A Parametric Multi-Agent Simulation Framework to Emulate Social Isolation During the Pandemic.

Area of Science:

• Computational epidemiology
• Social dynamics simulation
• Public health modeling

Background:

• The COVID-19 pandemic has led to widespread social distancing and lockdowns globally.
• Preliminary data suggests a significant increase in feelings of loneliness and isolation among the population.
• Factors contributing to isolation include fear of infection, bereavement, and restrictive public health measures.

Purpose of the Study:

• To propose a parametric multi-agent simulation framework for emulating social isolation during the COVID-19 pandemic.
• To create a realistic simulation environment mimicking real-world conditions for studying social isolation effects.

Main Methods:

• Development of a parametric multi-agent simulation framework.
• Emulation of a 144 km² area with a population of 200,000.
• Inclusion of key parameters: hospital capacity, infection rate, recovery, hospitalization, and mortality rates.

Main Results:

• The simulation framework was validated using a real-world scale artificial society.
• The model allows for extensive parameterization to simulate diverse scenarios.
• The framework effectively emulates social isolation dynamics within a pandemic context.

Conclusions:

• The proposed simulation framework provides a valuable tool for understanding and analyzing social isolation during pandemics.
• The model's flexibility allows for testing various public health interventions and their impact on social dynamics.
• This research contributes to computational epidemiology by offering a method to study the psychosocial impacts of pandemics.