Notice

This is not the latest version of this item. The latest version can be found at:https://dspace.mit.edu/handle/1721.1/131332.2

Show simple item record

dc.contributor.authorGuttieres, Donovan
dc.contributor.authorSinskey, Anthony J.
dc.contributor.authorSprings, Stacy L.
dc.date.accessioned2021-09-20T14:16:15Z
dc.date.available2021-09-20T14:16:15Z
dc.date.issued2021-01-13
dc.identifier.urihttps://hdl.handle.net/1721.1/131332
dc.description.abstractSARS-CoV-2, with an infection fatality rate between 0.5 and 1%, has spread to all corners of the globe and infected millions of people. While vaccination is essential to protect against the virus and halt community transmission, rapidly making and delivering safe and efficacious vaccines presents unique development, manufacturing, supply chain, delivery, and post-market surveillance challenges. Despite the large number of vaccines in or entering the clinic, it is unclear how many candidates will meet regulatory requirements and which vaccine strategy will most effectively lead to sustained, population-wide immunity. Interviews with experts from biopharmaceutical companies, regulatory and multilateral organizations, non-profit foundations, and academic research groups, complemented with extensive literature review, informed the development of a framework for understanding the factors leading to population-wide immunity against SARS-CoV-2, in particular considering the role of vaccines. This paper presents a systems-level modeling framework to guide the development of analytical tools aimed at informing time-critical decisions to make vaccines globally and equitably accessible. Such a framework can be used for scenario planning and evaluating tradeoffs across access strategies. It highlights the diverse and powerful ways in which data can be used to evaluate future risks and strategically allocate limited resources.en_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.relation.isversionofhttp://dx.doi.org/10.3390/systems9010004en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceMultidisciplinary Digital Publishing Instituteen_US
dc.titleModeling Framework to Evaluate Vaccine Strategies against the COVID-19 Pandemicen_US
dc.typeArticleen_US
dc.identifier.citationSystems 9 (1): 4 (2021)en_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-01-22T15:50:52Z
dspace.date.submission2021-01-22T15:50:52Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Needed


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

VersionItemDateSummary

*Selected version