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

ImportanceCOVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. ObjectiveTo 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). DesignThe 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. SettingThe entire United States. ParticipantsNone. ExposureAnnually 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 measuresEnsemble 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. ResultsFrom 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 RelevanceCOVID-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. Key pointsO_ST_ABSQuestionC_ST_ABSWhat is the likely impact of COVID-19 from April 2023-April 2025 and to what extent can vaccination reduce hospitalizations and deaths? FindingsUnder plausible assumptions about viral evolution and waning immunity, COVID-19 will likely cause annual epidemics peaking in November-January over the two-year projection period. Though significant, hospitalizations and deaths are unlikely to reach levels seen in previous winters. The projected health impacts of COVID-19 are reduced by 10-20% through moderate use of reformulated vaccines. MeaningCOVID-19 is projected to remain a significant public health threat. Annual vaccination can reduce morbidity, mortality, and strain on health systems.

The public health impact of Paxlovid COVID-19 treatment in the United States (preprint)

The antiviral drug Paxlovid has been shown to rapidly reduce viral load. Coupled with vaccination, timely administration of safe and effective antivirals could provide a path towards managing COVID-19 without restrictive non-pharmaceutical measures. Here, we estimate the population-level impacts of expanding treatment with Paxlovid in the US using a multi-scale mathematical model of SARS-CoV-2 transmission that incorporates the within-host viral load dynamics of the Omicron variant. We find that, under a low transmission scenario (Re~1.2) treating 20% of symptomatic cases with Paxlovid would be life and cost saving, leading to an estimated 0.26 (95% CrI:0.03, 0.59) million hospitalizations averted, 30.61 (95% CrI:1.69, 71.15) thousand deaths averted, and US$52.16 (95% CrI:2.62, 122.63) billion reduction in the US. Rapid and broad use of the antiviral Paxlovid could substantially reduce COVID-19 morbidity and mortality, while averting socioeconomic hardship.

Interpreting discordant SARS-CoV-2 diagnostic test results (preprint)

We introduce a model to interpret discordant SARS-CoV-2 test results and estimate that an individual receiving a positive rapid antigen test followed by a negative Nucleic Acid Amplification Test had only a 12-24% chance of being infected in the United States from March 2020 to May 2022.

Optimizing COVID-19 testing strategies on college campuses: evaluation of the health and economic costs (preprint)

Colleges and universities in the US struggled to provide safe in-person education throughout the COVID-19 pandemic. Testing coupled with isolation is a nimble intervention strategy that can be tailored to mitigate health and economic costs, as the virus and our arsenal of medical countermeasures continue to evolve. We developed a decision-support tool to aid in the design of university-based testing strategies using a mathematical model of SARS-CoV-2 transmission. Applying this framework to a large public university reopening in the fall of 2021 with a 60% student vaccination rate, we find that the optimal strategy, in terms of health and economic costs, is twice weekly antigen testing of all students. This strategy provides a 95% guarantee that, throughout the fall semester, case counts would not exceed the CDCs original high transmission threshold of 100 cases per 100k persons over 7 days. As the virus and our medical armament continue to evolve, testing will remain a flexible tool for managing risks and keeping campuses open. We have implemented this model as an online tool to facilitate the design of testing strategies that adjust for COVID-19 conditions, university-specific parameters, and institutional goals.

Disproportionate impacts of COVID-19 in a large US city (preprint)

COVID-19 has disproportionately impacted individuals depending on where they live and work, and based on their race, ethnicity, and socioeconomic status. Studies have documented catastrophic disparities at critical points throughout the pandemic, but have not yet systematically tracked their severity through time. Using anonymized hospitalization data from March 11, 2020 to June 1, 2021, we estimate the time-varying burden of COVID-19 by age group and ZIP code in Austin, Texas. During this 15-month period, we estimate an overall 16.9% (95% CrI: 16.1-17.8%) infection rate and 34.1% (95% CrI: 32.4-35.8%) case reporting rate. Individuals over 65 were less likely to be infected than younger age groups (8.0% [95% CrI: 7.5-8.6%] vs 18.1% [95% CrI: 17.2-19.2%]), but more likely to be hospitalized (1,381 per 100,000 vs 319 per 100,000) and have their infections reported (51% [95% CrI: 48-55%] vs 33% [95% CrI: 31-35%]). Children under 18, who make up 20.3% of the local population, accounted for only 5.5% (95% CrI: 3.8-7.7%) of all infections between March 1 and May 1, 2020 compared with 20.4% (95% CrI: 17.3-23.9%) between December 1, 2020 and February 1, 2021. We compared ZIP codes ranking in the 75th percentile of vulnerability to those in the 25th percentile, and found that the more vulnerable communities had 2.5 (95% CrI: 2.0-3.0) times the infection rate and only 70% (95% CrI: 61%-82%) the reporting rate compared to the less vulnerable communities. Inequality persisted but declined significantly over the 15-month study period. For example, the ratio in infection rates between the more and less vulnerable communities declined from 12.3 (95% CrI: 8.8-17.1) to 4.0 (95% CrI: 3.0-5.3) to 2.7 (95% CrI: 2.0-3.6), from April to August to December of 2020, respectively. Our results suggest that public health efforts to mitigate COVID-19 disparities were only partially effective and that the CDC's social vulnerability index may serve as a reliable predictor of risk on a local scale when surveillance data are limited.

The risk of SARS-CoV-2 Omicron variant emergence in low and middle-income countries (LMICs) (preprint)

We estimated the probability of undetected emergence of the SARS-CoV-2 Omicron variant in 25 low and middle-income countries (LMICs) prior to December 5, 2021. In nine countries, the risk exceeds 50%; in Turkey, Pakistan and the Philippines, it exceeds 99%. Risks are generally lower in the Americas than Europe or Asia.

International risk of SARS-CoV-2 Omicron variant importations originating in South Africa (preprint)

Omicron, a fast-spreading SARS-CoV-2 variant of concern reported to the World Health Organization on November 24, 2021, has raised international alarm. We estimated there is at least 50% chance that Omicron had been introduced by travelers from South Africa into all of the 30 countries studied by November 27, 2021.

Detecting in-school transmission of SARS-CoV-2 from case ratios and documented clusters (preprint)

Claims that in-person schooling has not amplified SARS-CoV-2 transmission are based on similar infection rates in schools and their surrounding communities and limited numbers of documented in-school transmission events. Simulations assuming high in-school transmission suggest that these metrics cannot exclude the possibility that transmission in schools exacerbated overall pandemic risks.

Survival analysis methods for analysis of hospitalization data: Application to COVID-19 patient hospitalization experience (preprint)

During most of 2020, the COVID-19 pandemic gave rise to considerable and growing numbers of hospitalizations across most of the U.S. Typical COVID-19 hospitalization data, including length of stay, intensive care unit (ICU) use, mechanical ventilation (Vent), and in-hospital mortality provide clearly interpretable health care endpoints that can be compared across population strata. They capture the resources consumed for the care of COVID-19 patients, and analysis of these endpoints can be used for resource planning at the local level. Yet, hospitalization data embody novel features that require careful statistical treatment to be useful in this context. Specifically, statistical models must meet three goals: (i) They should mesh with and inform mathematical epidemiologic or agent-based models of the COVID-19 experience in the population. (ii) They need to handle administrative censoring of hospitalization experience when data are extracted and downloaded for a given patient before that patients hospitalization experience has terminated. And, (iii) models need to handle risks for competing events, the occurrence of one blocking the possibility of the other(s). For example, live discharge from the hospital "competes with" (i.e., blocks) in-hospital mortality. We have adapted approaches from the survival analysis literature to address these challenges in order to better understand and quantify the population experience in hospital with respect to length of stay, ICU, Vent use and so on. Using hospitalization data from a large U.S. metropolitan region, in this report, we show how standard techniques from survival analysis can be brought to bear to address these challenges and yield interpretable results. In the breakout/discussion, we will discuss formulation, estimation and inference, and interpretation of competing risks models.

Early estimates of SARS-CoV-2 B.1.1.7 variant emergence in a university setting (preprint)

Recent identification of the highly transmissible novel SARS-CoV-2 variant in the United Kingdom (B.1.1.7) has raised concerns for renewed pandemic surges worldwide 1,2. B.1.1.7 was first identified in the US on December 29, 2020 and may become dominant by March 2021 3. However, the regional prevalence of B.1.1.7 is largely unknown because of limited molecular surveillance for SARS-CoV-2 4. Quantitative PCR data from a surveillance testing program on a large university campus with roughly 30,000 students provides local situational awareness at a pivotal moment in the COVID-19 pandemic.

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

The Experience of Two Independent Schools with In-Person Learning During the COVID-19 Pandemic (preprint)

BACKGROUND In 2020, U.S schools closed due to SARS-CoV-2 but their role in transmission was unknown. In fall 2020, national guidance for reopening omitted testing or screening recommendations. We report the experience of 2 large independent K-12 schools (School-A and School-B) that implemented an array of SARS-CoV-2 mitigation strategies that included periodic universal testing. METHODS SARS-CoV-2 was identified through periodic universal PCR testing, self-reporting of tests conducted outside school, and contact tracing. Schools implemented behavioral and structural mitigation measures, including mandatory masks, classroom disinfecting, and social distancing. RESULTS Over the fall semester, School-A identified 112 cases in 2320 students and staff; School-B identified 25 cases (2.0%) in 1200 students and staff. Most cases were asymptomatic and none required hospitalization. Of 69 traceable introductions, 63(91%) were not associated with school-based transmission, 59 cases (54%) occurred in the 2 weeks post-Thanksgiving. In 6/7 clusters, clear noncompliance with mitigation protocols was found. The largest outbreak had 28 identified cases and was traced to an off-campus party. There was no transmission from students to staff. CONCLUSIONS Although school-age children can contract and transmit SARS-CoV-2, rates of COVID-19 infection related to in-person education were significantly lower than those in the surrounding community. However, social activities among students outside of school undermined those measures and should be discouraged, perhaps with behavioral contracts, to ensure the safety of school communities. In addition, introduction risks were highest following extended school breaks. These risks may be mitigated with voluntary quarantines and surveillance testing prior to re-opening.

The impacts of COVID-19 vaccine timing, number of doses, and risk prioritization on mortality in the US (preprint)

As COVID-19 vaccination begins worldwide, policymakers face critical trade-offs. Using a mathematical model of COVID-19 transmission, we find that timing of the rollout is expected to have a substantially greater impact on mortality than risk-based prioritization and adherence and that prioritizing first doses over second doses may be life saving.

International risk of the new variant COVID-19 importations originating in the United Kingdom (preprint)

A fast-spreading SARS-CoV-2 variant identified in the United Kingdom in December 2020 has raised international alarm. We estimate that, in all 15 countries analyzed, there is at least a 50% chance the variant was imported by travelers from the United Kingdom by December 7th.

Projecting the combined healthcare burden of seasonal influenza and COVID-19 (preprint)

The overlapping 2020-2021 influenza season and COVID-19 pandemic may overwhelm hospitals throughout the Northern Hemisphere. Using a mathematical model, we project that COVID-19 burden will dwarf that of influenza. If non-pharmacological mitigation efforts fail, increasing influenza vaccination coverage by 30% points would avert 54 hospitalizations per 100,000 people.

The COVID-19 herd immunity threshold is not low: A re-analysis of European data from spring of 2020 (preprint)

The recent publication of the Great Barrington Declaration (GBD), which calls for relaxing all public health interventions on young, healthy individuals, has brought the question of herd immunity to the forefront of COVID-19 policy discussions, and is partially based on unpublished research that suggests low herd immunity thresholds (HITs) of 10-20%. We re-evaluate these findings and correct a flawed assumption leading to COVID-19 HIT estimates of 60-80%. If policymakers were to adopt a herd immunity strategy, in which the virus is allowed to spread relatively unimpeded, we project that cumulative COVID-19 deaths would be five times higher than the initial estimates suggest. Our re-estimates of the COVID-19 HIT corroborate strong signals in the data and compelling arguments that most of the globe remains far from herd immunity, and suggest that abandoning community mitigation efforts would jeopardize the welfare of communities and integrity of healthcare systems.

Design of COVID-19 Staged Alert Systems to Ensure Healthcare Capacity with Minimal Closures (preprint)

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including Frances ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence.

The impact of vaccination on COVID-19 outbreaks in the United States (preprint)

BackgroundGlobal vaccine development efforts have been accelerated in response to the devastating COVID-19 pandemic. We evaluated the impact of a 2-dose COVID-19 vaccination campaign on reducing incidence, hospitalizations, and deaths in the United States (US). MethodsWe developed an agent-based model of SARS-CoV-2 transmission and parameterized it with US demographics and age-specific COVID-19 outcomes. Healthcare workers and high-risk individuals were prioritized for vaccination, while children under 18 years of age were not vaccinated. We considered a vaccine efficacy of 95% against disease following 2 doses administered 21 days apart achieving 40% vaccine coverage of the overall population within 284 days. We varied vaccine efficacy against infection, and specified 10% pre-existing population immunity for the base-case scenario. The model was calibrated to an effective reproduction number of 1.2, accounting for current non-pharmaceutical interventions in the US. ResultsVaccination reduced the overall attack rate to 4.6% (95% CrI: 4.3% - 5.0%) from 9.0% (95% CrI: 8.4% - 9.4%) without vaccination, over 300 days. The highest relative reduction (54-62%) was observed among individuals aged 65 and older. Vaccination markedly reduced adverse outcomes, with non-ICU hospitalizations, ICU hospitalizations, and deaths decreasing by 63.5% (95% CrI: 60.3% - 66.7%), 65.6% (95% CrI: 62.2% - 68.6%), and 69.3% (95% CrI: 65.5% - 73.1%), respectively, across the same period. ConclusionsOur results indicate that vaccination can have a substantial impact on mitigating COVID-19 outbreaks, even with limited protection against infection. However, continued compliance with non-pharmaceutical interventions is essential to achieve this impact. Key pointsVaccination with a 95% efficacy against disease could substantially mitigate future attack rates, hospitalizations, and deaths, even if only adults are vaccinated. Non-pharmaceutical interventions remain an important part of outbreak response as vaccines are distributed over time.

How Timing of Stay-home Orders and Mobility Reductions Impacted First-Wave COVID-19 Deaths in US Counties (preprint)

SARS-CoV-2 transmission continues to evolve in the United States following the large second wave in the Summer. Understanding how location-specific variations in non-pharmaceutical epidemic control policies and behaviors contributed to disease transmission will be key for designing effective strategies to avoid future resurgences. We offer a statistical analysis of the relative effectiveness of the timing of both official stay-at-home orders and population mobility reductions, offering a distinct (but complementary) dimension of evidence gleaned from more traditional mechanistic models of epidemic dynamics. Specifically, we use a Bayesian hierarchical model fit to county-level mortality data from the first wave of the pandemic from Jan 21 2020 through May 10 2020 to establish how timing of stay-at-home orders and population mobility changes impacted county-specific epidemic growth. We find that population mobility reductions generally preceded stay-at-home orders, and among 356 counties with a pronounced early local epidemic between January 21 and May 10 (representing 195 million people and 32,000 observed deaths), a 10 day delay in population mobility reduction would have added 16,149 (95% credible interval [CI] 9,517 24,381) deaths by Apr 20, whereas shifting mobility reductions 10 days earlier would have saved 13,571 (95% CI 8,449 16,930) lives. Analogous estimates attributable to the timing of explicit stay-at-home policies were less pronounced, suggesting that mobility changes were the clearer drivers of epidemic dynamics. Our results also suggest that the timing of mobility reductions and policies most impacted epidemic dynamics in larger, urban counties compared with smaller, rural ones. Overall, our results suggest that community behavioral changes had greater impact on curve flattening during the Spring wave compared with stay at home orders. Thus, community engagement and buy-in with precautionary policies may be more important for predicting transmission risk than explicit policies.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support (preprint)

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.