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Increasing viral transmission paradoxically reduces progression rates to severe COVID-19 during endemic transition
Hyukpyo Hong; Ji Yun Noh; Hyojung Lee; Sunhwa Choi; Boseung Choi; Jae Kyoung Kim; Eui-Cheol Shin.
Affiliation
  • Hyukpyo Hong; Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
  • Ji Yun Noh; Division of Infectious Diseases, Department of Internal Medicine, Korea University College of Medicine, Seoul, Republic of Korea
  • Hyojung Lee; Department of Statistics, Kyungpook National University, Daegu, Republic of Korea
  • Sunhwa Choi; Division of Fundamental Research on Public Agenda, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
  • Boseung Choi; Division of Big Data Science, Korea University, Sejong, Republic of Korea
  • Jae Kyoung Kim; Dept. of Mathematical Sciences, KAIST
  • Eui-Cheol Shin; Korea Advanced Institute of Science and Technology
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-22270633
ABSTRACT
Natural infection with SARS-CoV-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 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.
License
cc_by_nc_nd
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Prognostic_studies Language: En Year: 2022 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Type of study: Prognostic_studies Language: En Year: 2022 Document type: Preprint