Measuring vaccine efficacy against infection and disease in clinical trials: sources and magnitude of bias in COVID-19 vaccine efficacy estimates

BackgroundPhase III trials have estimated COVID-19 vaccine efficacy (VE) against symptomatic and asymptomatic infection. We explore the direction and magnitude of potential biases in these estimates and their implications for vaccine protection against infection and against disease in breakthrough infections. MethodsWe developed a mathematical model that accounts for natural and vaccine-induced immunity, changes in serostatus and imperfect sensitivity and specificity of tests for infection and antibodies. We estimated expected biases in VE against symptomatic, asymptomatic and any SARS-CoV-2 infections and against disease following infection for a range of vaccine characteristics and measurement approaches, and the likely overall biases for published trial results that included asymptomatic infections. ResultsVE against asymptomatic infection measured by PCR or serology is expected to be low or negative for vaccines that prevent disease but not infection. VE against any infection is overestimated when asymptomatic infections are less likely to be detected than symptomatic infections and the vaccine protects against symptom development. A competing bias towards underestimation arises for estimates based on tests with imperfect specificity, especially when testing is performed frequently. Our model indicates considerable uncertainty in Oxford-AstraZeneca ChAdOx1 and Janssen Ad26.COV2.S VE against any infection, with slightly higher than published, bias-adjusted values of 59.0% (95% uncertainty interval [UI] 38.4 to 77.1) and 70.9% (95% UI 49.8 to 80.7) respectively. ConclusionMultiple biases are likely to influence COVID-19 VE estimates, potentially explaining the observed difference between ChAdOx1 and Ad26.COV2.S vaccines. These biases should be considered when interpreting both efficacy and effectiveness study results.

Reconstructing the COVID-19 epidemic in Delhi, India: infection attack rate and reporting of deaths

India reported over 10 million COVID-19 cases and 149,000 deaths in 2020. To estimate exposure and the potential for further spread, we used a SARS-CoV-2 transmission model fit to seroprevalence data from three serosurveys in Delhi and the time-series of reported deaths to reconstruct the epidemic. The cumulative proportion of the population estimated infected was 48.7% (95% CrI 22.1% - 76.8%) by end-September 2020. Using an age-adjusted overall infection fatality ratio (IFR) based on age-specific estimates from mostly high-income countries (HICs), we estimate that 15.0% (95% CrI 9.3% - 34.0%) of COVID-19 deaths were reported. This indicates either under-reporting of COVID-19 deaths and/or a lower age-specific IFR in India compared with HICs. Despite the high attack rate of SARS-CoV-2, a third wave occurred in late 2020, suggesting that herd immunity was not yet reached. Future dynamics will strongly depend on the duration of immunity and protection against new variants.