In an increasingly interconnected world, the field of social systems and human interaction analysis is pivotal for addressing intricate global challenges such as infectious diseases, economic fluctuations, and the influence of digital platforms. Historically, traditional models of social behavior have depended on simplified assumptions or were restricted by limited datasets, curbing their capability to reflect the complex and dynamic nature of human interactions. However, the rise of accessible large-scale social data from sources such as social media, communication networks, health records, and economic transactions offers unprecedented research opportunities. By merging mathematical modeling with modern data science and computational methodologies, scientists can create robust and predictive models of social behavior. These enhanced models do more than just improve theoretical perceptions of social dynamics; they extend practical insights into public policy formulation, health intervention strategies, and economic planning. Situated at the intersection of mathematics, computer science, and social sciences, this interdisciplinary field is adeptly adapting to the demands of a data-rich society.

Despite growing global challenges, encompassing pandemics, economic instability, and digital communication evolution, the precision of social behavior modeling and prediction remains constrained. Traditional mathematical models often rest on idealized presuppositions or lack comprehensive real-world data access, yielding oversimplified depictions of human interactions. This disparity between theoretical frameworks and actual phenomena hampers the efficiency of policy interventions, public health methodologies, and socio-economic plans.

This Research Topic invites interdisciplinary contributions that adeptly fuse mathematical modeling, computational methods, and extensive social data to further our comprehension of human behavior and social systems. We encourage research that marries theoretical innovation with empirical applicability, shedding light on how individuals and collectives interact, make decisions, and confront societal challenges. We welcome explorations in the following pivotal themes:

- Data-driven models of social interaction
- Socio-economic dynamics
- Computational social epidemiology
- Behavioral modeling
- Mathematical foundations of collective behavior

We welcome original research articles, review papers, and case studies explicitly demonstrating methodological rigor and societal impact. Submissions should clearly illustrate how mathematical and computational strategies enhance real-world applicability in areas such as public policy, health interventions, economic strategizing, or social program design. Emphasis will be on works fostering interdisciplinary dialogue and the successful synthesis of theoretical and practical aspects.

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Advancing Social System Understanding Through Data-Driven Modelling and Computational Techniques