COVID-19 has underscored the need for more timely access to vaccines during future pandemics. This has motivated development of broad-spectrum vaccines providing protection against viral families, which could be stockpiled ahead of an outbreak and deployed rapidly following detection. We use mathematical modelling to evaluate the utility of a broadly protective sarbecovirus vaccine (BPSV) during a hypothetical SARS-X outbreak, including ring-vaccination, spatial targeting and mass vaccination of high-risk populations. Our results show BPSV ring- or spatially-targeted vaccination strategies are unlikely to contain a SARS-CoV-2-like virus but could contain or slow the spread of a SARS-CoV-1-like virus. Vaccination of high-risk populations with the BPSV ahead of a virus-specific vaccine (VSV) becoming available could substantially reduce mortality. For a 250-day VSV development timeline, BPSV availability reduced infection-related deaths in our model by 54% on average, though exact impact depended on the non-pharmaceutical intervention (NPI) scenario considered. We further show that BPSV availability enables shorter and less stringent NPIs to be imposed whilst limiting disease burden to that observed in the VSV-only scenario, though results are sensitive to vaccine properties (e.g. efficacy), health system capabilities (e.g. vaccination rollout speed) and the assumed timeline to VSV availability. Our modelling suggests that availability of a BPSV for those aged 60+ years could have averted 40-65% of COVID-19 deaths during the pandemic's first year, though exact impact depends on the size of the maintained stockpile. Our work highlights significant potential impact of a BPSV, but that achieving this impact depends on investment into health systems enabling rapid and equitable access during future SARS-X pandemics.

The Coalition for Epidemic Preparedness Innovations (CEPI) funded the investigation into the impact of the 100 Days Mission. Authors maintained full freedom when designing the study and deciding on additional scenarios to explore. ACG has received personal consultancy fees from HSBC, GlaxoSmithKline, Sanofi and WHO related to COVID-19 epidemiology and from The Global Fund to Fight AIDS, Tuberculosis and Malaria for work unrelated to COVID-19. ACG was previously a non-remunerated member of a scientific advisory board for Moderna and is currently a non-remunerated member of the scientific advisory board for the Coalition for Epidemic Preparedness. OJW has received personal consultancy fees from WHO for work related to malaria. ABH has received personal consultancy fees from WHO for work related to COVID-19, and grant funding for COVID-19 work from WHO and NSW Ministry of Health, Australia. ABH is a member of the WHO Immunization and vaccines related implementation research advisory committee. CW has received personal consultancy fees from SecureBio for work relating to novel pathogen surveillance and from Blueprint Biosecurity for work relating to pandemic preparedness. All other authors declare no competing interests.

The investigation was funded by the Coalition for Epidemic Preparedness Innovations (CEPI) through the Vaccine Impact Assessment Modelling project funding. This work was supported by a Sir Henry Wellcome Postdoctoral Fellowship Ref 224190/Z/21/Z. This research was funded in whole, or in part, by the Wellcome Trust (Ref 224190/Z/21/Z). For Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The work is supported by the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1) which is jointly funded by the UK Medical Research Council (MRC), the EDCTP2 programme supported by the European Union and Community Jameel. DJL acknowledges funding from the Wellcome Trust for the Vaccine Impact Modelling Consortium (VIMC) Climate Change Research Programme (grant ID: 226727_Z_22_Z). ABH is supported by an Australian National Health and Medical Research Council Investigator Grant.

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The modelling framework, along with all relevant data and code required to reproduce the analyses presented in this manuscript are freely available in Github repository (https://github.com/mrc-ide/diseaseX_modelling).

https://github.com/mrc-ide/diseaseX_modelling