Impact of mobility restriction in COVID-19 superspreading events using agent-based model

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• The COVID-19 pandemic necessitates understanding disease transmission dynamics.
• Agent-based models offer a powerful tool for simulating infectious disease spread in heterogeneous populations.
• Identifying superspreaders is key to effective pandemic control strategies.

Purpose of the Study:

• To implement an agent-based model using probabilistic cellular automata to simulate COVID-19 (Susceptible-Infected-Recovered) dynamics.
• To investigate the emergent behavior of superspreaders without a priori definition.
• To analyze the impact of mobility restrictions and personal protective equipment on disease transmission.

Main Methods:

• Developed an agent-based model with probabilistic cellular automata for SIR dynamics.
• Simulated agent movement using random walks and infection spread upon site co-location.
• Constructed transmission networks to analyze degree distribution, betweenness, and closeness centrality.

Main Results:

• Reduced mobility correlated with decreased network degree but increased betweenness and closeness for some nodes.
• Superspreaders emerged as key drivers of infection propagation, identified post-simulation.
• Simulations demonstrated the impact of personal protective equipment in mitigating disease spread.

Conclusions:

• Agent-based models can reveal emergent superspreader behavior crucial for understanding infectious disease dynamics.
• Mobility restrictions have a complex effect on transmission networks, highlighting the need for nuanced control strategies.
• Targeted interventions, including personal protective equipment, are vital for managing highly infectious pandemics like COVID-19.