Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

Behavioral Health Challenges in an Acute Care Hospital During the Novel COVID-19 Pandemic.

The COVID-19 pandemic has impacted all areas of health care and posed unique challenges in managing patient care. COVID-19 precautions were taken in every practice setting. The placement and care of patients with a behavioral health (BH) diagnosis have revealed unforeseen acute care challenges during this pandemic. There were new barriers for acute care hospitals in the timely transition of care of BH patients who tested positive for COVID-19. The purpose of this article is to highlight the challenges encountered by an acute care hospital to provide extended care for BH patients during the COVID-19 pandemic. Immediately following COVID-19 recovery, the patients could not be placed in a licensed BH facility for continued care because of state COVID-19 quarantine executive order and public health mandate for congregate facilities. This mandate resulted in the acute care hospitals in Georgia adapting in real time to provide extended inpatient care for BH patients. This article presents 2 case studies, outlining challenges, interventions, and lessons learned, of how one acute care hospital ensured the best outcomes for extended-stay BH patients admitted with a positive COVID-19 diagnosis.

Estimating the impact of self-management education, influenza vaccines, nebulizers, and spacers on health utilization and expenditures for Medicaid-enrolled children with asthma.

INTRODUCTION: We quantify the effect of a set of interventions including asthma self-management education, influenza vaccination, spacers, and nebulizers on healthcare utilization and expenditures for Medicaid-enrolled children with asthma in New York and Michigan. METHODS: We obtained patients' data from Medicaid Analytic eXtract files and evaluated patients with persistent asthma in 2010 and 2011. We used difference-in-difference regression to quantify the effect of the intervention on the probability of asthma-related healthcare utilization, asthma medication, and utilization costs. We estimated the average change in outcome measures from pre-intervention/intervention (2010) to post-intervention (2011) periods for the intervention group by comparing this with the average change in the control group over the same time horizon. RESULTS: All of the interventions reduced both utilization and asthma medication costs. Asthma self-management education, nebulizer, and spacer interventions reduced the probability of emergency department (20.8-1.5%, 95%CI 19.7-21.9% vs. 0.5-2.5%, respectively) and inpatient (3.5-0.8%, 95%CI 2.1-4.9% vs. 0.4-1.2%, respectively) utilizations. Influenza vaccine decreased the probability of primary care physician (6-3.5%, 95%CI 4.4-7.6% vs. 1.5-5.5%, respectively) visit. The reductions varied by state and intervention. CONCLUSIONS: Promoting asthma self-management education, influenza vaccinations, nebulizers, and spacers can decrease the frequency of healthcare utilization and asthma-related expenditures while improving medication adherence.

Return on investment of self-management education and home visits for children with asthma.

OBJECTIVE: Priorities of the Centers for Disease Control and Prevention's 6|18 Initiative include outpatient asthma self-management education (ASME) and home-based asthma visits (home visit) as interventions for children with poorly-controlled asthma. ASME and home visit intervention programs are currently not widely available. This project was to assess the economic sustainability of these programs for state asthma control programs reimbursed by Medicaid. METHODS: We used a simulation model based on parameters from the literature and Medicaid claims, controlling for regression to the mean. We modeled scenarios under various selection criteria based on healthcare utilization and age to forecast the return on investment (ROI) using data from New York. The resulting tool is available in Excel or Python. RESULTS: Our model projected health improvement and cost savings for all simulated interventions. Compared against home visits alone, the simulated ASME alone intervention had a higher ROI for all healthcare utilization and age scenarios. Savings were primarily highest in simulated program participants who had two or more asthma-related emergency department visits or one inpatient visit compared to those participants who had one or more asthma-related emergency department visits. Segmenting the selection criteria by age did not significantly change the results. CONCLUSIONS: This model forecasts reduced healthcare costs and improved health outcomes as a result of ASME and home visits for children with high urgent healthcare utilization (more than two emergency department visits or one inpatient hospitalization) for asthma. Utilizing specific selection criteria, state based asthma control programs can improve health and reduce healthcare costs.

Assessing Health and Wellness Outcomes of Medicaid-Enrolled Infants Born to Adolescent Mothers.

OBJECTIVE: To assess the effect of adolescent birth on the health and wellness of these infants within their first year of life. METHODS: Our study focused on 2011 Medicaid births nationwide. The study group (infants born to adolescents, aged 10 to 19 at time of birth) was matched with infants born to adults (aged 20 to 44 at time of birth), based on demographics. Statistical tests (proportion test and Poisson test) were used to compare the outcomes of these two groups to determine if differences were significant. RESULTS: The outcomes assessed were: low birth weight (LBW), substance exposure, foster care, health status, infant mortality, emergency department (ED) visits, and wellness visits. Of the 68,562 infant pairs included in the study, we found statistically significant higher rates of LBW (P ≤ 0·005), infant mortality (P = 0·05), and ED visits (P ≤ 0·005) for infants born to adolescents at the 95% confidence interval. The rate of wellness visits for all infants was well below the recommended amount. Additional differences were found at the race/ethnicity and urbanicity levels. CONCLUSION FOR PRACTICE: Infants born to adolescents had a higher rate of ED visits within the first year of life, however, the increased rates of LBW and mortality for the Medicaid population are not as significant as previous national studies suggest. Analysis of outcomes across stratification helped identify vulnerable populations (i.e. urban infants). Public health programs are urged to examine ED visits in infants born to adolescents among the Medicaid population. Improved health education or phone-based resources could help reduce unnecessary visits and reduce cost.

Medicaid Claims for Contraception Among Women With Medical Conditions After Release of the US Medical Eligibility Criteria for Contraceptive Use.

INTRODUCTION: The US Medical Eligibility Criteria for Contraceptive Use (MEC) identified 20 medical conditions that increase a woman's risk for adverse outcomes in pregnancy. MEC recommends that women with these conditions use long-acting, highly effective contraceptive methods. The objective of our study was to examine provision of contraception to women enrolled in Medicaid who had 1 or more of these 20 medical conditions METHODS: We used Medicaid Analytic Extract claims data to study Medicaid-enrolled women who were of reproductive age in the 2-year period before MEC's release (2008 and 2009) (N = 442,424) and the 2-year period after its release (2011 and 2012) (N = 533,619) for 14 states. We assessed 2 outcomes: provision of family planning management (FPM) and provision of highest efficacy methods (HEMs) for the entire study population and by health condition. The ratio of the after-MEC rate to the before-MEC rate was used to determine significance in MEC's uptake. RESULTS: Outcomes increased significantly from the before-MEC period to the after-MEC period for both FPM (1.06; lower bound confidence interval [CI], 1.05) and HEM (1.37; lower bound CI, 1.36) for a 1-sided hypothesis test. For the 19 of 20 conditions we were able to test for FPM, contraceptive use increased significantly for 12 conditions, with ratios ranging from 1.05 to 2.14. For the 16 of 20 conditions tested for HEM, contraception use increased significantly for all conditions, with ratios ranging from 1.19 to 2.80. CONCLUSION: Provision of both FPM and HEM increased significantly among women with high-risk health conditions from the before-MEC period (2008 and 2009) to the after-MEC period (2011 and 2012). Health policy makers and clinicians need to continue promotion of effective family planning management for women with high-risk conditions.

Quantifying and explaining accessibility with application to the 2009 H1N1 vaccination campaign.

Accessibility and equity across populations are important measures in public health. This paper is specifically concerned with potential spatial accessibility, or the opportunity to receive care as moderated by geographic factors, and with horizontal equity, or fairness across populations regardless of need. Both accessibility and equity were goals of the 2009 vaccination campaign for the novel H1N1a influenza virus, including during the period when demand for vaccine exceeded supply. Distribution system design can influence equity and accessibility at the local level. We develop a general methodology that integrates optimization, game theory, and spatial statistics to measure potential spatial accessibility across a network, where we quantify spatial accessibility by travel distance and scarcity. We estimate and make inference on local (census-tract level) associations between accessibility and geographic, socioeconomic, and health care infrastructure factors to identify potential inequities in vaccine accessibility during the 2009 H1N1 vaccination campaign in the U.S. We find that there were inequities in access to vaccine at the local level and that these were associated with factors including population density and health care infrastructure. Our methodology for measuring and explaining accessibility leads to policy recommendations for federal, state, and local public health officials. The spatial-specific results inform the development of equitable distribution plans for future public health efforts.

The effect of geographic access on severe health outcomes for pediatric asthma.

BACKGROUND: Access to medical care and severe pediatric asthma outcomes vary with geography, but the relationship between them has not been studied. OBJECTIVE: We sought to evaluate the relationship between geographic access and health outcomes for pediatric asthma. METHODS: The severe outcome measures include emergency department (ED) visits and hospitalizations for children with an asthma diagnosis in Georgia and North Carolina. We quantify asthma prevalence, outcome measures, and factors included in the statistical model using multiple data sources. We calculate geographic access to primary and asthma specialist care using optimization models. We estimate the association between outcomes and geographic access in the presence of other factors using logistic regression. The model is used to project the reduction in severe outcomes with improvement in access. RESULTS: The association between access and outcomes for pediatric asthma depends on the type of outcome measure, type of care, and variations in other factors. The expression of this association is also different for the 2 states. Access to primary care plays a larger role than access to specialist care in explaining Georgia ED visits, whereas the reverse applies for hospitalizations. In North Carolina access to both primary and specialist care are statistically significant in explaining the variability in ED visits. CONCLUSIONS: The variation in the association between estimated access and outcomes affects the projected reductions of severe outcomes with access improvement. Thus applying one intervention would not have the same level of improvement across geography. Interventions must be tailored to target regions with the potential to deliver the highest effect to gain maximum benefit.

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy.

Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and complementing them with the knowledge about variations in state-level health policies. This study introduces a modeling approach for small-area estimation of spatial access to pediatric primary care that is data "rich" and mathematically rigorous, integrating data and health policy in a systematic way. We illustrate the sensitivity of the model to policy decision making across large geographic regions by performing a systematic comparison of the estimates at the census tract and county levels for Georgia and California. Our results show the proposed approach is able to overcome limitations of other existing models by capturing patient and provider preferences and by incorporating possible changes in health policies. The primary finding is systematic underestimation of spatial access, and inaccurate estimates of disparities across population and across geography at the county level with respect to those at the census tract level with implications on where to focus and which type of interventions to consider.

An optimization framework for measuring spatial access over healthcare networks.

BACKGROUND: Measurement of healthcare spatial access over a network involves accounting for demand, supply, and network structure. Popular approaches are based on floating catchment areas; however the methods can overestimate demand over the network and fail to capture cascading effects across the system. METHODS: Optimization is presented as a framework to measure spatial access. Questions related to when and why optimization should be used are addressed. The accuracy of the optimization models compared to the two-step floating catchment area method and its variations is analytically demonstrated, and a case study of specialty care for Cystic Fibrosis over the continental United States is used to compare these approaches. RESULTS: The optimization models capture a patient's experience rather than their opportunities and avoid overestimating patient demand. They can also capture system effects due to change based on congestion. Furthermore, the optimization models provide more elements of access than traditional catchment methods. CONCLUSIONS: Optimization models can incorporate user choice and other variations, and they can be useful towards targeting interventions to improve access. They can be easily adapted to measure access for different types of patients, over different provider types, or with capacity constraints in the network. Moreover, optimization models allow differences in access in rural and urban areas.

Estimating prevalence of overweight or obese children and adolescents in small geographic areas using publicly available data.

INTRODUCTION: Interventions for pediatric obesity can be geographically targeted if high-risk populations can be identified. We developed an approach to estimate the percentage of overweight or obese children aged 2 to 17 years in small geographic areas using publicly available data. We piloted our approach for Georgia. METHODS: We created a logistic regression model to estimate the individual probability of high body mass index (BMI), given data on the characteristics of the survey participants. We combined the regression model with a simulation to sample subpopulations and obtain prevalence estimates. The models used information from the 2001-2010 National Health and Nutrition Examination Survey, the 2010 Census, and the 2010 American Community Survey. We validated our results by comparing 1) estimates for adults in Georgia produced by using our approach with estimates from the Centers for Disease Control and Prevention (CDC) and 2) estimates for children in Arkansas produced by using our approach with school examination data. We generated prevalence estimates for census tracts in Georgia and prioritized areas for interventions. RESULTS: In DeKalb County, the mean prevalence among census tracts varied from 27% to 40%. For adults, the median difference between our estimates and CDC estimates was 1.3 percentage points; for Arkansas children, the median difference between our estimates and examination-based estimates data was 1.7 percentage points. CONCLUSION: Prevalence estimates for census tracts can be different from estimates for the county, so small-area estimates are crucial for designing effective interventions. Our approach validates well against external data, and it can be a relevant aid for planning local interventions for children.

Potential Impact of a Diagnostic Test for Detecting Prepatent Guinea Worm Infections in Dogs.

Chad has seen a considerable reduction in cases of Guinea worm disease (or dracunculiasis) in domestic dogs in recent years. Tethering of dogs and application of Abate® larvicide to water sources appear to have contributed to this progress, but with 767 reported dog cases in 2021, accelerating elimination of the disease in Chad may require additional tools. We investigate the potential benefits of a hypothetical diagnostic test that could be capable of detecting prepatent infections in dogs. We adapt an agent-based simulation model for forecasting the impact of interventions on guinea worm disease in dogs to examine the interaction of multiple test factors including test accuracy, when the test can detect infection, dog selection, and dog-owner compliance with tethering recommendations. We find that a diagnostic test could be successful if used in conjunction with existing interventions, and elimination can be achieved within 2 years with 80% or higher test sensitivity, 90% or higher specificity, systematic testing of each dog twice per year, and more than 90% long-term tethering compliance when a dog tests positive or a worm is emerging. Because of the long incubation period of Guinea worm disease (10-14 months) and the fact that no treatment exists, the benefits of the test rely on the testing rollout and response of dog owners. If the test could estimate the timing of worm emergence, long-term tethering could be eliminated and infected dogs could be tethered only when the worms are expected, minimizing the related resources (human and financial) to support the intervention.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina.

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Risk score models for urinary tract infection hospitalization.

Annually, urinary tract infections (UTIs) affect over a hundred million people worldwide. Early detection of high-risk individuals can help prevent hospitalization for UTIs, which imposes significant economic and social burden on patients and caregivers. We present two methods to generate risk score models for UTI hospitalization. We utilize a sample of patients from the insurance claims data provided by the Centers for Medicare and Medicaid Services to develop and validate the proposed methods. Our dataset encompasses a wide range of features, such as demographics, medical history, and healthcare utilization of the patients along with provider quality metrics and community-based metrics. The proposed methods scale and round the coefficients of an underlying logistic regression model to create scoring tables. We present computational experiments to evaluate the prediction performance of both models. We also discuss different features of these models with respect to their impact on interpretability. Our findings emphasize the effectiveness of risk score models as practical tools for identifying high-risk patients and provide a quantitative assessment of the significance of various risk factors in UTI hospitalizations such as admission to ICU in the last 3 months, cognitive disorders and low inpatient, outpatient and carrier costs in the last 6 months.

Evaluating the potential impact of rubella-containing vaccine introduction on congenital rubella syndrome in Afghanistan, Dem. Republic of Congo, Ethiopia, Nigeria, and Pakistan: A mathematical modeling study.

We assessed the potential impact of introducing rubella-containing vaccine (RCV) on congenital rubella syndrome (CRS) incidence in Afghanistan (AFG), Democratic Republic of Congo (COD), Ethiopia (ETH), Nigeria (NGA), and Pakistan (PAK). We simulated several RCV introduction scenarios over 30 years using a validated mathematical model. Our findings indicate that RCV introduction could avert between 86,000 and 535,000 CRS births, preventing 2.5 to 15.8 million disability-adjusted life years. AFG and PAK could reduce about 90% of CRS births by introducing RCV with current measles routine coverage and executing supplemental immunization activities (SIAs). However, COD, NGA, and ETH must increase their current routine vaccination coverage to reduce CRS incidence significantly. This study showcases the potential benefits of RCV introduction and reinforces the need for global action to strengthen immunization programs.

Evaluation of telemedicine for screening of diabetic retinopathy in the Veterans Health Administration.

OBJECTIVE: To explore the cost-effectiveness of telemedicine for the screening of diabetic retinopathy (DR) and identify changes within the demographics of a patient population after telemedicine implementation. DESIGN: A retrospective medical chart review (cohort study) was conducted. PARTICIPANTS: A total of 900 type 1 and type 2 diabetic patients enrolled in a medical system with a telemedicine screening program for DR. METHODS: The cost-effectiveness of the DR telemedicine program was determined by using a finite-horizon, discrete time, discounted Markov decision process model populated by parameters and testing frequency obtained from patient records. The model estimated the progression of DR and determined average quality-adjusted life years (QALYs) saved and average additional cost incurred by the telemedicine screening program. MAIN OUTCOME MEASURES: Diabetic retinopathy, macular edema, blindness, and associated QALYs. RESULTS: The results indicate that telemedicine screening is cost-effective for DR under most conditions. On average, it is cost-effective for patient populations of >3500, patients aged <80 years, and all racial groups. Observable trends were identified in the screening population since the implementation of telemedicine screening: the number of known DR cases has increased, the overall age of patients receiving screenings has decreased, the percentage of nonwhites receiving screenings has increased, the average number of miles traveled by a patient to receive a screening has decreased, and the teleretinal screening participation is increasing. CONCLUSIONS: The current teleretinal screening program is effective in terms of being cost-effective and increasing population reach. Future screening policies should give consideration to the age of patients receiving screenings and the system's patient pool size because our results indicate it is not cost-effective to screen patients aged older than 80 years or in populations with <3500 patients.

Evaluating the Effectiveness of Potential Interventions for Guinea Worm Disease in Dogs in Chad Using Simulations.

Guinea worm (GW) disease (or dracunculiasis) is currently transmitted among dogs in Chad, which presents risks for the human population. We studied how interventions implemented at different levels might reduce the spread of GW disease (geographically and over time) and what levels of interventions might accelerate elimination. We built a multiple-water-source agent-based simulation model to analyze the disease transmission among dogs in Chad, as well as in geographic district clusters, and validated it using local infection data. We considered two interventions: 1) tethering, where infected dogs are kept on a leash during periods of infectivity, and 2) Abate®, under which the water source is treated to reduce infectivity. Our results showed that elimination (0 dog infections) is most likely achieved within 5 years with extremely high levels of tethering (95%) and Abate (90%), when intervention levels are uniform across district clusters. We used an optimization model to determine an improved strategy, with intervention levels which minimize the number of dogs newly infected in the 6th year, under limitations on intervention levels across clusters; the number of dogs infected after 5 years of intervention could be reduced by approximately 220 dogs with an optimized strategy. Finally, we presented strategies that consider fairness based on intervention resource levels and outcomes. Increased tethering and Abate resources above historical levels are needed to achieve the target of GW disease elimination; optimization methods can inform how best to target limited resources and reach elimination faster.

Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub.

Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.

Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.

Importance: COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective: To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting: The entire United States. Participants: None. Exposure: Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures: Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results: From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance: COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.

Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.