Abstract
We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits-averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design-if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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COVID-19 / epidemiology
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COVID-19 / prevention & control*
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COVID-19 / therapy
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COVID-19 / virology
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COVID-19 Vaccines / standards*
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COVID-19 Vaccines / therapeutic use
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Clinical Trials as Topic
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Cost-Benefit Analysis*
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Humans
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Pandemics
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SARS-CoV-2 / drug effects*
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SARS-CoV-2 / pathogenicity
Grants and funding
L.I. is an employee of the biotech company Seqirus, and D.A.B. and S.B are employees of Berry Consultants. These companies did not have any role in study design, data collection and analysis, decision to publish, or preparation of this manuscript. The specific roles of these authors are specified in the ‘author contributions’ section. Funding support from the MIT Laboratory for Financial Engineering is gratefully acknowledged, but no direct funding was received for this study.