Return on Investment of the COVID-19 Vaccination Campaign in New York City.

Importance: New York City, an early epicenter of the pandemic, invested heavily in its COVID-19 vaccination campaign to mitigate the burden of disease outbreaks. Understanding the return on investment (ROI) of this campaign would provide insights into vaccination programs to curb future COVID-19 outbreaks. Objective: To estimate the ROI of the New York City COVID-19 vaccination campaign by estimating the tangible direct and indirect costs from a societal perspective. Design, Setting, and Participants: This decision analytical model of disease transmission was calibrated to confirmed and probable cases of COVID-19 in New York City between December 14, 2020, and January 31, 2022. This simulation model was validated with observed patterns of reported hospitalizations and deaths during the same period. Exposures: An agent-based counterfactual scenario without vaccination was simulated using the calibrated model. Main Outcomes and Measures: Costs of health care and deaths were estimated in the actual pandemic trajectory with vaccination and in the counterfactual scenario without vaccination. The savings achieved by vaccination, which were associated with fewer outpatient visits, emergency department visits, emergency medical services, hospitalizations, and intensive care unit admissions, were also estimated. The value of a statistical life (VSL) lost due to COVID-19 death and the productivity loss from illness were accounted for in calculating the ROI. Results: During the study period, the vaccination campaign averted an estimated $27.96 (95% credible interval [CrI], $26.19-$29.84) billion in health care expenditures and 315 724 (95% CrI, 292 143-340 420) potential years of life lost, averting VSL loss of $26.27 (95% CrI, $24.39-$28.21) billion. The estimated net savings attributable to vaccination were $51.77 (95% CrI, $48.50-$55.85) billion. Every $1 invested in vaccination yielded estimated savings of $10.19 (95% CrI, $9.39-$10.87) in direct and indirect costs of health outcomes that would have been incurred without vaccination. Conclusions and Relevance: Results of this modeling study showed an association of the New York City COVID-19 vaccination campaign with reduction in severe outcomes and avoidance of substantial economic losses. This significant ROI supports continued investment in improving vaccine uptake during the ongoing pandemic.

The durability of natural infection and vaccine-induced immunity against future infection by SARS-CoV-2.

The durability of vaccine-mediated immunity to SARS-CoV-2, the durations to breakthrough infection, and the optimal timings of booster vaccination are crucial knowledge for pandemic response. Here, we applied comparative evolutionary analyses to estimate the durability of immunity and the likelihood of breakthrough infections over time following vaccination by BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), ChAdOx1 (Oxford-AstraZeneca), and Ad26.COV2.S (Johnson & Johnson/Janssen). We evaluated anti-Spike (S) immunoglobulin G (IgG) antibody levels elicited by each vaccine relative to natural infection. We estimated typical trajectories of waning and corresponding infection probabilities, providing the distribution of times to breakthrough infection for each vaccine under endemic conditions. Peak antibody levels elicited by messenger RNA (mRNA) vaccines mRNA-1273 and BNT1262b2 exceeded that of natural infection and are expected to typically yield more durable protection against breakthrough infections (median 29.6 mo; 5 to 95% quantiles 10.9 mo to 7.9 y) than natural infection (median 21.5 mo; 5 to 95% quantiles 3.5 mo to 7.1 y). Relative to mRNA-1273 and BNT1262b2, viral vector vaccines ChAdOx1 and Ad26.COV2.S exhibit similar peak anti-S IgG antibody responses to that from natural infection and are projected to yield lower, shorter-term protection against breakthrough infection (median 22.4 mo and 5 to 95% quantiles 4.3 mo to 7.2 y; and median 20.5 mo and 5 to 95% quantiles 2.6 mo to 7.0 y; respectively). These results leverage the tools from evolutionary biology to provide a quantitative basis for otherwise unknown parameters that are fundamental to public health policy decision-making.

Universal healthcare as pandemic preparedness: The lives and costs that could have been saved during the COVID-19 pandemic.

The fragmented and inefficient healthcare system in the United States leads to many preventable deaths and unnecessary costs every year. During a pandemic, the lives saved and economic benefits of a single-payer universal healthcare system relative to the status quo would be even greater. For Americans who are uninsured and underinsured, financial barriers to COVID-19 care delayed diagnosis and exacerbated transmission. Concurrently, deaths beyond COVID-19 accrued from the background rate of uninsurance. Universal healthcare would alleviate the mortality caused by the confluence of these factors. To evaluate the repercussions of incomplete insurance coverage in 2020, we calculated the elevated mortality attributable to the loss of employer-sponsored insurance and to background rates of uninsurance, summing with the increased COVID-19 mortality due to low insurance coverage. Incorporating the demography of the uninsured with age-specific COVID-19 and nonpandemic mortality, we estimated that a single-payer universal healthcare system would have saved about 212,000 lives in 2020 alone. We also calculated that US$105.6 billion of medical expenses associated with COVID-19 hospitalization could have been averted by a single-payer universal healthcare system over the course of the pandemic. These economic benefits are in addition to US$438 billion expected to be saved by single-payer universal healthcare during a nonpandemic year.

COVID-19 hospitalizations and deaths averted under an accelerated vaccination program in northeastern and southern regions of the USA.

BACKGROUND: The fourth wave of COVID-19 pandemic peaked in the US at 160,000 daily cases, concentrated primarily in southern states. As the Delta variant has continued to spread, we evaluated the impact of accelerated vaccination on reducing hospitalization and deaths across northeastern and southern regions of the US census divisions. METHODS: We used an age-stratified agent-based model of COVID-19 to simulate outbreaks in all states within two U.S. regions. The model was calibrated using reported incidence in each state from October 1, 2020 to August 31, 2021, and parameterized with characteristics of the circulating SARS-CoV-2 variants and state-specific daily vaccination rate. We then projected the number of infections, hospitalizations, and deaths that would be averted between September 2021 and the end of March 2022 if the states increased their daily vaccination rate by 20 or 50% compared to maintaining the status quo pace observed during August 2021. FINDINGS: A 50% increase in daily vaccine doses administered to previously unvaccinated individuals is projected to prevent a total of 30,727 hospitalizations and 11,937 deaths in the two regions between September 2021 and the end of March 2022. Southern states were projected to have a higher weighted average number of hospitalizations averted (18.8) and lives saved (8.3) per 100,000 population, compared to the weighted average of hospitalizations (12.4) and deaths (2.7) averted in northeastern states. On a per capita basis, a 50% increase in daily vaccinations is expected to avert the most hospitalizations in Kentucky (56.7 hospitalizations per 100,000 averted with 95% CrI: 45.56 - 69.9) and prevent the most deaths in Mississippi, (22.1 deaths per 100,000 population prevented with 95% CrI: 18.0 - 26.9). INTERPRETATION: Accelerating progress to population-level immunity by raising the daily pace of vaccination would prevent substantial hospitalizations and deaths in the US, even in those states that have passed a Delta-driven peak in infections. FUNDING: This study was supported by The Commonwealth Fund. SMM acknowledges the support from the Canadian Institutes of Health Research [OV4 - 170643, COVID-19 Rapid Research] and the Natural Sciences and Engineering Research Council of Canada, Emerging Infectious Disease Modelling, MfPH grant. MCF acknowledges support from the National Institutes of Health (5 K01 AI141576).

Lives saved and hospitalizations averted by COVID-19 vaccination in New York City: a modeling study.

BACKGROUND: Following the start of COVID-19 vaccination in New York City (NYC), cases have declined over 10-fold from the outbreak peak in January 2020, despite the emergence of highly transmissible variants. We evaluated the impact of NYC's vaccination campaign on saving lives as well as averting hospitalizations and cases. METHODS: We used an age-stratified agent-based model of COVID-19 to include transmission dynamics of Alpha, Gamma, Delta and Iota variants as identified in NYC. The model was calibrated and fitted to reported incidence in NYC, accounting for the relative transmissibility of each variant and vaccination rollout data. We simulated COVID-19 outbreak in NYC under the counterfactual scenario of no vaccination and compared the resulting disease burden with the number of cases, hospitalizations and deaths reported under the actual pace of vaccination. FINDINGS: We found that without vaccination, there would have been a spring-wave of COVID-19 in NYC due to the spread of Alpha and Delta variants. The COVID-19 vaccination campaign in NYC prevented such a wave, and averted 290,467 (95% CrI: 232,551 - 342,664) cases, 48,076 (95% CrI: 42,264 - 53,301) hospitalizations, and 8,508 (95% CrI: 7,374 - 9,543) deaths from December 14, 2020 to July 15, 2021. INTERPRETATION: Our study demonstrates that the vaccination program in NYC was instrumental to substantially reducing the COVID-19 burden and suppressing a surge of cases attributable to more transmissible variants. As the Delta variant sweeps predominantly among unvaccinated individuals, our findings underscore the urgent need to accelerate vaccine uptake and close the vaccination coverage gaps. FUNDING: This study was supported by The Commonwealth Fund.

Population Immunity Against COVID-19 in the United States.

BACKGROUND: As of 28 July 2021, 60% of adults in the United States had been fully vaccinated against COVID-19, and more than 34 million cases had been reported. Given the uncertainty regarding undocumented infections, the population level of immunity against COVID-19 in the United States remains undetermined. OBJECTIVE: To estimate the population immunity, defined as the proportion of the population that is protected against SARS-CoV-2 infection due to prior infection or vaccination. DESIGN: Statistical and simulation modeling to estimate overall and age-specific population immunity. SETTING: United States. PARTICIPANTS: Simulated age-stratified population representing U.S. demographic characteristics. MEASUREMENTS: The true number of SARS-CoV-2 infections in the United States was inferred from data on reported deaths using age-specific infection-fatality rates (IFRs). Taking into account the estimates for vaccine effectiveness and protection against reinfection, the overall population immunity was determined as the sum of protection levels in vaccinated persons and those who were previously infected but not vaccinated. RESULTS: Using age-specific IFR estimates from the Centers for Disease Control and Prevention, it was estimated that as of 15 July 2021, 114.9 (95% credible interval [CrI], 103.2 to 127.4) million persons had been infected with SARS-CoV-2 in the United States. The mean overall population immunity was 62.0% (CrI, 58.4% to 66.4%). Adults aged 65 years or older were estimated to have the highest immunity level (77.2% [CrI, 76.2% to 78.6%]), and children younger than 12 years had the lowest immunity level (17.9% [CrI, 14.4% to 21.9%]). LIMITATION: Publicly reported deaths may underrepresent actual deaths. CONCLUSION: As of 15 July 2021, the U.S. population immunity against COVID-19 may still have been insufficient to contain the outbreaks and safely revert to prepandemic social behavior. PRIMARY FUNDING SOURCE: National Science Foundation, National Institutes of Health, Notsew Orm Sands Foundation, Canadian Institutes of Health Research, and Natural Sciences and Engineering Research Council of Canada.

Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis.

Quantification of asymptomatic infections is fundamental for effective public health responses to the COVID-19 pandemic. Discrepancies regarding the extent of asymptomaticity have arisen from inconsistent terminology as well as conflation of index and secondary cases which biases toward lower asymptomaticity. We searched PubMed, Embase, Web of Science, and World Health Organization Global Research Database on COVID-19 between January 1, 2020 and April 2, 2021 to identify studies that reported silent infections at the time of testing, whether presymptomatic or asymptomatic. Index cases were removed to minimize representational bias that would result in overestimation of symptomaticity. By analyzing over 350 studies, we estimate that the percentage of infections that never developed clinical symptoms, and thus were truly asymptomatic, was 35.1% (95% CI: 30.7 to 39.9%). At the time of testing, 42.8% (95% prediction interval: 5.2 to 91.1%) of cases exhibited no symptoms, a group comprising both asymptomatic and presymptomatic infections. Asymptomaticity was significantly lower among the elderly, at 19.7% (95% CI: 12.7 to 29.4%) compared with children at 46.7% (95% CI: 32.0 to 62.0%). We also found that cases with comorbidities had significantly lower asymptomaticity compared to cases with no underlying medical conditions. Without proactive policies to detect asymptomatic infections, such as rapid contact tracing, prolonged efforts for pandemic control may be needed even in the presence of vaccination.

Accelerated vaccine rollout is imperative to mitigate highly transmissible COVID-19 variants.

BACKGROUND: More contagious variants of SARS-CoV-2 have emerged around the world, sparking concerns about impending surge in cases and severe outcomes. Despite the development of effective vaccines, rollout has been slow. We evaluated the impact of accelerated vaccine distribution on curbing the disease burden of novel SARS-CoV-2 variants. METHODS: We used an agent-based model of SARS-CoV-2 transmission and vaccination to simulate the spread of novel variants with S-Gene Target Failure (SGTF) in addition to the original strain. We incorporated age-specific risk and contact patterns and implemented a two-dose vaccination campaign in accord with CDC-recommended prioritization. As a base case, we projected hospitalizations and deaths at a daily vaccination rate of 1 million doses in the United States (US) and compared with accelerated campaigns in which daily doses were expanded to 1.5, 2, 2.5, or 3 million. FINDINGS: We found that at a vaccination rate of 1 million doses per day, an emergent SGTF variant that is 20-70% more transmissible than the original variant would become dominant within 2 to 9 weeks, accounting for as much as 99% of cases at the outbreak peak. Our results show that accelerating vaccine delivery would substantially reduce severe health outcomes. For a SGTF with 30% higher transmissibility, increasing vaccine doses from 1 to 3 million per day would avert 152,048 (95% CrI: 134,772-168,696) hospitalizations and 48,448 (95% CrI: 42,042-54,285) deaths over 300 days. Accelerated vaccination would also prevent additional COVID-19 waves that would otherwise be fuelled by waning adherence to non-pharmaceutical interventions (NPIs). INTERPRETATION: We found that the current pace of vaccine rollout is insufficient to prevent the exacerbation of the pandemic that will be attributable to the novel, more contagious SARS-CoV-2 variants. Accelerating the vaccination rate should be a public health priority for averting the expected surge in COVID-19 hospitalizations and deaths that would be associated with widespread dissemination of the SGTF variants. Our results underscore the need to bolster the production and distribution of COVID-19 vaccines, to rapidly expand vaccination priority groups and distribution sites.

Evaluation of COVID-19 vaccination strategies with a delayed second dose.

Two of the Coronavirus Disease 2019 (COVID-19) vaccines currently approved in the United States require 2 doses, administered 3 to 4 weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose or to continue with the recommended 2-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these 2 vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of preexisting immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% credible interval [CrI]: 7.8-29.7) infections, 0.69 (95% CrI: 0.52-0.97) hospitalizations, and 0.34 (95% CrI: 0.25-0.44) deaths per 10,000 population compared to the recommended 4-week interval between the 2 doses. Pfizer-BioNTech vaccines also averted an additional 0.60 (95% CrI: 0.37-0.89) hospitalizations and 0.32 (95% CrI: 0.23-0.45) deaths per 10,000 population in a 9-week delayed second dose (DSD) strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the 2 doses.

Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada.

The novel coronavirus disease 2019 (COVID-19) has caused severe outbreaks in Canadian long-term care facilities (LTCFs). In Canada, over 80% of COVID-19 deaths during the first pandemic wave occurred in LTCFs. We sought to evaluate the effect of mitigation measures in LTCFs including frequent testing of staff, and vaccination of staff and residents. We developed an agent-based transmission model and parameterized it with disease-specific estimates, temporal sensitivity of nasopharyngeal and saliva testing, results of vaccine efficacy trials, and data from initial COVID-19 outbreaks in LTCFs in Ontario, Canada. Characteristics of staff and residents, including contact patterns, were integrated into the model with age-dependent risk of hospitalization and death. Estimates of infection and outcomes were obtained and 95% credible intervals were generated using a bias-corrected and accelerated bootstrap method. Weekly routine testing of staff with 2-day turnaround time reduced infections among residents by at least 25.9% (95% CrI: 23.3%-28.3%), compared to baseline measures of mask-wearing, symptom screening, and staff cohorting alone. A similar reduction of hospitalizations and deaths was achieved in residents. Vaccination averted 2-4 times more infections in both staff and residents as compared to routine testing, and markedly reduced hospitalizations and deaths among residents by 95.9% (95% CrI: 95.4%-96.3%) and 95.8% (95% CrI: 95.5%-96.1%), respectively, over 200 days from the start of vaccination. Vaccination could have a substantial impact on mitigating disease burden among residents, but may not eliminate the need for other measures before population-level control of COVID-19 is achieved.

The implications of silent transmission for the control of COVID-19 outbreaks.

Since the emergence of coronavirus disease 2019 (COVID-19), unprecedented movement restrictions and social distancing measures have been implemented worldwide. The socioeconomic repercussions have fueled calls to lift these measures. In the absence of population-wide restrictions, isolation of infected individuals is key to curtailing transmission. However, the effectiveness of symptom-based isolation in preventing a resurgence depends on the extent of presymptomatic and asymptomatic transmission. We evaluate the contribution of presymptomatic and asymptomatic transmission based on recent individual-level data regarding infectiousness prior to symptom onset and the asymptomatic proportion among all infections. We found that the majority of incidences may be attributable to silent transmission from a combination of the presymptomatic stage and asymptomatic infections. Consequently, even if all symptomatic cases are isolated, a vast outbreak may nonetheless unfold. We further quantified the effect of isolating silent infections in addition to symptomatic cases, finding that over one-third of silent infections must be isolated to suppress a future outbreak below 1% of the population. Our results indicate that symptom-based isolation must be supplemented by rapid contact tracing and testing that identifies asymptomatic and presymptomatic cases, in order to safely lift current restrictions and minimize the risk of resurgence.

Projecting hospital utilization during the COVID-19 outbreaks in the United States.

In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R0 Without self-isolation, when R0 = 2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4-50.3%), although still exceeding existing capacity. When R0 = 2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4-75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.