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

Sangkwon Kim, Youngjin Hwang, Chaeyoung Lee, Soobin Kwak, Junseok Kim

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, we present a Monte Carlo simulation (MCS) to estimate the total cost required to control the spread of the COVID-19 pandemic by financial incentives. One of the greatest difficulties in controlling the spread of the COVID-19 pandemic is that most infected people are not identified and can transmit the virus to other people. Therefore, there is an urgent need to rapidly identify and isolate the infected people to avoid the further spread of COVID-19. To achieve this, we can consider providing a financial incentive for the people who voluntarily take the COVID-19 test and test positive. To prevent the abuse of the financial incentive policy, several conditions should be satisfied to receive the incentive. For example, an incentive is offered only if the recipients know who infected them. Based on the data obtained from epidemiological investigations, we calculated an estimated total cost of financial incentives for the policy by generating various possible infection routes using the estimated parameters and MCS. These results would help public health policymakers implement the proposed method to prevent the spread of the COVID-19 pandemic. In addition, the incentive policy can support various preparations such as hospital bed preparation, vaccine development, and so forth.

Original languageEnglish
Article number1217
JournalInternational journal of environmental research and public health
Volume20
Issue number2
DOIs
Publication statusPublished - 2023 Jan

Bibliographical note

Funding Information:
The corresponding author (J. Kim) was supported by the Brain Korea 21 FOUR from the Ministry of Education of Korea.

Publisher Copyright:
© 2023 by the authors.

Keywords

  • COVID-19
  • Monte Carlo simulation
  • financial incentives
  • policymakers

ASJC Scopus subject areas

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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