Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub

Importance COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective To project COVID-19 hospitalizations and deaths from April 2023–April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023–April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting The entire United States. Participants None. Exposure Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results From April 15, 2023–April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November–January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000–4,270,000) hospitalizations and 209,000 (90% PI: 139,000–461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000–355,000) fewer hospitalizations and 33,000 (95% CI: 12,000–54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI: 29,000–69,000) fewer deaths. Conclusion and Relevance COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease. Question What is the likely impact of COVID-19 from April 2023–April 2025 and to what extent can vaccination reduce hospitalizations and deaths? Findings Under plausible assumptions about viral evolution and waning immunity, COVID-19 will likely cause annual epidemics peaking in November–January over the two-year projection period. Though significant, hospitalizations and deaths are unlikely to reach levels seen in previous winters. The projected health impacts of COVID-19 are reduced by 10–20% through moderate use of reformulated vaccines. Meaning COVID-19 is projected to remain a significant public health threat. Annual vaccination can reduce morbidity, mortality, and strain on health systems. ### Competing Interest Statement J. Espino is president of General Biodefense LLC, a private consulting group for public health informatics, and has interest in READE.ai, a medical artificial intelligence solutions company. M. Runge reports stock ownership in Becton Dickinson & Co., which manufactures medical equipment used in COVID-19 testing, vaccination, and treatment. J. Lessler has served as an expert witness on cases where the likely length of the pandemic was of issue. ### Funding Statement S. Jung, S, Loo, C. Smith, E. Carcelén, J. Lemaitre, K. Sato, C. Mckee, S. Truelove, A.Hill, and J. Lessler were supported by Centers for Disease Control and Prevention (200-2016-91781). C. Smith, S. Truelove, and A. Hill were supported by the National Science Foundation (2127976). C. Smith, A. Hill, S. Truelove, and J. Lessler were supported by the US Department of Health and Human Services; Department of Homeland Security; California Department of Public Health; Johns Hopkins University. J. Lemaitre, C. Smith, A. Hill, S. Truelove, and J. Lessler were supported by Amazon Web Services. J. Lessler (R01GM140564) and J. Lemaitre (5R01AI102939) were supported by the National Institutes of Health. J. Lemaitre was supported by the Swiss National Science Foundation (200021-172578). L. Contamin, J. Levander, J. Espino, and H. Hochheiser were supported by NIGMS 5U24GM132013. E. Howerton and K. Shea were supported by NSF RAPID awards DEB-2028301, DEB-2037885, DEB-2126278, and DEB-2220903. K. Yan was supported by NSF Grant No. DGE1255832. E. Howerton was supported by the Eberly College of Science Barbara McClintock Science Achievement Graduate Scholarship in Biology at the Pennsylvania State University. M. Chinazzi, J. T. Davis, K. Mu, and A. Vespignani were supported by HHS/CDC 6U01IP001137, HHS/CDC 5U01IP0001137, and the Cooperative Agreement no. NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE). E. Rosenstrom, J. Ivy, M. Mayorga, and J. Swann were supported by TRACS/NIH grant UL1TR002489; CSTE and CDC cooperative agreement no. NU38OT000297. G. España and S. Moore were supported by Scenario Modeling Hub Consortium fellowship. S. Moore was supported by NIAID R21AI164391. T. Perkins was supported by NIGMS R35 MIRA program R35GM143029. K. Bi, S. Bandekar, A. Bouchnita, S. Fox, and L. Meyers were supported by CSTE NU38OT000297, CDC Supplement U01IP001136- Suppl, CDC 75D30122C14776 and NIH Supplement R01AI151176-Suppl. P. Porebski, S. Venkatramanan, A. Adiga, B. Lewis, B. Klahn, J. Outten, B. Hurt, H. Mortveit, A. Wilson, M. Marathe, J. Chen, S. Hoops, P. Bhattacharya, D. Machi acknowledge support from SMC Fellowship 75D30121F00005-2005604290, VDH Grant PV-BII VDH COVID-19 Modeling Program VDH-21-501-0135, NSF Grant No. OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, DTRA subcontract/ARA S-D00189-15-TO-01-UVA, and UVA strategic funds. Model computation was supported by NSF ACCESS CIS230005 and UVA. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All source data are openly available on COVID-19 Scenario Modeling Hub GitHub: . Replication codes are available on GitHub: . Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.