Evaluating the Impact of SARS-CoV-2 Variants on the COVID-19 Epidemic and Social Restoration in the United States: A Mathematical Modelling Study

Frontiers in Public Health

Public Health

Zhuoru Zou¹  Rui Li¹  Yiming Liu¹  Mingwang Shen²  Guihua Zhuang²  Lei Zhang³  Yan Li⁴  Xinghui Li⁵ 

[1] China-Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Center, School of Public Health, Xi'an, China;China-Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Center, School of Public Health, Xi'an, China;Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, China;China-Australia Joint Research Center for Infectious Diseases, Xi'an Jiaotong University Health Science Center, School of Public Health, Xi'an, China;Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia;Faculty of Medicine, Nursing and Health Sciences, Central Clinical School, Monash University, Melbourne, VIC, Australia;Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China;Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States;Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States;School of Public Health, Shaanxi University of Chinese Medicine, Xi'an, China;

关键词: COVID-19 variants;    vaccine effectiveness;    social restoration;    mathematical modelling;    COVID-19 vaccination;   

DOI  :  10.3389/fpubh.2021.801763

 received in 2021-10-25, accepted in 2021-11-22,  发布年份 2022

来源: Frontiers

【 摘 要 】

Background: Multiple SARS-CoV-2 variants are still rampant across the United States (US). We aimed to evaluate the impact of vaccination scale-up and potential reduction in the vaccination effectiveness on the COVID-19 epidemic and social restoration in the US.Methods: We extended a published compartmental model and calibrated the model to the latest US COVID-19 data. We estimated the vaccine effectiveness against the variant and evaluated the impact of a potential reduction in vaccine effectiveness on the epidemics. We explored the epidemic trends under different levels of social restoration.Results: We estimated the overall existing vaccine effectiveness against the variant as 88.5% (95% CI: 87.4–89.5%) with the vaccination coverage of 70% by the end of August, 2021. With this vaccine effectiveness and coverage, there would be 498,972 (109,998–885,947) cumulative infections and 15,443 (3,828–27,057) deaths nationwide over the next 12 months, of which 95.0% infections and 93.3% deaths were caused by the variant. Complete social restoration at 60, 65, 70% vaccination coverage would increase cumulative infections to 1.6 (0.2–2.9) million 0.7 (0.1–1.2) million, and 511,159 (110,578–911,740), respectively. At same time it would increase cumulative deaths to 39,040 (5,509–72,570), 19,562 (3,873–35,250), 15,739 (3,841–27,638), respectively. However, if the vaccine effectiveness were reduced to 75%, 50% or 25% due to new SARS-CoV-2 variants, there would be 667,075 (130,682–1,203,468), 1.7 (0.2–3.2) million, 19.0 (5.3–32.7) million new infections and 19,249 (4,281–34,217), 42,265 (5,081–79,448), 426,860 (117,229–736,490) cumulative deaths to occur over the next 12 months. Further, social restoration at a lower vaccination coverage would lead to even greater secondary outbreaks.Conclusion: Current COVID-19 vaccines remain effective against the SARS-CoV-2 variant, and 70% vaccination coverage would be sufficient to restore social activities to a pre-pandemic level. Further reduction in vaccine effectiveness against SARS-CoV-2 variants would result in a potential surge of the epidemic. Multiple measures, including public health interventions, vaccination scale-up and development of a new vaccine booster, should be integrated to counter the new challenges of new SARS-CoV-2 variants.

【 授权许可】

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Copyright © 2022 Li, Li, Zou, Liu, Li, Zhuang, Shen and Zhang.