To Freeze or Not to Freeze? Epidemic Prevention and Control in the DSGE Model Using an Agent-Based Epidemic Component

Area of Science:

• Epidemiology
• Public Health Policy
• Economic Modeling

Background:

• The COVID-19 pandemic prompted widespread state interventions, with lockdowns becoming a primary global response.
• Lockdowns are increasingly viewed as economically inefficient and outdated, especially lacking effective treatments or vaccines.
• There is a critical need to evaluate alternative epidemic control strategies and their economic consequences.

Purpose of the Study:

• To assess the economic consequences of various epidemic prevention and control schemes implemented during the COVID-19 pandemic.
• To compare the economic efficiency of different response strategies beyond traditional lockdowns.
• To provide conditional forecasts of economic variables based on simulated epidemic control measures.

Main Methods:

• Utilized agent-based modeling (ABM) for epidemic simulations under diverse response scenarios.
• Employed dynamic stochastic general equilibrium (DSGE) models with a labor market component for economic forecasting.
• Integrated epidemiological simulations with macroeconomic modeling to assess policy impacts.

Main Results:

• Epidemic simulations revealed varying economic outcomes across different control schemes.
• Agent-based modeling provided insights into the transmission dynamics influenced by policy interventions.
• DSGE model forecasts indicated significant economic repercussions tied to the chosen epidemic control strategies.

Conclusions:

• The study highlights the economic inefficiencies associated with widespread lockdowns as a pandemic response.
• Agent-based and DSGE modeling offer valuable tools for evaluating public health interventions and their economic effects.
• Findings suggest a need for more economically viable and adaptable strategies for future epidemic control.