Dataset Information

Interim estimates in null models of COVID-19 vaccine effectiveness.

ABSTRACT: Recently released interim numbers from advanced vaccine candidate clinical trials suggest that a COVID-19 vaccine effectiveness (VE) above 90% is achievable. However, SARS-CoV-2 transmission dynamics is highly heterogeneous and exhibits localized bursts of transmission, which may lead to sharp localized peaks in the number of new cases, often followed by longer periods of low incidence. Here we show that, for interim estimates of VE, this characteristic burstiness in SARS-CoV-2 infection dynamics may introduce a strong positive bias in VE. Specifically, we generate null models of vaccine effectiveness, i.e., random models with burstiness that over longer times converge to exactly zero VE, but that for interim times frequently produce apparent VE near 100%. As an example, by following the relevant clinical trial protocol, we can reproduce recently reported interim outcomes from an ongoing phase 3 clinical trial of a RNA based vaccine candidate. Thus, to avoid potential random biases in VE, it is suggested that interim estimates on COVID-19 vaccine effectiveness should control for the intrinsic inhomogeneity in both SARS-CoV-2 infection dynamics and in reported cases.

SUBMITTER: Lisewski AM

PROVIDER: S-EPMC7970752 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPhase 3 trials found mRNA-1273 was highly effective in preventing COVID-19. We conducted a prospective cohort study at Kaiser Permanente Southern California (KPSC) to determine the real-world vaccine effectiveness (VE) of mRNA-1273 in preventing COVID-19 infection and severe disease.MethodsFor this planned interim analysis, individuals aged ≥18 years receiving 2 doses of mRNA-1273 ≥24 days apart (18/12/2020-31/03/2021) were 1:1 matched to randomly selected unvaccinated individuals by age, sex, and race/ethnicity, with follow-up through 30/06/2021. Outcomes were COVID-19 infection (SARS-CoV-2 positive molecular test or COVID-19 diagnosis code) or severe disease (COVID-19 hospitalization and COVID-19 hospital death). Adjusted hazard ratios (aHR) and confidence intervals (CI) for COVID-19 outcomes comparing vaccinated and unvaccinated individuals were estimated by Cox proportional hazards models accounting for multiple comparisons. Adjusted VE was calculated as (1-aHR)x100. Whole genome sequencing was performed on SARS-CoV-2 positive specimens from the KPSC population.FindingsThis analysis included 352,878 recipients of 2 doses of mRNA-1273 matched to 352,878 unvaccinated individuals. VE (99·3% CI) against COVID-19 infection was 87·4% (84·8-89·6%). VE against COVID-19 hospitalization and hospital death was 95·8% (90·7-98·1%) and 97·9% (66·9-99·9%), respectively. VE was higher against symptomatic (88·3% [98·3% CI: 86·1-90·2%]) than asymptomatic COVID-19 (72·7% [53·4-84·0%]), but was generally similar across age, sex, and racial/ethnic subgroups. VE among individuals with history of COVID-19 ranged from 8·2-33·6%. The most prevalent variants were Alpha (41·6%), Epsilon (17·5%), Delta (11·5%), and Gamma (9·1%), with Delta increasing to 54·0% of variants by June 2021.InterpretationThese interim results provide reassuring evidence of the VE of 2 doses of mRNA-1273 across age, sex, and racial/ethnic subgroups, and against asymptomatic and symptomatic COVID-19, and severe COVID-19 outcomes. Among individuals with history of COVID-19, mRNA-1273 vaccination may offer added protection beyond immunity acquired from prior infection. Longer follow-up is needed to fully evaluate VE of mRNA-1273 against emerging SARS-CoV-2 variants.FundingModerna Inc.

Project description:In the last two years, the coronavirus disease 19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a scientific and social challenge worldwide. Vaccines have been the most effective intervention for reducing virus transmission and disease severity. However, virus genetic variants are still circulating among vaccinated individuals with different symptomatology disease cases. Understanding the protective or disease associated mechanisms in vaccinated individuals is relevant to advance in vaccine development and implementation. To address this objective, serum protein profiles were characterized by quantitative proteomics and data analysis algorithms in four cohorts of vaccinated individuals uninfected and SARS-CoV-2 infected with asymptomatic, nonsevere and severe disease symptomatology. The results showed that immunoglobulins were the most overrepresented proteins in infected cohorts when compared to PCR-negative individuals. The immunoglobulin profile varied between different infected cohorts and correlated with protective or disease associated capacity. Overrepresented immunoglobulins in PCR-positive individuals correlated with protective response against SARS-CoV-2, other viruses, and thrombosis in asymptomatic cases. In nonsevere cases, correlates of protection against SARS-CoV-2 and HBV together with risk of myasthenia gravis and allergy and autoantibodies were observed. Patients with severe symptoms presented risk for allergy, chronic idiopathic thrombocytopenic purpura, and autoantibodies. The analysis of underrepresented immunoglobulins in PCR-positive compared to PCR-negative individuals identified vaccine-induced protective epitopes in various coronavirus proteins including the Spike receptor-binding domain RBD. Non-immunoglobulin proteins were associated with COVID-19 symptoms and biological processes. These results evidence host-associated differences in response to vaccination and the possibility of improving vaccine efficacy against SARS-CoV-2.

Project description:Highlights • COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof.• The unpredictable non-stationarity of the testing frames behind the figures reported by national authorities sets a fundamental limitation to any theoretical approach.• The time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by national authorities all over the world. However, despite the diversity of novel theoretical approaches and the comprehensiveness of many widely established models, the official figures that recount the course of the outbreak still sketch a largely elusive and intimidating picture. Here we show unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof. Our analysis naturally leads us to establish that there exists a fundamental limitation to any theoretical approach, namely the unpredictable non-stationarity of the testing frames behind the reported figures. However, we show how such bias can be quantified self-consistently and employed to mine useful and accurate information from the data. In particular, we describe how the time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. The importance of testing early and resolutely appears as a natural corollary of our analysis, as countries that tested massively at the start clearly had their true peak earlier and less deaths overall.

Dataset Information

Interim estimates in null models of COVID-19 vaccine effectiveness.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure