RESUMEN
Background: Oral Antiviral (OAV) COVID-19 treatments are widely used, but evidence for their effectiveness against the Omicron variant in higher risk, vaccinated individuals is limited. Methods: Retrospective study of two vaccinated cohorts of COVID-19 cases aged ≥70 years diagnosed during a BA.4/5 Omicron wave in Victoria, Australia. Cases received either nirmatrelvir-ritonavir or molnupiravir as their only treatment. Data linkage and logistic regression modelling was used to evaluate the association between treatment and death and hospitalisation and compared with no treatment. Findings: Of 38,933 individuals in the mortality study population, 13.5% (n = 5250) received nirmatrelvir-ritonavir, 51.3% (n = 19,962) received molnupiravir and 35.2% (n = 13,721) were untreated. Treatment was associated with a 57% (OR = 0.43, 95% CI 0.36-0.51) reduction in the odds of death, 73% (OR = 0.27, 95% CI 0.17-0.40) for nirmatrelvir-ritonavir and 55% (OR = 0.45, 95% CI 0.38-0.54) for molnupiravir. Treatment was associated with a 31% (OR = 0.69, 95% CI 0.55-0.86) reduction in the odds of hospitalisation, 40% (OR = 0.60, 95% CI 0.43-0.83) for nirmatrelvir-ritonavir and 29% (OR = 0.71, 95% CI 0.58-0.87) for molnupiravir. Cases treated within 1 day of diagnosis had a 61% reduction in the odds of death (OR = 0.39, 95% CI 0.33-0.46) compared with 33% reduction for a delay of 4 or more days (OR = 0.67, 95% CI 0.44-0.97). Interpretation: Treatment with both nirmatrelvir-ritonavir or molnupiravir was associated with a reduction in death and hospitalisation in vaccinated ≥70 years individuals during the Omicron era. Timely, equitable treatment with OAVs is an important tool in the fight against COVID-19. Funding: There was no funding for this study.
RESUMEN
A common approach to improving probabilistic forecasts is to identify and leverage the forecasts from experts in the crowd based on forecasters' performance on prior questions with known outcomes. However, such information is often unavailable to decision-makers on many forecasting problems, and thus it can be difficult to identify and leverage expertise. In the current paper, we propose a novel algorithm for aggregating probabilistic forecasts using forecasters' meta-predictions about what other forecasters will predict. We test the performance of an extremised version of our algorithm against current forecasting approaches in the literature and show that our algorithm significantly outperforms all other approaches on a large collection of 500 binary decision problems varying in five levels of difficulty. The success of our algorithm demonstrates the potential of using meta-predictions to leverage latent expertise in environments where forecasters' expertise cannot otherwise be easily identified.
