A mechanism to enhance multi-participant's prevention efforts under pandemic. (May 2022)

• Record Type: Journal Article
• Authors: Sun, Huan; Wang, Haiyan; Steffensen, Sonja
• Is Part Of: Computers & industrial engineering. Volume 167(2022)
• Publication Date: 2022-05
• Subjects: Sustainable response strategy of pandemic -- Joint prevention efforts -- Incentive mechanism -- Mechanism design -- Medical insurance; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.107972
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: Frequently emerging pandemics have caused participants' low prevention cooperation. This paper studies a reward and punishment mechanism for a health system and individuals to enhance their prevention. In the traditional way of responding to the pandemic, health systems generally obtain full subsidies by government and pay attention to treatment. Meanwhile, individuals need to cooperate according to their treatment. However, emerging pandemics are increasing and spreading quickly. This has forced the government to change its full-subsidy treatment strategy to prevention incentive strategy. However, participants (individuals and the health system) appear to have a low motivation to do prevention. The aim of this paper is to give a new mechanism based on reward and punishment policies, which can be offered by the government to the participants to improve their prevention efficiency. We develop a game-theoretic model with three nonlinear programs (NLP) to identify the optimal policies, and obtain the factors that influence the optimal policies. Our findings show that under the proposed mechanism, all participants are willing to make decisions in accordance with the requirements of the government. Furthermore, we indicate that the designed mechanism has a positive network externality. We also prove that the given mechanism is effective regardless of whether the information of the individuals' prevention efforts is available. A numerical example indicates that the proposed mechanism is more suitable for applications in areas with a large population.