ABSTRACT
BackgroundSARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. MethodsNine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. FindingsAbsent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. ConclusionsResults from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.
ABSTRACT
What is already known about this topic?The highly transmissible SARS-CoV-2 Delta variant has begun to cause increases in cases, hospitalizations, and deaths in parts of the United States. With slowed vaccination uptake, this novel variant is expected to increase the risk of pandemic resurgence in the US in July--December 2021. What is added by this report?Data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant. These resurgences, which have now been observed in most states, were projected to occur across most of the US, coinciding with school and business reopening. Reaching higher vaccine coverage in July--December 2021 reduces the size and duration of the projected resurgence substantially. The expected impact of the outbreak is largely concentrated in a subset of states with lower vaccination coverage. What are the implications for public health practice?Renewed efforts to increase vaccination uptake are critical to limiting transmission and disease, particularly in states with lower current vaccination coverage. Reaching higher vaccination goals in the coming months can potentially avert 1.5 million cases and 21,000 deaths and improve the ability to safely resume social contacts, and educational and business activities. Continued or renewed non-pharmaceutical interventions, including masking, can also help limit transmission, particularly as schools and businesses reopen.
ABSTRACT
ObjectiveThe COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions. Materials and MethodsUsing real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions. Results162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with [≥] 2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels. DiscussionPhysical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff. ConclusionSimulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.
ABSTRACT
BackgroundMonoclonal antibodies (mAbs) against SARS-CoV-2 are a promising treatment for limiting the progression of COVID-19 and decreasing strain on hospitals. Their use, however, remains limited, particularly in disadvantaged populations. MethodsElectronic health records were reviewed from SARS-CoV-2 patients at a single medical center in the United States that initiated mAb infusions in January 2021 with the support of the U.S. Department of Health and Human Services National Disaster Medical System. Patients who received mAbs were compared to untreated patients from the time period before mAb availability who met eligibility criteria for mAb treatment. We used logistic regression to measure the effect of mAb treatment on the risk of hospitalization or emergency department (E.D.) visit within 30 days of laboratory-confirmed COVID-19. ResultsOf 598 COVID-19 patients, 270 (45%) received bamlanivimab and 328 (55%) were untreated. Two hundred and thirty-one patients (39%) were Hispanic. Among treated patients, 5/270 (1.9%) presented to the E.D. or required hospitalization within 30 days of a positive SARS-CoV-2 test, compared to 39/328 (12%) untreated patients (p<0.001). After adjusting for age, gender, and comorbidities, the risk of E.D. visit or hospitalization was 82% lower in mAb-treated patients compared to untreated patients (95% confidence interval [CI]: 66%-94%). ConclusionsIn this diverse, real-world COVID-19 patient population, mAb treatment significantly decreased the risk of subsequent E.D. visit or hospitalization. Broader treatment with mAbs, including in disadvantaged patient populations, can decrease the burden on hospitals and should be facilitated in all populations in the United States to ensure health equity. SummaryIn a diverse, real-world COVID-19 patient population, treatment with monoclonal antibodies significantly decreased the risk of subsequent emergency department visit or hospitalization within 30 days of a positive SARS-CoV-2 viral test.
ABSTRACT
BackgroundThe COVID-19 pandemic caught the globe unprepared without targeted medical countermeasures, such as therapeutics, to target the emerging SARS-CoV-2 virus. However, in recent months multiple monoclonal antibody therapeutics to treat COVID-19 have been authorized by the U.S. Food and Drug Administration (FDA) under Emergency Use Authorization (EUA). Despite these authorizations and promising clinical trial efficacy results, monoclonal antibody therapies are currently underutilized as a treatment for COVID-19 across the U.S. Many barriers exist when deploying a new infused therapeutic during an ongoing pandemic with limited resources and staffing, and it is critical to better understand the process and site requirements of incorporating monoclonal antibody infusions into pandemic response activities. MethodsWe examined the monoclonal antibody infusion site process components, resources, and requirements during the COVID-19 pandemic using data from three initial infusion sites at medical centers in the U.S. supported by the National Disaster Medical System. A descriptive analysis was conducted using process assessment metrics to inform recommendations to strengthen monoclonal antibody infusion site implementation. ResultsThe monoclonal antibody infusion sites varied in physical environment and staffing models due to state polices, infection control mechanisms, and underlying medical system structure, but exhibited a common process workflow. Sites operationalized an infusion process staffing model with at least two nurses per ten infusion patients. Monoclonal antibody implementation success factors included tailoring the infusion process to the patient community, strong engagement with local medical providers, batch preparing the therapy before patient arrival, placing the infusion center in proximity to emergency services, and creating procedures resilient to EUA changes. Infusion process challenges stemmed from confirming patient SARS-CoV-2 positivity, strained staff, scheduling needs, and coordination with the pharmacy for therapy preparation. ConclusionsInfusion site processes are most effective when integrated into the pre-existing pandemic response ecosystems and can be implemented with limited staff and physical resources. As the pandemic and policy tools such as EUAs evolve, monoclonal antibody infusion processes must also remain adaptable, as practice changes directly affect resources, staffing, timing, and workflows. Future use may be aided by incorporating innovative emergency deployment techniques, such as vehicle and home-based therapy administration, and by developing drug delivery mechanisms that alleviate the need for observed intravenous infusions by medically-accredited staff.
ABSTRACT
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.
