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Human challenge trials (HCTs) are a potential method to accelerate development of vaccines and therapeutics. However, HCTs for COVID-19 pose ethical and practical challenges, in part due to the unclear and developing risks. In this article , we introduce an interactive model for exploring some risks of a severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) dosing study, a prerequisite for any COVID-19 challenge trials. The risk estimates we use are based on a Bayesian evidence synthesis model which can incorporate new data on infection fatality risks (IFRs) to patients, and infer rates of hospitalization. The model estimates individual risk, which we then extrapolate to overall mortality and hospitalization risk in a dosing study. We provide a web tool to explore risk under different study designs. Based on the Bayesian model, IFR for someone between 20 and 30 years of age is 15.1 in 100,000, with a 95% uncertainty interval from 11.8 to 19.2, while risk of hospitalization is 130 per 100,000 (100-160). However, risk will be reduced in an HCT via screening for comorbidities, selecting lower-risk population, and providing treatment. Accounting for this with stronger assumptions, we project the fatality risk to be as low as 2.5 per 100,000 (1.6-3.9) and the hospitalization risk to be 22.0 per 100,000 (14.0-33.7). We therefore find a 50-person dosing trial has a 99.74% (99.8-99.9%) chance of no fatalities, and a 98.9% (98.3-99.3%) probability of no cases requiring hospitalization.
Manheim et al. (Sat,) studied this question.