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Assessing the medical resources in COVID-19 based on evolutionary game.
Guo, Keyu; Lu, Yikang; Geng, Yini; Lu, Jun; Shi, Lei.
  • Guo K; Information School, The University of Sheffield, Sheffield, United Kingdom.
  • Lu Y; School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
  • Geng Y; School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
  • Lu J; MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, China.
  • Shi L; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China.
PLoS One ; 18(1): e0280067, 2023.
Article in English | MEDLINE | ID: covidwho-2197137
ABSTRACT
COVID-19 has brought a great challenge to the medical system. A key scientific question is how to make a balance between home quarantine and staying in the hospital. To this end, we propose a game-based susceptible-exposed-asymptomatic -symptomatic- hospitalized-recovery-dead model to reveal such a situation. In this new framework, time-varying cure rate and mortality are employed and a parameter m is introduced to regulate the probability that individuals are willing to go to the hospital. Through extensive simulations, we find that (1) for low transmission rates (ß < 0.2), the high value of m (the willingness to stay in the hospital) indicates the full use of medical resources, and thus the pandemic can be easily contained; (2) for high transmission rates (ß > 0.2), large values of m lead to breakdown of the healthcare system, which will further increase the cumulative number of confirmed cases and death cases. Finally, we conduct the empirical analysis using the data from Japan and other typical countries to illustrate the proposed model and to test how our model explains reality.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2023 Document Type: Article Affiliation country: Journal.pone.0280067

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2023 Document Type: Article Affiliation country: Journal.pone.0280067