RESUMO
Since authorization of the Moderna mRNA COVID-19 Vaccine, real-world evidence has indicated its effectiveness in preventing COVID-19 cases. However, increased cases of mRNA vaccine-associated myocarditis/pericarditis have been reported, predominantly in young adults and adolescents. The Food and Drug Administration conducted benefit-risk assessment to inform review of the Biologics License Application for use of the Moderna vaccine among individuals ages 18 years and older. We modeled benefit-risk per million individuals who receive two complete doses of the vaccine. Benefit endpoints were vaccine-preventable COVID-19 cases, hospitalizations, intensive care unit (ICU) admissions, and deaths. The risk endpoints were vaccine-related myocarditis/pericarditis cases, hospitalizations, ICU admissions and deaths. The analysis was conducted on the age-stratified male population, due to data signals and previous work showing males to be the main risk group. We constructed six scenarios to evaluate the impact of uncertainty associated with pandemic dynamics, vaccine effectiveness (VE) against novel variants, and rates of vaccine-associated myocarditis/pericarditis cases on the model results. For our most likely scenario, we assumed the US COVID-19 incidence was for the week of December 25, 2021, and a VE of 30% against cases and 72% against hospitalization with the Omicron-dominant strain. Our source for estimating vaccine-attributable myocarditis/pericarditis rates was FDAs CBER Biologics Effectiveness and Safety (BEST) System databases. Overall, our results supported the conclusion that the benefits of the vaccine outweigh its risks. Remarkably, we predicted vaccinating one million 18-25 year-old males would prevent 82,484 cases, 4,766 hospitalizations, 1,144 ICU admissions, and 51 deaths due to COVID-19, comparing to 128 vaccine-attributable myocarditis/pericarditis cases, 110 hospitalizations, zero ICU admissions, and zero deaths. Uncertainties in the pandemic trajectory, effectiveness of vaccine against novel variants, and vaccine-attributable myocarditis/pericarditis rate are important limitations of our analysis. Also, the model does not evaluate potential long-term adverse effects due to either COVID-19 or vaccine-attributable myocarditis/pericarditis.
RESUMO
Quantifying how accurate epidemiological models of COVID-19 forecast the number of future cases and deaths can help frame how to incorporate mathematical models to inform public health decisions. Here we analyze and score the predictive ability of publicly available COVID-19 epidemiological models on the COVID-19 Forecast Hub. Our score uses the posted forecast cumulative distributions to compute the log-likelihood for held-out COVID-19 positive cases and deaths. Scores are updated continuously as new data become available, and model performance is tracked over time. We use model scores to construct ensemble models based on past performance. Our publicly available quantitative framework may aid in improving modeling frameworks, and assist policy makers in selecting modeling paradigms to balance the delicate trade-offs between the economy and public health.
RESUMO
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.
