Modelling Singapore COVID-19 pandemic with a SEIR multiplex network model

Area of Science:

• Epidemiology
• Computational modeling
• Network science

Background:

• The COVID-19 pandemic highlighted the need for sophisticated modeling of disease transmission.
• Understanding complex human interaction patterns is key to predicting and controlling outbreaks.
• Singapore's dense urban environment presents unique challenges for infectious disease control.

Purpose of the Study:

• To implement a large-scale agent-based model for studying COVID-19 outbreaks in Singapore.
• To analyze the impact of complex human interaction patterns on disease spread.
• To evaluate the efficacy of various control measures.

Main Methods:

• Utilized a multiplex network approach to differentiate social interactions (household, workplace, crowds, gatherings, dormitories).
• Integrated the multiplex network with the Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model.
• Simulated epidemic outbreaks within densely populated residential areas.

Main Results:

• Densely populated areas, even with a small population fraction, are highly susceptible to infection.
• Simulations demonstrated rapid and uncontrollable disease spread originating from these areas without interventions.
• The model assessed the feasibility and efficacy of control measures like social distancing and lockdowns.

Conclusions:

• Agent-based modeling with multiplex networks provides precise insights into COVID-19 transmission dynamics.
• Targeted interventions in high-risk, densely populated areas are critical for outbreak control.
• Proactive implementation of control strategies is essential to mitigate widespread infection.