Atypical viral dynamics from transport through popular places

Area of Science:

• Complex Systems
• Epidemiology
• Network Science
• Mathematical Modeling

Background:

• Visitor flux in public spaces significantly impacts the spread of viruses (e.g., H1N1, Zika) and information (e.g., rumors, social media trends).
• Current understanding of the resulting viral dynamics, particularly concerning the interplay of movement and presence, is limited.
• Existing models often lack the granularity to capture the diverse infection profiles observed in real-world scenarios.

Purpose of the Study:

• To develop a minimal dynamical model that captures the time-dependent relationship between visitor mobility and space occupancy.
• To analyze the resulting viral spreading patterns and understand the diversity of infection profiles.
• To provide a theoretical framework that can be compared against empirical data of social contagion phenomena.

Main Methods:

• Development of a generic, minimal dynamical model focusing on visitor mobility and occupancy.
• Analytical investigation of the model to understand its emergent properties.
• Comparison of theoretical infection profiles generated by the model with real-world social contagion data.

Main Results:

• The model analytically explains a rich diversity of infection profiles, varying in shape, duration, and intensity.
• The interplay between mobility and occupancy is identified as a key driver of these diverse dynamics.
• Theoretical profiles generated by the model show good agreement with empirical data from recent social contagion events.

Conclusions:

• A simplified dynamical model can effectively capture complex viral spreading dynamics influenced by human mobility and presence.
• The model provides valuable insights into the mechanisms driving diverse infection patterns in both physical and online environments.
• This framework offers a foundation for further research into contagion dynamics and can inform public health and information dissemination strategies.