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Decision-making about booster dosing for COVID-19 vaccine recipients hinges on reliable methods for evaluating the longevity of vaccine protection. We show that modeling of protection as a piecewise linear function of time since vaccination for the log hazard ratio of the vaccine effect provides more reliable estimates of vaccine effectiveness at the end of an observation period and also more reliably detects plateaus in protective effectiveness as compared with the traditional method of estimating a constant vaccine effect over each time period. This approach will be useful for analyzing data pertaining to COVID-19 vaccines and other vaccines where rapid and reliable understanding of vaccine effectiveness over time is desired.
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BackgroundThe duration of protection afforded by Covid-19 vaccines in the United States is unclear. Whether the recent increase of breakthrough infections was caused by waning immunity to the primary vaccination or by emergence of new variants that are more highly transmissible is also unknown. MethodsWe extracted data on vaccination histories and clinical outcomes (Covid-19, hospitalization, death) for the period from December 13, 2020 through September 8, 2021 by linking data from the North Carolina COVID-19 Surveillance System and COVID-19 Vaccine Management System covering [~]10.6 million residents statewide. We used the Kaplan-Meier method to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), and Ad26.COV2.S (Janssen) vaccines in reducing the incidence of Covid-19 over successive post-vaccination time periods, producing separate estimates for individuals vaccinated during different calendar periods. In addition, we used Cox regression with time-dependent vaccination status and time-varying hazard ratios to estimate the effectiveness of the three vaccines in reducing the hazard rates or current risks of Covid-19, hospitalization, and death, as a function of time elapsed since the first dose. ResultsFor the Pfizer two-dose regimen, vaccine effectiveness in reducing the current risk of Covid-19 ramps to a peak level of 94.9% (95% confidence interval [CI], 94.5 to 95.2) at 2 months (post the first dose) and drops to 70.1% (95% CI, 68.9 to 71.2) after 7 months; effectiveness in reducing the current risk of hospitalization ramps to a peak level of 96.4% (95% CI, 94.7 to 97.5) at 2 months and remains at 87.7% (95% CI, 84.3 to 90.4) at 7 months; effectiveness in reducing the current risk of death ramps to 95.9% (95% CI, 92.9 to 97.6) at 2 months and is maintained at 88.4% (95% CI, 83.0 to 92.1) at 7 months. For the Moderna two-dose regimen, vaccine effectiveness in reducing the current risk of Covid-19 ramps to a peak level of 96.0% (95% CI, 95.6 to 96.4) at 2 months and drops to 81.9% (95% CI, 81.0 to 82.7) after 7 months; effectiveness in reducing the current risk of hospitalization ramps to a peak level of 97.5% (95% CI, 96.3 to 98.3) at 2 months and remains at 92.3% (95% CI, 89.7 to 94.3) at 7 months; effectiveness in reducing the current risk of death ramps to 96.0% (95% CI, 91.9 to 98.0) at 3 months and remains at 93.7% (95% CI, 90.2 to 95.9) at 7 months. For the Janssen one-dose regimen, effectiveness in reducing the current risk of Covid-19 ramps to a peak level of 79.0% (95% CI, 77.1 to 80.7) at 1 month and drops to 64.3% (95% CI, 62.3 to 66.1) after 5 months; effectiveness in reducing the current risk of hospitalization ramps to a peak level of 89.8% (95% CI, 78.8 to 95.1) at 2 months and stays above 80% through 5 months; effectiveness in reducing the current risk of death ramps to 89.4% (95% CI, 52.3 to 97.6) at 3 months and stays above 80% through 5 months. For all three vaccines, the ramping and waning patterns are similar for individuals who were vaccinated at different dates, and across various demographic subgroups (age, sex, race/ethnicity, geographic region, county-level vaccination rate). ConclusionsThe two mRNA vaccines are remarkably effective and durable in reducing the risks of hospitalization and death. The Janssen vaccine also offers a high level of protection against hospitalization and death. The Moderna vaccine is significantly more durable than the Pfizer vaccine in reducing the risk of Covid-19. Waning vaccine effectiveness is caused primarily by declining immunity rather than emergence of new variants. It would be worthwhile to investigate the effectiveness of the Janssen vaccine as a two-dose regimen, with the second dose given approximately 1-2 months after the first dose.
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Although interim results from several large placebo-controlled phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic COVID-19, it is unknown how effective the vaccines are in preventing people from becoming asymptomatically infected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against SARS-CoV-2 infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between two antibody or RT-PCR tests. Additional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment or crossover before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, staggered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies mimicking the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates. SummaryWe show how to estimate potentially waning efficacy of COVID-19 vaccines against SARS-CoV-2 infection using blood or nasal samples collected periodically from clinical trials with staggered enrollment of participants and crossover of placebo recipients.
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Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naive baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance StatementThis paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.
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Large-scale deployment of safe and durably effective vaccines can curtail the COVID-19 pandemic.1-3 However, the high vaccine efficacy (VE) reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about two months4-5 and thus does not pertain to long-term efficacy. To evaluate the duration of protection while allowing trial participants timely access to efficacious vaccine, investigators can sequentially cross participants over from the placebo arm to the vaccine arm according to priority groups. Here, we show how to estimate potentially time-varying placebo-controlled VE in this type of staggered vaccination of participants. In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term VE. Authors InformationDan-Yu Lin, Ph.D., is Dennis Gillings Distinguished Professor of Biostatistics, and Donglin Zeng, Ph.D., is Professor of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599-7420, USA. Peter B. Gilbert, Ph.D., is Member, Vaccine and Infectious Disease Division, Fred Hutch, Seattle, WA 98109-1024, USA. SummaryWe show how to estimate the potentially waning long-term efficacy of COVID-19 vaccines using data from randomized, placebo-controlled clinical trials with staggered enrollment of participants and sequential crossover of placebo recipients.
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A large number of studies are being conducted to evaluate the efficacy and safety of candidate vaccines against novel coronavirus disease-2019 (COVID-19). Most Phase 3 trials have adopted virologically confirmed symptomatic COVID-19 disease as the primary efficacy endpoint, although laboratory-confirmed SARS-CoV-2 is also of interest. In addition, it is important to evaluate the effect of vaccination on disease severity. To provide a full picture of vaccine efficacy and make efficient use of available data, we propose using SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19 as dual or triple primary endpoints. We demonstrate the advantages of this strategy through realistic simulation studies. Finally, we show how this approach can provide rigorous interim monitoring of the trials and efficient assessment of the durability of vaccine efficacy. SummaryTo increase statistical power and meet vaccine success criteria, we propose to evaluate the efficacy of COVID-19 vaccines by using the dual or triple primary endpoints of SARS-CoV-2 infection, symptomatic COVID-19, and severe COVID-19.
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Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making. We propose a robust and parsimonious survival-convolution model for predicting key statistics of COVID-19 epidemics (daily new cases). We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the infection rate and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only data in the early phase (two to three weeks after the outbreak). A fast rate of decline in Rt was observed and adopting mitigation strategies early in the epidemic was effective in reducing the infection rate in these two countries. The lockdown in Italy did not further accelerate the speed at which the infection rate decreases. The effective reproduction number has staggered around Rt = 1.0 for more than 2 weeks before decreasing to below 1.0, and the epidemic in Italy is currently under control. In the US, Rt significantly decreased during a 2-week period after the declaration of national emergency, but afterwards the rate of decrease is substantially slower. If the trend continues after May 1, the first wave of COVID-19 may be controlled by July 26 (CI: July 9 to August 27). However, a loss of temporal effect on infection rate (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (November 19 with less than 100 daily cases) and a total of more than 2 million cases.
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Aim To study the protective effects of limb remote ischemic postconditioning ( LRIP ) on is-chemic stroke rats under hyperglycemia and explore the mechanisms. Methods Rats were given 50% glucose (6 mL·kg-1 ) by intraperitoneal injection to get acute hyperglycemia model. Then middle cerebral artery oc-clusion ( MCAO) models were created. After blocking middle artery for 1. 5 h and reperfusion for 2 h, behav-ioral testing, infarct size of brain, NO concentration and SOD activity in the serum of those rats were detec- ted. Results LRIP could improve behavioral score, decrease the area of cerebral infarction, increase the concentration of NO and the SOD activity in serum of MCAO rats. Conclusion LRIP can relieve cerebral ischemia-reperfusion injury of MCAO rats under acute hyperglycemia.
