted the power of mathematical modeling and analysis in uncovering disease dynamics, as well as the value of high-resolution data and complex models in delivering more accurate forecasts and enabling timely interventions. Advances in computational capabilities and data availability have facilitated the integration of diverse data sources, significantly improving model precision. However, ongoing debates regarding optimal modeling frameworks, robust parameter estimation, and effective uncertainty quantification point to the need for continued methodological development. Moreover, the unforeseen dynamics observed during the COVID-19 pandemic underscore the importance of flexible and adaptive models that can respond to rapidly evolving epidemiological situations.

This Research Topic aims to highlight cutting-edge applications of mathematical modeling, analytical methods, and data-driven approaches in the study of infectious disease dynamics. We seek to explore how these methodologies can be refined, adapted, and applied across diverse contexts to enhance our understanding of disease transmission and inform strategies for improving public health outcomes.

To gather further insights in the vast domain of mathematical modeling and analysis of infectious diseases, we welcome articles addressing, but not limited to, the following themes:

- Novel mathematical modelling and analytical techniques and their applications in infectious disease outbreaks
- Data-driven strategies for assessing and predicting disease spread
- Novel mathematical and statistical methods for evaluating the effectiveness of intervention
- Integrating cross-disciplinary data for enhanced model accuracy
- Addressing challenges in model uncertainty and parameter estimation

We encourage a range of article types, including original research, reviews, methods, and data reports, that contribute to this dynamic and critical area of public health.

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Mathematical Modelling and Data Analysis in Infectious Diseases