An epidemic-economic model for COVID-19

Area of Science:

• Epidemiology
• Mathematical Modeling
• Health Economics

Background:

• COVID-19 has profound epidemic and economic consequences.
• Understanding the interplay between disease dynamics and economic factors is crucial for effective pandemic management.
• Previous models may not fully capture the complex interactions between disease transmission, control strategies, and economic indicators.

Purpose of the Study:

• To develop and present a new mathematical model for analyzing the interconnected epidemic and economic consequences of COVID-19.
• To incorporate symptomatic and asymptomatic infections and the effectiveness of disease control measures into transmission dynamics.
• To demonstrate the model's application using real-world data from Tennessee, USA.

Main Methods:

• Development of a compartmental mathematical model tracking susceptible, infectious, and recovered populations.
• Integration of disease control effectiveness into transmission rate parameters.
• Fitting the model to reported COVID-19 cases and unemployment rates in Tennessee.
• Analysis of the interaction between pandemic progression and economic development levels.

Main Results:

• The model successfully captures the dynamics of COVID-19 transmission and its economic repercussions.
• Analysis revealed significant impacts of pandemic progression and control measures on economic indicators like unemployment.
• The Tennessee case study validated the framework's utility in real-world scenarios.

Conclusions:

• The proposed mathematical model provides a robust framework for studying the dual epidemic and economic impacts of COVID-19.
• Effective pandemic management strategies can mitigate both health and economic downturns.
• This modeling approach offers valuable insights for policymakers addressing future public health crises and their economic fallout.