3/3—Pfizer vaccine prevents asymptomatic infections
Single-dose BNT162b2 [Pfizer] vaccine protects against asymptomatic SARS-CoV-2 infection
[Preprint, via Johns Hopkins.] Researchers from the University of Cambridge and Public Health England published (preprint) findings from a study on the efficacy of the Pfizer-BioNTech vaccine in preventing asymptomatic SARS-CoV-2 infection. They identified 26 positive results out of 3,252 total tests in unvaccinated healthcare workers (0.80%), compared to 13 positive tests out of 3,535 tests (0.37%) among HCWs vaccinated less than 12 days after their first dose and 4 out of 1,989 tests (0.20%) among HCWs who received their first dose 12 days or more before the test. This corresponds to a statistically significant decrease in infection risk among vaccinated HCWs. Viral loads in vaccinated HCWs tended to be lower than in unvaccinated HCWs, although these results were not statistically significant. While not a placebo-controlled and randomized clinical trial, this study does provide real-world evidence that the Pfizer-BioNTech vaccine could provide protection against infection.
BNT162b2 [Pfizer] mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting
In this study, data from Israel’s largest health care organization were used to evaluate the effectiveness of the BNT162b2 mRNA vaccine. All persons who were newly vaccinated […] were matched to unvaccinated controls in a 1:1 ratio. Each study group included 596,618 persons. Estimated vaccine effectiveness for the study outcomes at days 14 through 20 after the first dose and at 7 or more days after the second dose was as follows: for documented infection, 46% (95% confidence interval [CI], 40 to 51) and 92% (95% CI, 88 to 95); for symptomatic Covid-19, 57% (95% CI, 50 to 63) and 94% (95% CI, 87 to 98); for hospitalization, 74% (95% CI, 56 to 86) and 87% (95% CI, 55 to 100); and for severe disease, 62% (95% CI, 39 to 80) and 92% (95% CI, 75 to 100), respectively. Estimated effectiveness in preventing death from Covid-19 was 72% (95% CI, 19 to 100) for days 14 through 20 after the first dose. This study in a nationwide mass vaccination setting suggests that the BNT162b2 mRNA vaccine is effective for a wide range of Covid-19–related outcomes, a finding consistent with that of the randomized trial.
Staying Ahead of the Variants: Policy Recommendations to Identify and Manage Current and Future Variants of Concern
[Johns Hopkins report.] Priority recommendations:
Maintain Policies that Slow Transmission: Variants will continue to emerge as the pandemic unfolds, but the best chance of minimizing their frequency and impact will be to continue public health measures that reduce transmission. This includes mask mandates, social distancing requirements, and limited gatherings.
Prioritize Contact Tracing and Case Investigation for Data Collection: Cases of variants of concern should be prioritized for contact tracing.
Develop a Genomic Surveillance Strategy: To guide the public health response, maximize resources, and ensure an equitable distribution of benefits, the US Department of Health and Human Services should develop a national strategy for genomic surveillance to implement and direct a robust SARS-CoV-2 genomic surveillance program.
Improve Coordination for Genomic Surveillance and Characterization: There are several factors in creating a successful genomic surveillance and characterization network. Clear leadership and coordination will be necessary.
Future scenarios for the COVID-19 pandemic
We do not yet know if, and when, revaccination with current or new COVID-19 vaccines will be required since the duration of immunological protection and the efficacy against emergent SARS-CoV-2 variants remain unknown. With such uncertainties, we should not assume that recent scientific progress on COVID-19 diagnostics, vaccines, and treatments will end the pandemic. The world is likely to have many more years of COVID-19 decision making ahead—there is no quick solution available at present.
Forecasting for COVID-19 has failed
Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, consideration of only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects, and selective reporting are some of the causes of these failures. [Looking at] performance of four data-driven models […], all models failed in terms of accuracy; for the majority of states, this figure was less than 20% [see Figure]. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence.