Market design to accelerate COVID-19 vaccine supply

Castillo, Juan Camilo; Ahuja, Amrita; Athey, Susan; Baker, A. Cornelius; Budish, Eric; Chipty, Tasneem; Glennerster, Rachel; Kominers, Scott Duke; Kremer, Michael; Larson, Greg; Lee, Jean; Prendergast, Canice; Snyder, Christopher M.; Tabarrok, Alex; Tan, Brandon Joel; Więcek, Witold

doi:10.1126/science.abg0889

Cited by 137 publications

(108 citation statements)

References 8 publications

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“…For LMICs, reducing the number of new cases of COVID-19 is crucial for the prevention and control of the pandemic. Affordable, effective, and fast diagnosis is still lacking [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Performance of Diagnostic Model for Differentiating Between COVID-19 and Influenza: A 2-Center Retrospective Study

Qiu

et al. 2021

Med Sci Monit

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Background COVID-19 and influenza share many similarities, such as mode of transmission and clinical symptoms. Failure to distinguish the 2 diseases may increase the risk of transmission. A fast and convenient differential diagnosis between COVID-19 and influenza has significant clinical value, especially for low- and middle-income countries with a shortage of nucleic acid detection kits. We aimed to establish a diagnostic model to differentiate COVID-19 and influenza based on clinical data. Material/Methods A total of 493 patients were enrolled in the study, including 282 with COVID-19 and 211 with influenza. All data were collected and reviewed retrospectively. The clinical and laboratory characteristics of all patients were analyzed and compared. We then randomly divided all patients into development sets and validation sets to establish a diagnostic model using multivariate logistic regression analysis. Finally, we validated the diagnostic model using the validation set. Results We preliminarily established a diagnostic model for differentiating COVID-19 from influenza that consisted of 5 variables: age, dry cough, fever, white cell count, and D-dimer. The model showed good performance for differential diagnosis. Conclusions This initial model including clinical features and laboratory indices effectively differentiated COVID-19 from influenza. Patients with a high score were at a high risk of having COVID-19, while patients with a low score were at a high risk of having influenza. This model could help clinicians quickly identify and isolate cases in the absence of nucleic acid tests, especially during the cocirculation of COVID-19 and influenza. Owing to the study’s retrospective nature, further prospective study is needed to validate the accuracy of the model.

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“…For LMICs, reducing the number of new cases of COVID-19 is crucial for the prevention and control of the pandemic. Affordable, effective, and fast diagnosis is still lacking [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Performance of Diagnostic Model for Differentiating Between COVID-19 and Influenza: A 2-Center Retrospective Study

Qiu

et al. 2021

Med Sci Monit

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“…It seems likely that HCTs are a viable way to rapidly test vaccine efficacy, which is particularly critical now for testing second‐generation vaccines, which are important in quickly expanding vaccine availability (Castillo et al., 2021) and may prove superior to first‐generation vaccines. A dosing study is an urgent first step, and the risk estimates and tools developed for this article can assist in planning such studies and informing volunteers.…”

Section: Discussionmentioning

confidence: 99%

Exploring Risks of Human Challenge Trials For COVID‐19

Manheim

Więcek²,

Schmit³

et al. 2021

Risk Analysis

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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.

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“…It seems likely that HCTs are a viable way to rapidly test vaccine efficacy, which is particularly critical now for testing second-generation vaccines, which are important in quickly expanding vaccine availability (Castillo et al, 2021) and may prove superior to first generation vaccines. A dosing study is an urgent first step, and the risk estimates and tools developed for this paper can assist in planning such studies and informing volunteers.…”

Section: Discussionmentioning

confidence: 99%

Exploring Risks of Human Challenge Trials for COVID-19

Manheim

Więcek²,

Schmit³

et al. 2020

Preprint

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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 paper, we introduce an interactive model for exploring some risks of a 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 rates (IFRs) to patients, and infer rates of hospitalization. We have also created a web tool to explore risk under different study design parameters and participant scenarios. Finally, we use our model to estimate individual risk, as well as the overall mortality and hospitalization risk in a dosing study.Based on the Bayesian model we expect IFR for someone between 20 and 30 years of age to be 17.5 in 100,000, with 95% uncertainty interval from 12.8 to 23.6. Using this estimate, we find that a simple 50-person dosing trial using younger individuals has a 99.1% (95% CI: 98.8% to 99.4%) probability of no fatalities, and a 92.8% (95% CI: 90.3% to 94.6%) probability of no cases requiring hospitalization. However, this IFR will be reduced in an HCT via screening for comorbidities, as well as providing medical care and aggressive treatment for any cases which occur, so that with stronger assumptions, we project the risk to be as low as 3.1 per 100,000, with a 99.85% (95% CI: 99.7% to 99.9%) chance of no fatalities, and a 98.7% (95% CI: 97.4% to 99.3%) probability of no cases requiring hospitalization.

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Market design to accelerate COVID-19 vaccine supply

Abstract: Build more capacity, and stretch what we already have

Cited by 137 publications

References 8 publications

Performance of Diagnostic Model for Differentiating Between COVID-19 and Influenza: A 2-Center Retrospective Study

Performance of Diagnostic Model for Differentiating Between COVID-19 and Influenza: A 2-Center Retrospective Study

Exploring Risks of Human Challenge Trials For COVID‐19

Exploring Risks of Human Challenge Trials for COVID-19

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