The impact of COVID-19 vaccination in the US: Averted burden of SARS-COV-2-related cases, hospitalizations and deaths

Teresa K Yamana; Marta Galanti; Sen Pei; Manuela Di Fusco; Frederick J Angulo; Mary M Moran; Farid Khan; David L Swerdlow; Jeffrey Shaman

doi:10.1371/journal.pone.0275699

. 2023 Apr 25;18(4):e0275699. doi: 10.1371/journal.pone.0275699

The impact of COVID-19 vaccination in the US: Averted burden of SARS-COV-2-related cases, hospitalizations and deaths

Teresa K Yamana ^1,^*, Marta Galanti ¹, Sen Pei ¹, Manuela Di Fusco ², Frederick J Angulo ³, Mary M Moran ³, Farid Khan ³, David L Swerdlow ³, Jeffrey Shaman ^1,^4,^*

Editor: Sung-mok Jung⁵

PMCID: PMC10129007 PMID: 37098043

Abstract

By August 1, 2022, the SARS-CoV-2 virus had caused over 90 million cases of COVID-19 and one million deaths in the United States. Since December 2020, SARS-CoV-2 vaccines have been a key component of US pandemic response; however, the impacts of vaccination are not easily quantified. Here, we use a dynamic county-scale metapopulation model to estimate the number of cases, hospitalizations, and deaths averted due to vaccination during the first six months of vaccine availability. We estimate that COVID-19 vaccination was associated with over 8 million fewer confirmed cases, over 120 thousand fewer deaths, and 700 thousand fewer hospitalizations during the first six months of the campaign.

Introduction

By August 1, 2022, SARS-CoV-2, the virus responsible for the COVID-19 pandemic, had caused over 90 million cases and 1 million deaths in the United States [1]. While these numbers are likely affected by the widespread availability of SARS-CoV-2 vaccines, the precise impact of vaccination on the burden of COVID-19 disease is uncertain. Here we use a dynamic model, coupled with historical data, statistical inference methods, and hospitalization costs, to quantify the clinical and economic burdens of infections, hospitalizations, and deaths averted due to vaccination in the US, both cumulatively and in individual states, during the first approximately six months of vaccine availability when the wild type and alpha variants of SARS-CoV-2 were the predominant drivers of infection.

In mid-December 2020, the first SARS-CoV-2 vaccine received emergency use authorization in the US and was initially recommended for healthcare workers and long-term care facility residents, followed by adults aged 65 years and older, adults aged 16–64 with high-risk medical conditions and essential workers [2]. By early April 2021, the vaccine recommendation was extended to the general population aged 16 years and older. Subsequent steps have seen recommended vaccine use for 12–15 year-olds (May 2021) and 5–11 year-olds (November 2021). Three different vaccines (two mRNA vaccines and one antiviral vector vaccine) with varying efficacy and estimates of duration of protection have been authorized for use in the US. However, vaccination delivery has been variable: it was initially limited by vaccine availability, with roughly 15 million doses provided in the first month, but reached a peak of roughly 90 million doses administered during April, 2021 [1].

By early November 2021, 78% of the US population aged 12 years and older had received at least one dose of a SARS-CoV-2 vaccine, with heterogeneous distribution across age groups (97% of adults aged 65+ years vs 60% of persons aged 12–18 years) and across states (<65% in AL, ID, IN, LA, MS, ND, TN, WY, WV, compared with >90% in CT, MA, HI, VT, PA [2]). During the time period of vaccine rollout, variable levels of non-pharmaceutical interventions (NPIs), such as social distancing, closures of restaurants and bars, mask mandates and travel restrictions, were implemented across states with different start and end dates.

Here, we use a dynamic county-scale metapopulation model, previously used COVID-19 inference and projections [3–5], to conduct counterfactual simulations representing the effects of vaccination. These simulations are used to estimate the number of cases, hospitalizations, and deaths averted due to vaccination during the first six months of vaccine roll out.

Methods

We used a metapopulation model with a Susceptible-Exposed-Infected-Recovered (SEIR) structure run at the county level, coupled with a data assimilation method (EAKF, the ensemble adjustment Kalman filter). We have previously used this framework for inference, forecasting and projections of influenza and SARS-CoV-2 infections at various locations and spatial scales, in both research and operational contexts [3–7]. Here, we simulated SARS-CoV-2 transmission within and among the 3142 counties of the United States.

We first used the model-inference system to fit reported case counts in each county of the US [3] from the time of identification of the first COVID-19 cases in the United States in February 2020, through December 14, 2020, the date of first authorized SARS-CoV-2 vaccination in the US. The inferred values of parameters and state variables on December 14, 2020 served as initial conditions for the averted burden analysis. The model estimates of susceptibility have previously been validated with serological data [4]. Specifically, estimates of cumulative infections during 2020, generated by this model when coupled with inference approaches, were compared and validated with estimates of seroprevalence derived from serological surveys collected on multiple dates and for multiple locations in the US. These external serological data provided strong validation of the model estimates of cumulative infections and thus population susceptibility.

We then added a representation of vaccination to the dynamical model structure using documented daily rates of vaccine administration [1, 8] (see S1 Text). State-level daily vaccination data from the CDC COVID Data Tracker [1] were allocated proportionally to each county based on population size. Within each county, we assumed equal probability of vaccination regardless of prior infection status. We modeled the vaccine as providing 90% effectiveness against infection [9–11]–i.e. 90% of vaccinated individuals with no prior immunity were fully protected from both infection and transmission, while the remaining 10% receive no protection from either infection or transmission. Specifically, 90% of vaccinated individuals with no prior immunity were removed from the Susceptible pool and placed in the Recovered compartment 24 days after administration of the first dose (S1 Text). Since effectively vaccinated individuals do not transmit the disease, we account for both direct and indirect effects of vaccination. In the Recovered compartment, we did not distinguish between vaccinated individuals and individuals recovered from infection; given uncertainty and limited data on re-infections and waning, both were considered immune for the remainder of the simulation period. With the 24-day delay, the impact of vaccinations on the simulation begins on January 8th. This baseline scenario, retrospectively fitted to case counts, enabled estimation of the daily timeseries of epidemiological parameters, including R_t, the time-varying reproductive number, for each county location from December 14, 2020 through June 3, 2021.

We ran the simulations through June 3, 2021 to focus on the impact of vaccination prior to the predominance of the Delta and Omicron variants [1]. Given that the higher transmissibility and immune escape properties of the Delta and Omicron variants require substantial additional modifications of the dynamical model structure, as well as re-parametrization, we restricted our analysis to the December 14, 2020 through June 3, 2021, or the pre-Delta, time period, during which the vaccine provided strong protection against infection. To quantify the burden averted by vaccination, we compared the baseline vaccination scenario to 3 counterfactual no-vaccination scenarios simulated over the same time period. All counterfactual scenarios assumed no vaccinations (or, equivalently, 0% vaccine effectiveness) but varied transmissibility to mimic different levels of non-pharmaceutical intervention (NPI) response in the absence of vaccination:

Counterfactual Scenario 1; A no-transmission-change, no-vaccination scenario in which the R_t daily time series for each location was as inferred for the baseline scenario;
Counterfactual Scenario 2: A no-vaccination scenario in which R_t for each location-day was increased 10% with respect to the baseline scenario; and
Counterfactual Scenario 3: A no-vaccination scenario in which R_t for each location-day was decreased 10% with respect to the baseline scenario.

These counterfactual scenarios represent potential population behaviors and policies that might have been effected in the absence of vaccination. Scenario 3 represents increased NPIs through policies and individual action; Scenario 2 represents a decrease of NPIs, perhaps due to pandemic fatigue. We compared cumulative SARS-COV-2 cases in the 3 no-vaccination scenarios to the baseline scenario at national and state levels, analyzed differences in averted cases among states, and identified factors correlating with vaccination success.

Hospitalizations and deaths

To calculate hospitalizations and deaths in the counterfactual scenarios, we made the assumption that excess cases would have continued to lead to hospitalizations and deaths at the same overall rate as they did in each state during the summer and fall of 2020, prior to vaccine availability. We applied a state-specific pre-vaccine Case Hospitalization Rate (CHR) and a Case Fatality Rate (CFR) multiplier to the total number of averted cases in each scenario. These rates were computed by dividing the number of reported hospitalizations and deaths divided by the corresponding number of reported cases. The denominator for both the CHR and CFR in each state is the number of cases reported from August 1 –December 14, 2020 from the Johns Hopkins Center for Systems Science and Engineering (JHU CSSE) COVID-19 data set [12]. We assume a 4- and 9-day lag for hospitalizations and deaths, respectively, reflecting the delay from case reporting to hospitalization and death [13]. Note that the date of case reporting includes an additional lag from the time an individual first becomes infectious. Hospitalization data were compiled from the HHS dataset [14] and cases. August 2020 was the first full month with all states reporting daily COVID-19 hospitalizations. Death data are from the JHU CSSE COVID-19 Data [12]. We excluded deaths and cases prior to August 1, 2020, for consistency with the hospitalization data set, and because both the ascertainment rate (fraction of true infections that are reported as confirmed cases) and the infection fatality rate (fraction of true infections that resulted in death) were unstable during the initial wave of the pandemic [4].

Hospitalization costs

We calculated averted hospitalization costs by multiplying the distribution of estimated COVID-19 associated hospitalizations averted by the distribution of costs per hospitalization episode, obtained from the US-based Premier Healthcare COVID-19 claims database [15]. The median (interquartile range Q1-Q3) cost per hospitalization episode was $12,046 ($6,309-$25,361).

Results

Initialization

At the start of the simulation period, December 14, 2020, it was estimated 74.1% (95% credible interval: 70.2–78.6) of the US population was susceptible, 0.8% (95% CrI 0.6–1.2%) exposed, 0.8% (95% CrI 0.6–1.0%) infectious and 24.3% (95% CrI 19.2–28.6%) recovered. Fig 1 shows the distribution of the estimated epidemiological parameters across states at the beginning of vaccine administration. The median estimated susceptible fraction, corresponding to the fraction of the population that had not yet been infected since the beginning of the pandemic varied by state and ranged from 58% (95% CrI 56%-61%) in North Dakota to 94% (93%-95%) in Vermont. The susceptible fraction was highest in northwestern and northeastern states. The time dependent reproductive number ranged from median 0.8 (0.7–1.4 95% CrI) in Minnesota to 2.0 (1.7–2.3 95% CrI) in Tennessee. The CFR prior to the start of vaccination varied from 0.5% in Alaska (95% CrI 0.3–0.7%) to 2.3% (95% CrI 2.1–2.6%) in Rhode Island. The CHR in the same period ranged from 3.8% (95% CrI 3.6–4.0%) in Alaska to 20.7% in Kentucky (95% CrI 20.5–20.9%). While there was a modest reduction in CHR at the national scale from the pre-vaccine period (8.8% August 1, 2020 –December 14, 2020) to the analysis period (7.8% December 15, 2020 –June 2, 2021), we did not observe consistent population level differences in CFR at the national level (1.5% during both time periods) nor to CHR and CFR at the state level.

Fig 1 — Upper Left: Population susceptibility, S (proportion of the population not yet infected), at the start of vaccine administration; Upper Right: Time-varying reproductive number, R_t, at the start of vaccine administration; Lower Left: State-specific case hospitalization rate, CHR; Lower Right: State-specific case fatality rate, CFR. Color scales show the median values. Base maps show state boundaries from US Census Bureau [16].

Model results

Between December 14 and June 3, 2021, the baseline model estimated 16.1 million (95% CrI 15.1–18.3 million) total cases, 1.4 million (95% CrI 1.3–1.6 million) hospitalizations, and 246.7 thousand (95% CrI 230.4–279.6 thousand) deaths across the United States. These estimates were consistent with the 16.7 million cases, 1.3 million hospitalizations, and 250 thousand deaths reported in the JHU CSSE and HHS hospitalization datasets [12, 14]. S1 Fig shows fitted baseline cases compared to observations.

The time series of R_t resulting from fitting the baseline scenario from December 14, 2020 through June 4, 2021 is shown at the state and national level in Fig 2. Note that R_t in this analysis refers to the time-varying basic reproductive number, not to be confused with the effective reproductive number R_eff(t), which is R_t multiplied with the fractional susceptible population.

Fig 2 — Top: Covid-19 cases per 100,000 population per day (7-day moving average); Bottom: Median estimate of Rt in baseline scenario. Each blue line represents a single state. The black line is the overall national value. State and national estimates are derived by taking a population-weighted average of county-level estimates of Rt.

By June 4, 2021, 51% of the population in the US had received at least one dose of vaccine [1]. Vaccine coverage differed widely by location, ranging from 35% of the population in Mississippi up to 74% in Vermont. The weekly number of vaccinations administered increased over time: initially at less than 5 million vaccinated per week but reaching a peak of 14 million vaccinated per week in April when vaccination was extended to the general population aged 16 and older (S2 Fig).

Table 1 reports the cumulative averted COVID-19 cases, deaths, hospitalizations and hospitalization cost savings for the 3 scenarios, while Fig 3 shows the modeled COVID-19 case trajectories under the three counterfactual scenarios.

Table 1. Total cumulative COVID-19 cases, deaths, hospitalizations averted and hospitalization cost savings.

	Scenario 1	Scenario 2	Scenario 3
	No change in transmission	10% higher transmission	10% lower transmission
Cases averted	8.1 million	17.0 million	-1.6 million
median, (95% CrI)	(-4.8, 26.3 million)	(1.5, 32.0 million)	(-8.8, 18.2 million)
Deaths averted	123.2 thousand	260.1 thousand	-25.1 thousand
median, (95% CrI)	(-74.3, 403.0 thousand)	(23.0, 489.7 thousand)	(-134.8, 278.8 thousand)
Hospitalizations averted	0.7 million	1.5 million	-0.1 million
median, (95% CrI)	(-0.4, 2.3 million)	(0.1, 2.8 million)	(-0.8, 1.6 million)
Hospitalization cost savings	$7.0 billion	$17.3 billion	-$0.9 billion
median, (95% CrI)	($-11.9, 112.0 billion)	($0.9, 170.3 billion)	(-$44.7, 70.1 billion)

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Fig 3 — Modeled total COVID-19 Cases in Counterfactual Scenarios 1 (top panel), 2 (middle panel) and 3 (bottom panel) in the United States. The black line presents observed cases, the blue line indicates the median counterfactual projection, and the blue shaded area shows the 95% credible interval.

In the scenario with no change in transmission, we estimated that vaccination averted 8.1 million cases at the national level (median value, 95% CrI: [-4.8, 26.3] millions cases), 123.2 thousand deaths (median value, 95% CrI.: [-74.3, 403.0] thousand deaths) and 0.7 million hospitalizations (median value, 95% CrI: [-0.4, 2.3] millions hospitalizations). The median cost savings associated with averted hospitalization was $7.0 billion (median value, 95% CrI:[-$11.9, $112.0]) (see Table 1).

Increasing Rt by 10% with no vaccination in Counterfactual Scenario 2 roughly doubled the median cases averted nationally whereas decreasing Rt by 10% with no vaccination in Counterfactual Scenario 3 considerably reduced the averted burden during the approximately 6 months of analysis (Fig 3 and Table 1). In effect, the decreased Rt, representing increased NPIs, initially offsets the effects of no vaccination during the first 3 months when a more limited percentage of the population is effectively vaccinated. However, this effect decreases in mid-March as vaccination rates climb in the baseline scenario, and by May more cases are produced per day in Counterfactual Scenario 3 due to the absence of vaccination.

In all three counterfactual scenarios, the majority of averted cases occurred between April and June 2021 (Fig 3).

State-level results are presented in S1 Table and Fig 4. For individual states, the median estimates of cases averted under Scenario 1 ranged from roughly 1000 cases per hundred thousand population in South Dakota to over 6000 cases per hundred thousand population in Maine and Arizona. Median cumulative averted hospitalizations varied from 74 per hundred thousand in South Dakota to 752 per hundred thousand in Kentucky. Median cumulative averted deaths varied from 16 per hundred thousand in South Dakota to over 120 per hundred thousand in Arizona and Rhode Island. Higher averted case burden correlated with higher vaccination rate (R² = 0.16) and higher population susceptibility at the beginning of the vaccination campaign (R² = 0.21). In most states, two to three times as many cases were averted under Scenario 2 compared to Scenario 1, indicating that the impact of vaccination would have been even greater if Covid restrictions were relaxed. Roughly three quarters of the states had fewer cases in Scenario 3 than in baseline, indicating that increasing non-pharmaceutical interventions would have been effective in averting cases during this time period.

Discussion

Evaluating the population-level impact of COVID-19 vaccination through mathematical modeling can provide useful insights to policy makers. Here, we leveraged a validated dynamical modeling approach, previously used for research and operationally to simulate county-level COVID-19 transmission, to quantify the additional burden of disease in alternate scenarios without vaccination. Our analyses show that under unchanged NPI levels, COVID-19 vaccination in the US cumulatively prevented 8.3 million cases, 681 thousand hospitalizations and 118 thousand deaths in the first 6 months of implementation. States with high vaccination coverage such as Maine averted as many as 6,000 cases per 100,000 individuals.

These simulations are in general agreement with findings from three other modeling studies set in the US that have found substantial direct and indirect impacts of vaccination in terms of averted burden of disease. Shoukat et al. found that vaccination was fundamental for reducing the spring/summer wave in NYC by reducing cases by one third and hospitalization and deaths by half during December 2020 through July 2021 [17]. Vilches et al. showed that vaccination may have averted more than 14 million cases, 241 thousand deaths and 1.1 million hospitalizations in the US by late June 2021 [18]. Moghadas et al. found an even stronger effect of vaccination with 26 million cases, 1.2 million hospitalizations and 279,000 deaths averted through the end of June 2021 [19]. These three studies agree with our findings that indicate the US would have experienced a substantial wave of infections beginning in March/April 2021 in the absence of a vaccine [17–19]. Haas et al. [20] used a different methodology and found large direct effects of vaccination in Israel. This analysis compared the rates of SARS-CoV-2 infection-related outcomes between vaccinated and unvaccinated populations and estimated that two thirds of hospitalizations and deaths were averted with vaccination in the first four months of vaccine implementation [20].

Our study augments prior research in this field by providing further geographical granularity.

The state-level analyses provide a dynamic picture revealing trends and differences in the public health response to the COVID-19 pandemic, which may be informative for state and local policymakers. Additionally, our study sought to quantify the cost savings associated with vaccine-preventable severe disease (i.e. hospitalizations). The COVID-19 pandemic has challenged the capacity of hospitals and strained hospital and health system finances [21]. In Scenario 3, we estimated that, in the first six months of rollout of the COVID-19 Vaccination program, the hospitalization cost savings were $17.3 billion. More conservatively, Scenario 1 estimated cost savings of $7.0 billion. While these numbers are not adjusted for geographic differences, they provide a ballpark estimate that is in line with a previously published analysis, which estimated $13.8 billion of costs from preventable COVID-19 hospitalizations from June through November 2021 [22]. Our analyses show that the benefits of vaccination due to reduced hospitalizations translated into cost-savings in the billions of dollars. The broad availability of COVID-19 vaccines brought wide-ranging benefits from both a public health and economic perspective. Vaccination may also lessen other societal impacts associated with the pandemic (e.g. work productivity loss). The total economic impact may therefore be even greater than reported, and further studies elucidating those impacts are warranted.

Our study also adds to the existing literature by considering 3 counterfactual scenarios, all without vaccinations, but with varying R_t, that mimic different possible population responses to disease spread in the absence of a vaccine. The first counterfactual scenario is designed to quantify what the SARS-COV-2 related burden would have been without vaccinations if the population had maintained the same NPI measures as occurred with vaccination. The other two counterfactual scenarios are designed to explore the uncertainties of these estimates, as it is difficult to anticipate the public policy and population behavior response in the absence of a vaccine. Specifically, Counterfactual Scenario 2 represents a stronger relaxation of NPIs, possibly due to pandemic fatigue in the absence of an available vaccine, while Counterfactual Scenario 3 represents a reinforcement of NPIs during the 6 months of projections, assuming that the population would have responded with increased measures to control transmission. Counterfactual 3 shows that in the early months of the vaccine rollout, an increase in NPIs could have produced an even greater reduction of disease compared to vaccination as it occurred. However, while increased NPIs may have slowed transmission in the short term (the first months of vaccine rollout), those measures would not have been as effective as vaccination once the Alpha variant became established in the United States (Fig 3). The benefits of vaccination are seen in the difference between Counterfactual Scenario 3 and the baseline curve during the last month of simulation.

All 3 scenarios show that vaccination benefits were limited during the early months of vaccine rollout, and that most of the averted burden was realized in the last 2 months of the analyzed period. The winter peak of COVID-19 cases was reached in the US during mid-January 2021 just when the first vaccinations started to become effective. Vaccine availability constraints during the first months of the campaign restricted administration to portions of the population with increased risk of exposure and severe disease. It was not until April 2021 that vaccination was recommended for the general population aged ≥16 years. The combination of an initially slower rate of vaccination and a decreasing trend in transmission, with some states having a significant proportion of the population no longer susceptible to infection, narrowed the overall averted burden in the first months of 2021. Some exceptions occurred in states with larger initial susceptible fractions (e.g. Vermont); for these states the averted burden per hundred thousand was already significant in the early months of the vaccination campaign.

By March 2021, the Alpha variant, a SARS-CoV-2 strain with increased transmissibility relative to the wild type, became the predominant circulating serotype [1]. This variant, combined with progressive relaxation of NPIs in most states, likely produced the increase of R_t inferred at this time. Simultaneously, the impact of vaccination, seen in the divergence between the baseline scenario and the no-vaccination scenario case curves, becomes much more evident at the national level (Fig 3).

A limitation of this analysis is that it relies on assumptions about whether and how the parameters inferred from the true observed course of the pandemic would have changed in the absence of a vaccine. Our primary counterfactual, Scenario 1, assumed that the parameters–including the disease transmission rates and the case ascertainment rate–would have been the same with or without a vaccine. We explored some of the sensitivity to this assumption by altering the time-varying reproductive number in Counterfactual Scenarios 2 and 3. However, these are very simplified representations, and one could just as well imagine dramatically different counterfactual scenarios.

We limit our analysis to a relatively short projection time: the first six months of the vaccination campaign. Our estimates are therefore not generalizable to the entire period of the vaccination campaign. In subsequent months, booster doses, the expansion of the Pfizer vaccine to children aged 5–11, waning immunity, and the establishment of the more virulent Delta and immune-evading Omicron variants have made estimation of vaccine effects more challenging. These later phases of the pandemic driven by new variants led to tens of millions of Covid-19 infections. We are not able to say definitively whether the averted cases, hospitalizations and deaths quantified in this analysis were truly averted or merely delayed. Nevertheless, it can be argued that these early averted cases were crucial, as this period was prior to the widespread availability of antiviral medication and a time with substantially lower population immunity against severe outcomes.

Additional assumptions should also be noted. The model structure is parsimonious and does not explicitly represent certain factors including population age structure, breakthrough infections or reinfections. We used a constant case hospitalization rate (CHR) and case fatality rate (CFR) for each state, computed based on COVID-19 outcomes during the 6 months before vaccination, to calculate counterfactual hospitalization and deaths in all scenarios. These choices ignore differences in age-specific behavior and probability of severe outcomes. These assumptions may have led to biases in our results, as the risk of both hospitalization and death following COVID-19 infection are known to increase with age. Since our CHR and CFRs were calculated based on population-level averages, we likely underestimated the number of averted hospitalizations and deaths in the first few months of the analysis when vaccine uptake was concentrated in populations most at risk of severe outcomes. These assumptions also neglect spatial differences in CHR and CFR within a state.

We also note that the full effect of COVID-19 vaccination on hospitalizations and deaths derives from two effects: those averted due to averted cases; and those averted due to improved outcomes in vaccinated individuals if infected. The estimates of averted hospitalizations and deaths in this analysis are restricted to the effect of averted cases and do not include reductions in the probability of hospitalization and death among the vaccinated if infected–as a result, they likely underestimate the true number of averted hospitalizations and deaths. Each of the approved COVID-19 vaccines has been shown to be highly effective in preventing severe outcomes in individuals infected by SARS-CoV-2. Here, we assumed that the contribution of the second effect was relatively small compared to the first, as the vaccines were shown to be highly effective at preventing infections in the short term after inoculation and against the strains circulating at the time of the study [23–25].

In conclusion, our analysis shows that COVID-19 vaccination reduced the burden of disease. Base case results indicate that COVID-19 vaccination was associated with over 8 million fewer confirmed cases, over 120 thousand fewer deaths, and 700 thousand fewer hospitalizations in the first six months of the campaign. As such, COVID-19 vaccines represented a critical component of the public health response to the COVID-19 pandemic in the US.

Supporting information

S1 Text. Additional model details.

(DOCX)

Click here for additional data file.^{(35.9KB, docx)}

S1 Fig. Time-series of confirmed Covid-19 cases in each state.

Observed data are shown in black (7-day backwards looking moving average) and fitted model results are in red.

(TIF)

Click here for additional data file.^{(331.9KB, tif)}

S2 Fig. Covid-19 cumulative vaccination rate from December 14^th, 2020 through June 2^nd, 2021.

The vaccination rate as a percentage of total state population is shown as indicated in the color bar.

(TIF)

Click here for additional data file.^{(5.2MB, tif)}

S1 Table. Estimated cumulative COVID-19 cases, deaths, hospitalizations averted by state.

(XLSX)

Click here for additional data file.^{(19.7KB, xlsx)}

Data Availability

The data used in this analysis, as well as the model code, are publicly available at https://github.com/tkcy/avertedcases_code.

Funding Statement

This study was sponsored by Pfizer Inc.

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PLoS One. doi: 10.1371/journal.pone.0275699.r001

Decision Letter 0

Sung-mok Jung

24 Oct 2022

PONE-D-22-26146The impact of COVID-19 vaccination in the US: averted burden of SARS-COV-2-related cases, hospitalizations and deathsPLOS ONE

Dear Dr. Yamana,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Comments were kindly provided by two reviewers. I concur with Reviewer 1's suggestion that the authors clarify whether the indirect effects of vaccination were considered in the present study. If not, I would incline that authors can discuss any potential impact on the proposed result. Furthermore, please add a detailed method and validation of the proposed model for naive readers.

==============================

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Sung-mok Jung

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Reviewer #1: Partly

Reviewer #2: Partly

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

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Reviewer #1: Vaccination programs contributed to preventing the number of infected with SARS-CoV-2, hospitalizations and deaths related to COVID-19 worldwide. Quantifying the averted those burdens is crucial to evaluate the impact of the vaccination and decision-making at the time. Using the meta-population model, the authors tried to estimate the numbers of prevented SARS-CoV-2-related cases, hospitalizations and deaths attributable to vaccination in the US. Because the authors applied the model previously published in a few scientific journals, I believe the model could be robust even in the current context of the manuscript. However, I have some comments on the manuscript, so I would like the authors to consider them carefully.

In terms of the characteristics of the effectiveness of the vaccination, could the authors clarify it? The effectiveness would be divided into two: direct and indirect. I presume that the authors estimated the direct effect of vaccination because they did not take into account the reduction of the transmission rate of vaccinated people. I would be grateful if the authors discussed such effectiveness using the cited references (e.g., Israel’s study explored the direct effect but not the indirect one).

Please provide the fitted baseline scenarios for counties or states; otherwise, it is difficult to judge the scientific validation of the calibrated mode.

I would recommend that the authors provide information on the model even though the model was previously well described elsewhere. For example, how did the authors cooperate vaccination into the model? Unfortunately, I could not understand how they were considered even though I read the Supplementary Material. Please share the equation.

I agree with the advantages of the meta-population model that could capture the various characteristics of county or state-specific. However, some measures, e.g., CHR and CFR, are very important to consider the discrepancy between age groups. Therefore, please validate the usage of averages of such measures by showing the comparison between observed values and estimates of the hospitalizations and deaths from baseline scenarios, for example.

I do not understand why the authors calculated averted hospitalization costs. Please provide more insights obtained from the analyses in the discussion.

In the result, the authors showed that 74.1% were susceptible on 14 December 2020. It would be great if the authors compared this value and observed one, possibly from serological surveys, in order to validate the baseline model.

Please provide results shown in Table 1 for each county or state as tables or figures (cumulative) in the result or Supplementary Material.

Although the authors mentioned some results (i.e. averted cases, hospitalizations and deaths) estimated from previously published studies, there are no mentions of the methodology differences. For the direct effect of the vaccination, averted cases and hospitalizations can be calculated by the difference in incidences between unvaccinated and vaccinated. Why did the authors use the meta-population model? If the interactions between counties or states, why do not explore the insights (e.g. difference between states) further? Those points need to be described more in the manuscript.

Reviewer #2: This paper aims at caracterizing the averted covid-19 burden from vaccination in the first 6 months of the campaign in the US. It's a short paper, to the point, with a simple application of an already developed model. While the paper is suitable for publication in PLoS One, I think there are some barriers to have it published as it is.

Mainly, a lot of details are lacking in understanding the paper. Mainly the methods are not described fully (but available from other papers), and some diagnosis figures (posterior predictive checks or equivalent, vaccination baseline scenario model fit ...) are missing.

Some other remarks:

One question: why model at county scale but only present results at national or state-scale ?

« We modeled the vaccine as producing direct effects only » but with susceptible depletion, aren’t some indirect effects also taken into account in this model ?

A lot of simplifying assumptions are made but the discussion does not detail enough their possible impact on concusion (especially focusing only on the first six month without omicron, not using an age-stratified model, no discussion on delayed vs averted death, )

"All 3 scenarios show that vaccination benefits were limited during the early months of vaccine rollout" This might an artifact from the age-prioritization of covid-19 no ?

What makes the model finding so different from other modeling studies cited in the conclusion ?.

The paper is not standalone:

"and its [the model] full details are described in Pei & Shaman [4]."

"Parameter values for μ, Z, D and θ are assigned according to the values inferred in Pei and Shaman [4]."

Figure S1 is refered nowhere in the main text nor in the SI text.

So with the addition that the code is not shared yet, nor are figures of model fits, so it's really hard to provide a proper review of the paper. Especially as this study is sponsored by Pfizer, there should be a strong justification of the conclusions from the methods, which I cannot judge due to the lack of insight.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2023 Apr 25;18(4):e0275699. doi: 10.1371/journal.pone.0275699.r002

Author response to Decision Letter 0

30 Jan 2023

We thank the reviewers and the editor for their constructive feedback. We have addressed each of the comments in the manuscript, as well as in the responses below. Please note that this response is also attached to this submission as a word document with color-coded replies, for easier reading.

1) In terms of the characteristics of the effectiveness of the vaccination, could the authors clarify it? The effectiveness would be divided into two: direct and indirect. I presume that the authors estimated the direct effect of vaccination because they did not take into account the reduction of the transmission rate of vaccinated people. I would be grateful if the authors discussed such effectiveness using the cited references (e.g., Israel’s study explored the direct effect but not the indirect one).

Thank you for raising this point – we have clarified this issue in the revised manuscript. We assumed vaccination to be fully protective against infection, so our analysis does include the indirect effects of vaccination. That is, because vaccinated individuals cannot be infected, they also cannot transmit the disease to others, thereby adding a level of protection to unvaccinated individuals. We have clarified this in the manuscript. We have also specified in the discussion which of the cited references consider direct vs indirect effects.

2) Please provide the fitted baseline scenarios for counties or states; otherwise, it is difficult to judge the scientific validation of the calibrated mode.

Thank you for this suggestion - the fitted baseline scenarios for each state have been added.

3) I would recommend that the authors provide information on the model even though the model was previously well described elsewhere. For example, how did the authors cooperate vaccination into the model? Unfortunately, I could not understand how they were considered even though I read the Supplementary Material. Please share the equation.

We expanded the model description, which now includes the equation used to simulate vaccinate to the model description and is found in the Supplementary Material. We have also included a table of model parameters (Table S1).

4) I agree with the advantages of the meta-population model that could capture the various characteristics of county or state-specific. However, some measures, e.g., CHR and CFR, are very important to consider the discrepancy between age groups. Therefore, please validate the usage of averages of such measures by showing the comparison between observed values and estimates of the hospitalizations and deaths from baseline scenarios, for example.

We have added a comparison of observed and estimated hospitalizations and deaths:

“Between December 14 and June 3, 2021, the baseline model estimated 16.1 million (95% CrI 15.1 – 18.3 million) total cases, 1.4 million (95% CrI 1.3 – 1.6 million) hospitalizations, and 246.7 thousand (95% CrI 230.4 – 279.6 thousand) deaths across the United States. These estimates were consistent with the 16.7 million cases, 1.3 million hospitalizations, and 250 thousand deaths reported in the JHU CSSE and HHS hospitalization datasets (1, 2).”

Additionally, we have added discussion on the impact of the age discrepancies.

5) I do not understand why the authors calculated averted hospitalization costs. Please provide more insights obtained from the analyses in the discussion.

Thank you for your comment. We have further expanded the Discussion with the following:

Additionally, our study sought to quantify the cost savings associated with vaccine-preventable severe disease (i.e. hospitalizations). The COVID-19 pandemic has challenged the capacity of hospitals and strained hospital and health system finances (3). In Scenario 3, we estimated that, in the first six months of rollout of the COVID-19 Vaccination program, the hospitalization cost savings were $17.3 billion. More conservatively, Scenario 1 estimated cost savings of $7.0 billion. While these numbers are not adjusted for geographic differences, they provide a ballpark estimate that is in line with a previously published analysis, which estimated $13.8 billion of costs from preventable COVID-19 hospitalizations from June through November 2021 (4). Our analyses show that the benefits of vaccination due to reduced hospitalizations translated into cost-savings in the billions of dollars. The broad availability of COVID-19 vaccines brought wide-ranging benefits from both a public health and economic perspective. Vaccination may also lessen other societal impacts associated with the pandemic (e.g. work productivity loss). The total economic impact may therefore be even greater than reported, and further studies elucidating those impacts are warranted.

6) In the result, the authors showed that 74.1% were susceptible on 14 December 2020. It would be great if the authors compared this value and observed one, possibly from serological surveys, in order to validate the baseline model.

The model estimates of susceptibility have previously been validated with serological data (5). Specifically, estimates of cumulative infections during 2020, generated by this model when coupled with inference approaches, were compared and validated with estimates of seroprevalence derived from serological surveys collected on multiple dates and for multiple locations in the US. These external serological data provided strong validation of the model estimates of cumulative infections and thus population susceptibility.

7) Please provide results shown in Table 1 for each county or state as tables or figures (cumulative) in the result or Supplementary Material.

A table presenting the mean number of averted cases, hospitalizations and deaths in each location has been added to the Supplementary Material.

8) Although the authors mentioned some results (i.e. averted cases, hospitalizations and deaths) estimated from previously published studies, there are no mentions of the methodology differences. For the direct effect of the vaccination, averted cases and hospitalizations can be calculated by the difference in incidences between unvaccinated and vaccinated. Why did the authors use the meta-population model? If the interactions between counties or states, why do not explore the insights (e.g. difference between states) further? Those points need to be described more in the manuscript.

We used the metapopulation model for this study as it has been extensively validated and used both for research and operational purposes over the course of the pandemic. In the revised manuscript, we have expanded our presentation of state-level results.

1) Mainly, a lot of details are lacking in understanding the paper. Mainly the methods are not described fully (but available from other papers), and some diagnosis figures (posterior predictive checks or equivalent, vaccination baseline scenario model fit ...) are missing.

Thank you for pointing out this oversight. We have added a figure showing baseline scenarios for each state. We have also added more information about the model structure and parameterization.

Some other remarks:

2) One question: why model at county scale but only present results at national or state-scale ?

While our model simulates Covid transmission dynamics at the county scale, we use state level data to inform our CHR, CFR and vaccination rates. We chose to present results at state scale to avoid introducing further uncertainty into the model by disaggregating these state-level data.

3) « We modeled the vaccine as producing direct effects only » but with susceptible depletion, aren’t some indirect effects also taken into account in this model ?

Thank you for raising this point – we should not have used the phrase ‘direct effects’ here, and have clarified this in the manuscript.

We assumed vaccination to be fully protective against infection, so our analysis does include the indirect effects of vaccination. That is, because vaccinated individuals cannot be infected, they also cannot transmit the disease to others, thereby adding a level of protection to unvaccinated individuals.

4) A lot of simplifying assumptions are made but the discussion does not detail enough their possible impact on concusion (especially focusing only on the first six month without omicron, not using an age-stratified model, no discussion on delayed vs averted death, )

We have added the following discussion on delayed vs averted deaths:

“… the establishment of the more virulent Delta and immune-evading Omicron variants have made estimation of vaccine effects more challenging. These later phases of the pandemic driven by new variants led to tens of millions of Covid-19 infections. We are not able to say definitively whether averted cases, hospitalizations and deaths quantified in this analysis were truly averted or merely delayed. Nevertheless, it can be argued that these early averted cases were crucial, as this period was prior to the widespread availability of antiviral medication and a time with substantially lower population immunity against severe outcomes.”

We have also discussed the impact of non-age stratified model

“These assumptions may have led to biases in our results, as the risk of both hospitalization and death following COVID-19 infection are known to increase with age. Since our CHR and CFRs were calculated based on population-level averages, we likely underestimated the number of averted hospitalizations and deaths in the first few months of the analysis when vaccine uptake was concentrated in populations most at risk of severe outcomes.”

5) "All 3 scenarios show that vaccination benefits were limited during the early months of vaccine rollout" This might an artifact from the age-prioritization of covid-19 no ?

We believe this finding is explained due to the relatively small proportion of vaccinated individuals, as well as the overall trend of declining transmission during that time.

6) What makes the model finding so different from other modeling studies cited in the conclusion ?.

Our findings are generally in agreement with previous studies cited in the manuscript. We have expanded our discussion of the differences in our methodological approach.

Our study also adds to the existing literature by considering 3 counterfactual scenarios, all without vaccinations, but with varying Rt, that mimic different possible population responses to disease spread in the absence of a vaccine.

The paper is not standalone:

7) "and its [the model] full details are described in Pei & Shaman [4]."

8) "Parameter values for μ, Z, D and θ are assigned according to the values inferred in Pei and Shaman [4]."

We have removed the dependence Pei & Shaman by providing a full model description and a table of model parameters (Table S1).

9) Figure S1 is refered nowhere in the main text nor in the SI text.

We added the reference in the methods.

10) So with the addition that the code is not shared yet, nor are figures of model fits, so it's really hard to provide a proper review of the paper. Especially as this study is sponsored by Pfizer, there should be a strong justification of the conclusions from the methods, which I cannot judge due to the lack of insight.

We hope that the revised materials provide confidence in the study methods and conclusions.

References

1. CSSE J. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University 2020 [Available from: https://github.com/CSSEGISandData/COVID-19.

2. Health UDo, Services H. COVID-19 reported patient impact and hospital capacity by facility 2020 [Available from: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh.

3. Association AH. Massive growth in expenses and rising inflation fuel continued financial challenges for America’s hospitals and health systems. Cost of Caring Report Published April. 2022.

4. Amin K, Cox C. Unvaccinated COVID-19 hospitalizations cost billions of dollars. Health System Tracker Available at: https://bit ly/3GTceUq [cited 2021, Dec 10]. 2022.

5. Pei S, Yamana TK, Kandula S, Galanti M, Shaman J. Burden and characteristics of COVID-19 in the United States during 2020. Nature. 2021;598(7880):338-41.

Attachment

Submitted filename: Response to reviewers_clean.docx

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PLoS One. doi: 10.1371/journal.pone.0275699.r003

Decision Letter 1

Sung-mok Jung

24 Feb 2023

PONE-D-22-26146R1The impact of COVID-19 vaccination in the US: averted burden of SARS-COV-2-related cases, hospitalizations and deathsPLOS ONE

Dear Dr. Yamana,

Please submit your revised manuscript by Apr 10 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Sung-mok Jung

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

The manuscript has been well revised overall. However, I concur with Reviewer 1's comment regarding potential biases in CFR and CHR. Such naively calculated CFR and CHR may have been underestimated if the time delay from infection (or confirmation) to death (or hospitalization) was not fully taken into consideration, especially if the epidemic size exponentially increased in the corresponding period. Thus, I would like to strongly recommend that authors reestimate the CFR and CHR while taking the time delay distribution into consideration.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I am concerned about CHR and CFR. When the authors estimated CFR as dividing cumulative deaths reported between Aug 1 to Dec 14 2020 by cumulative cases infected during the same period, it may lead to an underestimate due to the right censoring issue of reported deaths (reporting delay between infection and death), especially in the case the magnitude of infections is large in the late period.

Reviewer #2: Thanks for including the code and for making the paper standalone. I appreciated the comments added in the description.

I do not have any comment that would require another review. Just two things, not mandatory

- l106: . "We modeled the vaccine as providing 90% effectiveness against infection"

I think "and transmission" should be added there to make sure there isn't any misunderstanding. (perhaps that could be also mentionned in discussion).

- fig S2: any tentatitive explanation of why Rt is higher in winter than in summer would be welcome. I do think this figures should be in the main text (with another pannel being vaccines uptake ?).

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2023 Apr 25;18(4):e0275699. doi: 10.1371/journal.pone.0275699.r004

Author response to Decision Letter 1

4 Apr 2023

We thank the editor and reviewers for your time and comments. We have addressed each of the comments below, and in the manuscript.

Editor:

Thank you for raising this issue. We fully agree that the delays between case reports and the corresponding hospitalizations and deaths are important. We did in fact apply delays, but this was not clearly explained in our original manuscript. We have edited the manuscript to be more precise.

Reviewer #2: Thanks for including the code and for making the paper standalone. I appreciated the comments added in the description.

I do not have any comment that would require another review. Just two things, not mandatory

- l106: . "We modeled the vaccine as providing 90% effectiveness against infection"

I think "and transmission" should be added there to make sure there isn't any misunderstanding. (perhaps that could be also mentionned in discussion).

Thank you for your comments. We have added ‘and transmission’ to lines 107 and 108 to avoid any misinterpretation.

- fig S2: any tentatitive explanation of why Rt is higher in winter than in summer would be welcome. I do think this figures should be in the main text (with another pannel being vaccines uptake ?).

We moved the figure to the main text. We provide some possible explanations for temporal changes in Rt in the discussion (eg population behavior, new variant, Lines 387-390). While we’re very interested in other possible factors, this does not fall within the scope of this study.

Attachment

Submitted filename: response to reviewers.docx

Click here for additional data file.^{(15KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0275699.r005

Decision Letter 2

Sung-mok Jung

12 Apr 2023

The impact of COVID-19 vaccination in the US: averted burden of SARS-COV-2-related cases, hospitalizations and deaths

PONE-D-22-26146R2

Dear Dr. Yamana,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Sung-mok Jung

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I appreciate the authors’ effort in integrating all the comments in the manuscript. The manuscript has been well-revised, and in my opinion, it is now ready for acceptance. However, if the authors could clarify how exactly the reporting delay was considered in the calculation of CFR and CHR (e.g., assuming identical delays across all cases or back-projecting the epidemic curve with the distribution), that would be more helpful for naïve readers to follow. Again, congratulations!

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0275699.r006

Acceptance letter

Sung-mok Jung

17 Apr 2023

PONE-D-22-26146R2

The impact of COVID-19 vaccination in the US: averted burden of SARS-COV-2-related cases, hospitalizations and deaths

Dear Dr. Yamana:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Sung-mok Jung

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Text. Additional model details.

(DOCX)

Click here for additional data file.^{(35.9KB, docx)}

S1 Fig. Time-series of confirmed Covid-19 cases in each state.

Observed data are shown in black (7-day backwards looking moving average) and fitted model results are in red.

(TIF)

Click here for additional data file.^{(331.9KB, tif)}

S2 Fig. Covid-19 cumulative vaccination rate from December 14^th, 2020 through June 2^nd, 2021.

The vaccination rate as a percentage of total state population is shown as indicated in the color bar.

(TIF)

Click here for additional data file.^{(5.2MB, tif)}

S1 Table. Estimated cumulative COVID-19 cases, deaths, hospitalizations averted by state.

(XLSX)

Click here for additional data file.^{(19.7KB, xlsx)}

Attachment

Submitted filename: Response to reviewers_clean.docx

Click here for additional data file.^{(28.5KB, docx)}

Attachment

Submitted filename: response to reviewers.docx

Click here for additional data file.^{(15KB, docx)}

Data Availability Statement

The data used in this analysis, as well as the model code, are publicly available at https://github.com/tkcy/avertedcases_code.

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PERMALINK

The impact of COVID-19 vaccination in the US: Averted burden of SARS-COV-2-related cases, hospitalizations and deaths

Teresa K Yamana

Marta Galanti

Sen Pei

Manuela Di Fusco

Frederick J Angulo

Mary M Moran

Farid Khan

David L Swerdlow

Jeffrey Shaman

Roles

Abstract

Introduction

Methods

Hospitalizations and deaths

Hospitalization costs

Results

Initialization

Fig 1. Initial conditions, case hospitalization rates and case fatality rates.

Model results

Fig 2. Covid-19 cases and Rt from December 14th, 2020 through June 2nd, 2021.

Table 1. Total cumulative COVID-19 cases, deaths, hospitalizations averted and hospitalization cost savings.

Fig 3.

Fig 4.

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Sung-mok Jung

Roles

Author response to Decision Letter 0

Decision Letter 1

Sung-mok Jung

Roles

Author response to Decision Letter 1

Decision Letter 2

Sung-mok Jung

Roles

Acceptance letter

Sung-mok Jung

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Fig 2. Covid-19 cases and Rt from December 14^th, 2020 through June 2^nd, 2021.