Estimation of Total Cost Required in Controlling COVID-19 Outbreaks by Financial Incentives

Area of Science:

• Epidemiology
• Public Health Policy
• Computational Modeling

Background:

• Controlling COVID-19 spread is challenging due to asymptomatic transmission.
• Rapid identification and isolation of infected individuals are crucial.
• Financial incentives are proposed to encourage voluntary testing and reporting.

Purpose of the Study:

• To estimate the total cost of a financial incentive policy for COVID-19 control.
• To model infection routes and assess policy effectiveness using Monte Carlo simulation.
• To provide data for public health policymakers regarding incentive-based pandemic control.

Main Methods:

• Utilizing Monte Carlo simulation (MCS) for cost estimation.
• Incorporating data from epidemiological investigations.
• Modeling various infection routes based on estimated parameters.

Main Results:

• Estimated total cost for implementing the financial incentive policy.
• Quantification of potential impact on COVID-19 transmission control.
• Identification of conditions to prevent policy abuse, such as knowing the source of infection.

Conclusions:

• Financial incentives, coupled with specific conditions, offer a viable strategy for pandemic control.
• The proposed MCS method provides a framework for estimating policy costs.
• This approach can support broader public health preparedness, including hospital resources and vaccine development.