Hong Hyukpyo, Noh Ji Yun, Lee Hyojung, Choi Sunhwa, Choi Boseung, Kim Jae Kyoung, Shin Eui-Cheol
Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
Biomedical Mathematics Group, Institute for Basic Science (IBS), Daejeon 34126, Korea.
Immune Netw. 2022 May 18;22(3):e23. doi: 10.4110/in.2022.22.e23. eCollection 2022 Jun.
Natural infection with severe acute respiratory syndrome-coronavirus-2 or vaccination induces virus-specific immunity protecting hosts from infection and severe disease. While the infection-preventing immunity gradually declines, the severity-reducing immunity is relatively well preserved. Here, based on the different longevity of these distinct immunities, we develop a mathematical model to estimate courses of endemic transition of coronavirus disease 2019 (COVID-19). Our analysis demonstrates that high viral transmission unexpectedly reduces the rates of progression to severe COVID-19 during the course of endemic transition despite increased numbers of infection cases. Our study also shows that high viral transmission amongst populations with high vaccination coverages paradoxically accelerates the endemic transition of COVID-19 with reduced numbers of severe cases. These results provide critical insights for driving public health policies in the era of 'living with COVID-19.'
严重急性呼吸综合征冠状病毒2的自然感染或疫苗接种可诱导病毒特异性免疫，保护宿主免受感染和重症疾病。虽然预防感染的免疫力会逐渐下降，但减轻疾病严重程度的免疫力相对保持良好。在此，基于这些不同免疫力的不同持久性，我们开发了一个数学模型来估计2019冠状病毒病（COVID-19）的地方病转变过程。我们的分析表明，尽管感染病例数量增加，但在地方病转变过程中，高病毒传播率意外地降低了进展为重症COVID-19的发生率。我们的研究还表明，在高疫苗接种覆盖率人群中，高病毒传播率反而加速了COVID-19的地方病转变，同时重症病例数量减少。这些结果为在“与COVID-19共存”时代推动公共卫生政策提供了关键见解。
