Effectiveness of Vaccination and Previous Infection Against Omicron Infection and Severe Outcomes in Children Under 12 Years of Age (preprint)

Background: Data on the protection conferred by vaccination and previous infection against omicron infection and severe outcomes in children can inform prevention strategies. Methods: We obtained vaccination records and clinical outcomes for 1,368,721 North Carolina residents 11 years of age or younger from October 29, 2021 to January 6, 2023. We used Cox regression to estimate the time varying effects of primary and booster vaccination and previous infection on the risks of omicron infection, hospitalization, and death. Results: For children 5 to 11 years of age, the effectiveness of primary vaccination against infection was 59.9% (95% confidence interval [CI], 58.5 to 61.2), 33.7% (95% CI, 32.6 to 34.8), and 14.9% (95% CI, 12.3 to 17.5) at 1, 4 and 10 months after the first dose; the effectiveness of a monovalent or bivalent booster dose after 1 month was 24.4% (95% CI, 14.4 to 33.2) or 76.7% (95% CI, 45.7 to 90.0); and the effectiveness of omicron infection against reinfection was 79.9% (95% CI, 78.8 to 80.9) and 53.9% (95% CI, 52.3 to 55.5) after 3 and 6 months, respectively. For children 0 4 years of age, the effectiveness of primary vaccination against infection was 63.8% (95% CI, 57.0 to 69.5) and 58.1% (95% CI, 48.3 to 66.1) at 2 and 5 months after the first dose, and the effectiveness of omicron infection against reinfection was 77.3% (95% CI, 75.9 to 78.6) and 64.7% (95% CI, 63.3 to 66.1) after 3 and 6 months, respectively. For both age groups, vaccination and previous infection had better effectiveness against hospitalization and death than against infection. Conclusions: Vaccination was effective against omicron infection and severe outcomes in children under the age of 12 years, although the effectiveness decreased over time. Bivalent boosters were more effective than monovalent boosters. Immunity acquired via omicron infection was very high and waned gradually over time.

Reliably Assessing Duration of Protection for COVID-19 Vaccines (preprint)

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.

Effectiveness of Covid-19 Vaccines in the United States Over 9 Months: Surveillance Data from the State of North Carolina (preprint)

Background: The duration of protection afforded by Covid19 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. Methods: We extracted data on vaccination histories and clinical outcomes (Covid19, hospitalization, death) for the period from December 13, 2020 through September 8, 2021 by linking data from the North Carolina COVID19 Surveillance System and COVID19 Vaccine Management System covering ~10.6 million residents statewide. We used the Kaplan-Meier method to estimate the effectiveness of the BNT162b2 (Pfizer), mRNA-1273 (Moderna), and Ad26.COV2.S (Janssen) vaccines in reducing the incidence of Covid19 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 Covid19, hospitalization, and death, as a function of time elapsed since the first dose. Results: For 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). Conclusions: The 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 Covid19. 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 month after the first dose.

Evaluating Effectiveness of Public Health Intervention Strategies for Mitigating COVID-19 Pandemic (preprint)

Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global public health challenge. In the United States (US), state governments have implemented various non-pharmaceutical interventions (NPIs), such as physical distance closure (lockdown), stay-at-home order, mandatory facial mask in public in response to the rapid spread of COVID-19. To evaluate the effectiveness of these NPIs, we propose a nested case-control design with propensity score weighting under the quasi-experiment framework to estimate the average intervention effect on disease transmission across states. We further develop a method to test for factors that moderate intervention effect to assist precision public health intervention. Our method takes account of the underlying dynamics of disease transmission and balance state-level pre-intervention characteristics. We prove that our estimator provides causal intervention effect under assumptions. We apply this method to analyze US COVID-19 incidence cases to estimate the effects of six interventions. We show that lockdown has the largest effect on reducing transmission and reopening bars significantly increase transmission. States with a higher percentage of non-white population are at greater risk of increased $R_t$ associated with reopening bars.

Evaluating Vaccine Efficacy Against SARS-CoV-2 Infection (preprint)

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.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)

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. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse 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. f

Evaluating the Long-Term Efficacy of COVID-19 Vaccines (preprint)

Large-scale deployment of safe and durably effective vaccines can halt the COVID-19 pandemic. However, the high vaccine efficacy reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about two months 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 placebo recipients to the vaccine arm according to priority groups. Here, we show how to estimate potentially time-varying placebo-controlled vaccine efficacy in this type of staggered vaccination of placebo recipients. In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term vaccine efficacy.

Evaluating the Efficacy of COVID-19 Vaccines (preprint)

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, 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.

Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies (preprint)

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.