A Review of Hot Deck Imputation for Survey Non-response
Clin Infect Dis. 2021 Jul ten : ciab626.
Severe Astute Respiratory Syndrome Coronavirus ii Cumulative Incidence, United States, August 2020–December 2020
Patrick Sean Sullivan
oneDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, U.s.
Aaron J Siegler
iDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United states
Kayoko Shioda
iiGangarosa Department of Environmental Health, Rollins Schoolhouse of Public Health, Emory University, Atlanta, Georgia, USA
Eric Due west Hall
oneSection of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, Usa
Heather Bradley
3Section of Population Health Sciences, Georgia State Academy Schoolhouse of Public Health, Atlanta, Georgia, U.s.
Travis Sanchez
1Section of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United states
Nicole Luisi
iDepartment of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
Mariah Valentine-Graves
1Department of Epidemiology, Rollins School of Public Wellness, Emory University, Atlanta, Georgia, USA
Kristin North Nelson
1Department of Epidemiology, Rollins Schoolhouse of Public Wellness, Emory University, Atlanta, Georgia, United states of america
Mansour Fahimi
4Marketing Systems Grouping, Horsham, Pennsylvania, U.s.a.
Amanda Kamali
vCalifornia Department of Public Wellness, Sacramento, California, Usa
Charles Sailey
6Molecular Testing Labs, Vancouver, Washington, The states
Benjamin A Lopman
aneDepartment of Epidemiology, Rollins School of Public Wellness, Emory University, Atlanta, Georgia, Us
Abstract
Groundwork
Reported coronavirus disease 2019 (COVID-19) cases underestimate severe astute respiratory syndrome coronavirus 2 (SARS-CoV-two) infections. We conducted a national probability survey of U.s. households to judge cumulative incidence adapted for antibody waning.
Methods
From August–December 2020 a random sample of US addresses were mailed a survey and self-nerveless nasal swabs and dried blood spot cards. 1 adult household member completed the survey and mail specimens for viral detection and total (immunoglobulin [Ig] A, IgM, IgG) nucleocapsid antibody by a commercial, emergency employ authorization–approved antigen capture assay. We estimated cumulative incidence of SARS-CoV-2 adjusted for waning antibodies and calculated reported fraction (RF) and infection fatality ratio (IFR). Differences in seropositivity amongst demographic, geographic, and clinical subgroups were explored.
Results
Amidst 39 500 sampled households, 4654 respondents provided responses. Cumulative incidence adjusted for waning was 11.9% (95% apparent interval [CrI], 10.five%–xiii.five%) as of 30 Oct 2020. Nosotros estimated 30 332 842 (CrI, 26 703 753–34 335 338) total infections in the US developed population by xxx October 2020. RF was 22.three% and IFR was 0.85% among adults. Black non-Hispanics (Prevalence ratio (PR) two.2) and Hispanics (PR, three.one) were more likely than White not-Hispanics to exist seropositive.
Conclusions
Ane in eight The states adults had been infected with SARS-CoV-2 by October 2020; yet, few had been accounted for in public health reporting. The COVID-19 pandemic is likely substantially underestimated past reported cases. Disparities in COVID-19 by race observed amidst reported cases cannot be attributed to differential diagnosis or reporting of infections in population subgroups.
Keywords: SARS-CoV-2, serology, probability survey, incidence, viral detection
A complete agreement of the U.s. coronavirus illness 2019 (COVID-nineteen) epidemic requires measuring unreported (ie, non diagnosed or diagnosed just not reported to public health surveillance systems) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-two) infections. Cumulative SARS-CoV-2 incidence must account for unreported cases and systematic differences betwixt documented and undocumented cases related to healthcare access or health-seeking behaviors (eg, people experiencing symptoms are more likely to exam). Serosurveys place people who have developed an immune response to SARS-CoV-2, regardless of symptoms, seeking medical care or being diagnosed or reported to public health surveillance systems. Nevertheless, most serosurveys to appointment are subject to selection biases past overrepresenting people concerned about symptoms or exposures, people seeking medical evaluation, or high-risk subpopulations (eg, healthcare workers). Accurate US national estimates of the cumulative incidence of SARS-CoV-ii infection require minimally biased, population-based surveys and screening with viral and antibody detection assays.
The natural history of SARS-CoV-2 infection and immunity informs this effort. Relying solely on detectable levels of SARS-CoV-ii antibodies to estimate cumulative incidence is inadequate because antibodies wane in the months following primary infection [1, ii]. Because of antibody waning, population anti–SARS-CoV-2 antibiotic prevalence in New York City and the Britain decreased during a time of increasing total reported cases [two-4]. Further, antibodies confronting the nucleocapsid (N) protein likely wane faster than antibodies against the spike (S) protein [v]. Thus, cantankerous-sectional prevalence estimates that rely on antibody testing, especially studies conducted subsequently spring 2020, likely essentially underestimate cumulative incidence. Specimens collected later in epidemic are increasingly subject to false-negative antibody results, that is, failing to identify antibodies in previously infected persons.
To develop a nationally representative estimate of the cumulative incidence of SARS-CoV-ii, we conducted a national probability survey of US households with mailed at-home specimen collection and polymerase concatenation reaction (PCR) and serology testing [vi]. We calculated adjusted seroprevalence and used a Bayesian model to account for waning antibodies to guess the overall cumulative incidence in the The states as of 30 October 2020 [7].
METHODS
Sampling
As previously described [6], we used a national address-based household sample of all residential commitment points in the U.s. (about 130 million addresses) that has been used in numerous wellness research studies [8-10]. To recruit ≥4000 responding households, 39 500 addresses were sampled. Due to state-level interest in estimates of cardinal parameters, households were oversampled in California (6500 oversampled) and Georgia (12 000 oversampled). In response to differentially low return rates by Black and Hispanic respondents, households in census tracts with >l% Black residents and households with surnames likely to stand for Hispanic ethnicity [eight] were likewise oversampled.
Survey and Laboratory Procedures
One person per selected household was asked to enumerate household members and each person'southward age; 1 household member aged ≥xviii years was randomly selected to participate in the COVIDVu study. Consenting participants completed an online survey and provided a cocky-collected anterior nares (AN) swab and a self-collected dried claret spot (DBS) card as previously described [11] and returned specimens to a central laboratory by postal service [12]. AN swabs were tested past PCR using the Thermo EUA (emergency use authorization) Version 2 kit (Thermo Fisher, Waltham, MA). DBS specimens were tested using the BioRad Platelia Total Antibody test (BioRad, Hercules, CA) that targets the NC protein as a laboratory-developed test under Clinical Laboratory Improvements Act/College of American Pathologists (CLIA/CAP) protocols. The Platelia assay has advantages for the purpose of a serosurvey: it detects multiple antibiotic isotypes; targets the NC protein, which indicates natural infection but not vaccination; and has robust sensitivity (98.0%) and specificity (99.3%) [13]. To narrate potential misclassification biases associated with test functioning, we adjusted prevalence estimates for test operation per Sempos and Tian. [xiv]. Nosotros resampled each adjusted prevalence approximate and examination operation parameter estimate (ie, sensitivity and specificity) to estimate conviction intervals (CIs; k = 100 000 iterations) [15].
Antibodies to NC wane more quickly than antibodies to S [v]. Therefore, we quantified the magnitude of potential bias of lower sensitivity of the BioRad exam by retesting a subset of BioRad antibody-negative specimens with the EUROIMMUN immunoglobulin (Ig) G assay (Lübeck, Germany) that targets the S protein. The specimen subset comprised participants with negative total Ig results and a loftier pretest probability of prior infection (n = 122; eg, participants reporting previous diagnosis, hospitalization for COVID-19, or reported loss of smell or taste since 1 January 2020) and a group of randomly selected full Ig-negative participants (n = 275).
The Emory University Institutional Review Board approved the COVIDVu study.
Computation of Sample Weights
Sample weights were developed to facilitate unbiased estimation of key parameters that represent the noninstitutionalized, housed adults (ie, aged ≥18 years US population). Hierarchical hot deck imputation [16] was performed to ensure no participants were missing data for cardinal variables (gender, 0.1% missing; education, i.2% missing; race, 3.2% missing; ethnicity, i.6% missing; marital status, 2.2% missing; income, 13.8% missing) needed for weighting. These imputation steps were carried out sequentially within homogeneous imputation cells, each time using the variables previously imputed for the construction of cells for the next variable to exist imputed. Side by side, design weights were computed to reflect the pick probabilities for household addresses and the selection of 1 adult per household and adjusted to account for differential nonresponse. For this purpose, Nomenclature and Regression Tree analysis was used to place characteristics that were differentially distributed amid responding vs nonresponding households. Variables identified as key predictors of nonresponse were homeownership status (rent vs own), residing in a household located in a census tract with >fifty% Black residents, presence of Hispanic surname, and presence of household information nigh income or number of adults on the address-based sampling frame.
In the next step, nonresponse-adjusted design weights were post-stratified to distributions of demographic characteristics amid United states adults. Specifically, an iterative proportional plumbing fixtures (raking) procedure was used to align weighted distributions of respondents with respect to gender, age, race/ethnicity, didactics, income, marital status, and census division [17]. Weights were examined to find extreme outliers and trimmed at the 99th percentile on both ends of the distribution.
Seroprevalence analyses were conducted in SAS v9.4 and SUDAAN. Using the sampling weights, we estimated the weighted seroprevalence and 95% modified Wilson score confidence limits of total Ig for the entire sample and for demographic and clinical factors of involvement. To place meaning differences, prevalence ratios (PRs) and corresponding 95% CIs were estimated using weighted logistic regression procedures in SUDAAN. A χ 2 examination for linear trend in proportions was performed for seroprevalence across levels of didactics.
Interpretation of SARS-CoV-2 Cumulative Incidence and Infection Fatality Ratio Accounting for Waning Antibodies
To adjust for SARS-CoV-2 antibodies waning below the detectable levels [xviii, xix], nosotros used a Bayesian model to guess the cumulative incidence of SARS-CoV-2 at the median date of our sample (thirty October 2020). The model uses population-level cross-sectional data from the present study and accounts for both the expected timeline of seroconversion and the timeline for seroreversion. Details of this model have been described [7]. Briefly, the model estimates the timing of infection based on empirical data on the distribution of time from symptom onset to expiry and is calibrated with the national weighted seroprevalence gauge from the present study by applying cumulative density functions for the fourth dimension from seroconversion to seroreversion. The model generates a daily estimate of new infections and derives a cumulative incidence approximate past summing the total number of modeled infections since the beginning of the epidemic. The model directly estimates the infection fatality ratio (IFR) [vii]. We also estimated the IFR for 2 age strata (55–64 and ≥65 years) where adequate historic period-specific time-serial data were available in Centers for Disease Control and Prevention (CDC) public utilize datasets. An exploratory analysis of cumulative incidence was conducted for CI through 31 Dec 2020 using updated mortality data reported through 15 April 2021.
Adding of Reported Fraction
We defined reported fraction as the ratio of reported cases in the United States equally of 30 October 2020 (using information from the CDC'south public apply dataset [xx] and assuming that those aged eighteen–xix years represented 21% of the 10- to 19-year age group) and the cumulative incidence as of the same date. Credible intervals (CrIs) were constructed using the 95% CrIs for the cumulative incidence of the denominator [21].
RESULTS
Sampling, Participation Rates, and Representation of Racial/Ethnic Minorities
A full of 39 500 registration packages were mailed to sampled The states households from July 2020 through Oct 2020 (Effigy i). There were 2444 addresses (half-dozen.2%) that were unable to receive mail and excluded from the sample. A total of 5666 surveys (15.iii%) were completed. Of those completing surveys, 4654 (12.half-dozen% of sampled households) likewise returned a DBS specimen nerveless during the period ix August 2020–8 Dec 2020 with a valid antibody result. There were 450 other participants (7.ix%) who did not accept a total Ig result simply had a valid PCR test. The overall participation rate was 15.3% for the survey only and 12.6% for the survey and a valid antibody test result.
Consort diagram for a national household probability sample of The states households to judge the cumulative incidence of severe acute respiratory syndrome coronavirus 2 infection in the United States, 2020. Abbreviations: AN, anterior nares; COVIDVu, coronavirus affliction 2019 study; Ig, immunoglobulin.
Antibody and PCR RNA Positivity
Overall, 229 of 4654 (4.92%) DBS specimens were reactive for full Ig (ie, unadjusted seroprevalence); these made inference to the seroprevalence amid 242 875 582 The states adults (Table 1). The weighted seroprevalence was 5.24% (CI, 4.14%–6.60%); seroprevalence results suggested that the number of US adults with prevalent anti–SARS-CoV-ii Ig not adjusted for waning antibodies for the menstruum 9 August 2020–8 December 2020 was 12 722 882. In a sensitivity analysis adjusting for examination operation [13], the overall prevalence of antibodies was lower (four.71%; CI, 3.3–6.xi; Supplementary Table 1). At that place were 36 of 4984 (0.72%) AN specimens that were positive by PCR testing, of which 10 (29%) were also reactive for total Ig.
Table 1.
Severe Acute Respiratory Syndrome Coronavirus 2 Serology and Viral Detection Results for a Probability Sample of 4654 US Households and Weighted Results Compared With the United states Population Aged ≥ 18 Years, United States, 2020
| Ig Only | Ig or AN | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Sample | Weighted Sample | Sample | Weighted Sample | US Population Anile ≥18 Yearsa | ||||||
| Characteristic | N | % | Weighted N | Column % | North | % | Weighted N | Column % | N | % |
| Overall | 4654 | 100 | 242 875 582 | 100 | 5104 | 100 | 242 972 595 | 100 | 255 200 373 | 100 |
| Sex | ||||||||||
| Male | 1927 | 41.four | 115 613 214 | 47.half-dozen | 2129 | 45.7 | 115 725 392 | 47.6 | 124 348 656 | 48.seven |
| Female | 2727 | 58.6 | 127 262 368 | 52.4 | 2975 | 63.9 | 127 247 203 | 52.4 | 130 851 717 | 51.3 |
| Race/Ethnicity | ||||||||||
| Hispanic | 607 | 13 | forty 277 007 | 16.half dozen | 668 | fourteen.iv | 40 389 513 | sixteen.6 | 41 884 672 | xvi.4 |
| Non-Hispanic Black | 683 | 14.vii | 27 643 982 | xi.four | 797 | 17.1 | 28 062 416 | 11.half-dozen | 32 169 434 | 12.6 |
| Non-Hispanic White | 3063 | 65.8 | 153 881 404 | 63.4 | 3316 | 71.iii | 153 414 972 | 63.two | 162 644 095 | 63.seven |
| Other | 301 | half-dozen.5 | 21 073 189 | eight.7 | 323 | 6.9 | 21 105 695 | 8.7 | eighteen 502 172 | 7.iii |
| Age, years | ||||||||||
| 18–34 | 1013 | 21.viii | 67 946 989 | 28 | 1103 | 23.7 | 68 229 816 | 28.ane | 76 159 527 | 29.8 |
| 35–44 | 777 | 16.7 | xl 347 844 | sixteen.half dozen | 850 | 18.three | 40 347 557 | 16.6 | 41 659 144 | 16.three |
| 45–54 | 765 | 16.4 | 39 524 761 | 16.iii | 833 | 17.9 | 39 481 380 | 16.3 | 40 874 902 | 16 |
| 55–64 | 926 | 19.9 | 41 638 646 | 17.i | 1012 | 21.seven | 41 389 099 | 17 | 42 448 537 | xvi.6 |
| 65+ | 1173 | 25.2 | 53 417 341 | 22 | 1306 | 28.1 | 53 524 744 | 22 | 54 058 263 | 21.two |
| US census region | ||||||||||
| Northeast | 476 | 10.2 | 42 937 799 | 17.7 | 519 | 11.2 | 43 151 385 | 17.8 | 44 478 478 | 17.4 |
| Midwest | 591 | 12.vii | 51 141 237 | 21.1 | 632 | 13.vi | fifty 719 007 | 20.9 | 52 980 427 | xx.8 |
| Due south | 2275 | 48.9 | 90 171 242 | 37.1 | 2531 | 54.four | xc 429 763 | 37.2 | 97 108 254 | 38.1 |
| Westward | 1312 | 28.2 | 58 625 304 | 24.1 | 1422 | 30.six | 58 672 440 | 24.2 | 60 633 214 | 23.8 |
Characterizing Potential Bias From Lower Sensitivity for Detection of Antibodies to NC Protein
Among 122 samples with a negative NC Ig assay and a clinical history compatible with COVID-19 illness, 1 of 122 (0.8%) had a reactive issue on the IgG analysis for the S protein. No specimen from the 275 randomly selected NC Ig-nonreactive specimens was reactive on the IgG assay for the S protein. Therefore, we believed that the choice of the NC target did not upshot in misclassification bias and used the results of the BioRad analysis for all analyses reported hither.
Associations of Antibody Positivity
Weighted seroprevalence was 3-fold college among Hispanic and two-fold higher among Blackness, non-Hispanic participants compared with White, non-Hispanic participants (Table ii). Compared with persons aged ≥65 years, weighted seroprevalence was three times college in those aged 18–34 or 35–44 years. Weighted seroprevalence was nearly double among persons living in the South compared with the Westward, and results showed an inverse relationship between educational attainment and seroprevalence (trend in proportions, P = .008). Seroprevalence was higher among participants residing in metropolitan areas and who reported cold/influenza symptoms or loss of taste or smell since i January 2020. Overall, nearly nine in 10 Ig-seropositive participants reported at least 1 symptom (loss of taste/smell, influenza, or any of the other potential symptoms listed in the Table ii footnote), and viii in 10 of those who were SARS-CoV-2–seronegative reported ≥1 symptom since 1 January 2020. There was no deviation in seropositivity by comorbidities.
Table ii.
Unweighted and Weighted Astringent Acute Respiratory Syndrome Coronavirus 2 Antibody Prevalence for a Probability Sample of 4654 US Households and Weighted Results and Prevalence Ratios, United states, 2020
| Unweighted | Weighted | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Characteristic | due north | N | Prevalence | n | N | Prevalence | 95% CIa | Prevalence Ratio | 95% CI | ||
| Overall | 229 | 4654 | iv.9 | 12 722 882 | 242 875 582 | 5.24 | four.14 | 6.60 | northward/a | ||
| Sexual practice | |||||||||||
| Male | 92 | 1927 | 4.8 | five 983 835 | 115 613 214 | 5.18 | 3.59 | 7.41 | Reference | ||
| Female | 137 | 2727 | v.0 | 6 739 047 | 127 262 368 | 5.30 | 3.93 | 7.10 | 1.02 | .64 | 1.64 |
| Race/Ethnicity | |||||||||||
| Hispanic | 51 | 607 | viii.4 | iv 631 941 | twoscore 277 007 | 11.50 | vii.54 | 17.16 | three.11 | 1.83 | v.28 |
| Non-Hispanic Black | 71 | 683 | 10.4 | 2 200 979 | 27 643 982 | 7.96 | 4.73 | 13.xi | 2.xv | 1.17 | iii.97 |
| Non-Hispanic White | 104 | 3063 | three.iv | five 692 713 | 153 881 404 | 3.70 | two.67 | five.10 | Reference | ||
| Other | 3 | 301 | 1.0 | 197 250 | 21 073 189 | 0.94 | 0.27 | iii.24 | 0.25 | .06 | 1.01 |
| Age, years | |||||||||||
| xviii–34 | 72 | 1013 | 7.i | 4 558 387 | 67 946 989 | 6.71 | 4.44 | 10.01 | 2.70 | 1.18 | 6.18 |
| 35–44 | 53 | 777 | 6.eight | two 963 168 | xl 347 844 | 7.34 | four.65 | 11.41 | 2.96 | 1.26 | 6.93 |
| 45–54 | 33 | 765 | 4.3 | i 911 289 | 39 524 761 | 4.84 | 2.59 | viii.84 | 1.95 | .75 | 5.06 |
| 55–64 | 37 | 926 | 4.0 | ane 963 111 | 41 638 646 | 4.71 | ii.76 | 7.94 | 1.90 | .77 | 4.66 |
| 65+ | 34 | 1173 | 2.9 | 1 326 927 | 53 417 341 | 2.48 | 1.22 | four.98 | Reference | ||
| US demography region | |||||||||||
| Northeast | 20 | 476 | 4.2 | 2 619 466 | 42 937 799 | half dozen.10 | 3.54 | 10.32 | 1.67 | .79 | iii.55 |
| Midwest | nineteen | 591 | 3.2 | 2 027 923 | 51 141 237 | 3.97 | 2.24 | vi.92 | ane.09 | .50 | ii.36 |
| South | 149 | 2275 | 6.6 | 5 934 236 | 90 171 242 | six.58 | 4.66 | ix.21 | one.80 | .97 | 3.36 |
| W | 41 | 1312 | 3.i | 2 141 257 | 58 625 304 | 3.65 | 2.18 | 6.07 | Reference | ||
| Urbanicity (zip code) | |||||||||||
| Micropolitan/Small town/Rural | 20 | 468 | 4.three | 728 649 | 32 292 975 | 2.26 | one.20 | iv.20 | Reference | ||
| Metropolitan | 209 | 4186 | 5.0 | 11 994 233 | 210 582 607 | five.70 | 4.46 | 7.25 | two.52 | 1.27 | v.00 |
| Education | |||||||||||
| High School/GED or less | 47 | 698 | 6.7 | v 598 377 | 85 965 483 | 6.51 | four.23 | ix.91 | 1.63 | .94 | 2.82 |
| Some college/Acquaintance's degree | 71 | 1409 | 5.0 | 3 727 595 | 69 226 861 | five.38 | three.67 | 7.84 | ane.35 | .81 | 2.24 |
| Available'south caste | 68 | 1430 | 4.viii | 2 228 895 | 55 756 279 | four.00 | 2.85 | five.57 | ref | ||
| Graduate caste | 43 | 1117 | three.nine | one 168 014 | 31 926 958 | 3.66 | 2.23 | 5.93 | 0.92 | .50 | one.67 |
| Annual income | |||||||||||
| $0–$24 999 | 39 | 721 | v.4 | ane 165 276 | 29 566 723 | 3.94 | 2.32 | six.62 | 0.79 | .40 | 1.57 |
| $25 000–$49 999 | 56 | 916 | six.1 | 3 276 418 | 41 443 877 | seven.91 | 4.89 | 12.53 | one.59 | .84 | 3.03 |
| $l 000–$99 999 | 69 | 1445 | 4.8 | 3 638 036 | 73 211 031 | 4.97 | 3.23 | seven.57 | Reference | ||
| $100 000–199 999 | 55 | 1125 | iv.9 | three 435 662 | 67 795 060 | 5.07 | 3.26 | 7.79 | i.02 | .55 | 1.89 |
| $200 000+ | x | 447 | 2.two | 1 207 490 | 30 858 891 | three.91 | one.61 | 9.18 | 0.79 | .29 | 2.fifteen |
| Health insurance | |||||||||||
| None | 19 | 263 | vii.two | one 243 547 | xiii 358 208 | 9.31 | 4.35 | 18.83 | 1.88 | .83 | iv.28 |
| Medicare/Medicaid/Other government plan | lx | 1352 | four.iv | two 887 942 | 66 230 875 | iv.36 | ii.lxx | 6.98 | 0.88 | .l | iv.28 |
| Individual/Parent'due south programme | 135 | 2734 | iv.9 | 7 286 120 | 147 299 448 | iv.95 | 3.65 | six.67 | Reference | ||
| Don't know | 15 | 305 | 4.ix | 1 305 273 | xv 987 051 | 8.16 | 3.73 | 16.94 | ane.65 | 0.71 | 3.84 |
| Comorbidities | |||||||||||
| Diabetes | 27 | 438 | six.2 | 683 580 | 22 485 621 | 3.04 | 1.08 | eight.26 | 0.56 | 0.19 | 1.67 |
| Heart condition | 11 | 325 | 3.4 | 430 691 | 16 727 097 | 2.57 | 1.03 | 6.32 | 0.47 | 0.18 | ane.26 |
| Chronic lung disease | 16 | 389 | 4.i | i 274 183 | 21 451 947 | 5.94 | 2.44 | thirteen.77 | ane.15 | 0.45 | 2.94 |
| Hypertension | 50 | 1045 | 4.8 | ane 175 196 | 46 383 405 | ii.53 | 1.54 | 4.xv | 0.43 | 0.25 | 0.76 |
| Symptoms since 1 Jan 2020 | |||||||||||
| Common cold/Flu | 149 | 1917 | seven.viii | viii 053 479 | 98 083 444 | 8.21 | 6.xiv | 10.90 | ii.55 | i.57 | 4.13 |
| Loss of gustation or odor | 103 | 272 | 37.9 | 5 396 043 | 13 179 352 | xl.94 | 30.94 | 51.75 | 12.84 | 8.50 | xix.37 |
| Any other symptomb | 202 | 3803 | 5.3 | 11 222 678 | 196 089 280 | five.72 | 4.46 | 7.31 | 1.78 | 0.86 | 3.lxx |
| Symptoms in by 30 days | |||||||||||
| Loss of taste or smell | 25 | 85 | 29.iv | ii 185 030 | 4 449 757 | 49.x | 32.16 | 66.26 | eleven.11 | 7.06 | 17.49 |
| Any other symptomb | 131 | 2816 | 4.vii | 7 920 970 | 144 955 397 | 5.46 | 4.04 | 7.35 | 1.11 | 0.69 | ane.eighty |
| Month of sample collection | |||||||||||
| August | 36 | 1195 | iii.0 | 4 100 580 | 98 937 128 | 4.14 | 2.67 | 6.37 | Reference | ||
| September | 23 | 406 | 5.7 | 1 981 937 | 33 460 432 | 5.92 | iii.33 | 10.31 | one.43 | 0.69 | two.95 |
| October | 27 | 812 | 3.3 | 2 675 819 | 55 101 083 | 4.86 | 2.90 | eight.02 | 1.17 | 0.60 | 2.31 |
| ane November–8 December | 143 | 2241 | 6.4 | 3 964 546 | 53 376 939 | seven.xvi | 4.86 | 10.44 | 1.73 | 0.96 | 3.ten |
Estimated Cumulative Incidence of SARS-CoV-ii Infections and IFR Adjusted for Waning Antibodies
Estimated cumulative incidence adjusted for waning antibodies was eleven.9% (CrI, 10.v%–13.5%) on 30 October 2020 (Figure ii). The estimated IFR was 0.85% (CrI, 0.76%–0.97%) for adults aged ≥18 years, 0.59% (0.45%–0.83%) for those aged 55–64 years, and vii.i% (v.04%–10.38%) among those anile ≥65 years. Nosotros estimated 30 332 842 (CrI, 26 703 753–34 335 338) infections amidst adults aged ≥18 years by thirty October 2020. At that place were 6 769 219 cumulative reported COVID-19 cases in adults through 30 October 2020, suggesting that well-nigh i in 5 (22.3%; Crl, xix.vii%–25.iii%) of developed SARS-CoV-2 infections had been reported as a COVID-xix case by 30 October 2020. The exploratory estimate for developed cumulative incidence through 31 December 2020 was 18.2% (CrI, 16.1%–20.iv%). Estimated daily seroprevalence is also presented in Effigy 2. Estimated daily seroprevalence tracked in parallel to cumulative incidence through summer 2020 just then began increasing more slowly than cumulative incidence.
Estimated cumulative incidence of severe astute respiratory syndrome coronavirus 2 infection adapted for waning antibodies and daily seroprevalence, The states, 2020. Abbreviation: COVIDVu, coronavirus affliction 2019.
Give-and-take
Past accounting for data on the distribution of fourth dimension from exposure to seroconversion, seroreversion, and fourth dimension to death, we report that although the daily seroprevalence of antibodies to SARS-CoV-2 remained relatively stable at betwixt 4% and 5% from Baronial 2020 to October 2020, cumulative incidence connected to climb. The cumulative incidence rose to more than 30 meg U.s. adults, and nearly 1 in 8 had been infected with the virus by the end of October 2020.
Agreement the extent of the SARS-CoV-2 epidemic in the U.s.a. has been challenging since the get-go of the epidemic for multiple reasons. Start, deficits in testing chapters were acute in the early months of the epidemic, resulting in substantial underdiagnosis of COVID-19 cases, particularly mildly symptomatic cases [22]. 2d, early serosurveys were frequently based on convenience samples and field of study to choice bias for people concerned almost exposure or symptoms [half dozen, 23]. 3rd, many SARS-CoV-2 infections may be asymptomatic; asymptomatic or paucisymptomatic persons are unlikely to seek diagnostic testing and be reported as cases. Fourth, reporting systems for COVID-nineteen had to be established very rapidly by public health institutions, and at that place was substantial underreporting of demographic data, including race/ethnicity, needed to describe relative impacts of the epidemic [24, 25]. Finally, naturally acquired antibodies to SARS-CoV-two wane over time, and antibodies directed toward different antigenic targets might wane at different rates [26]. Every bit a result, seroprevalence estimates alone are not a reliable indicator of cumulative incidence, even over the short history of the US epidemic. Our written report addressed many of these challenges by collecting data from randomly selected U.s. households (minimizing option bias), oversampling to attain a diverse sample, and using statistical methods to business relationship for waning antibodies.
Previously reported US seroprevalence studies have featured varying degrees of probability sampling methods and convenience sampling. One study synthetic a demographically and geographically representative sample from a sampling frame of screened volunteers [27]. However, to our knowledge, no study has reported national data from a probability sample of US households [28]. A synthesis of population-based samples and remnant clinical samples yielded a seroprevalence of 14.three% by mid-November 2020 but did non consider waning antibodies and called for additional serosurvey data [29]. A written report of The states plasma donors reported seroprevalence of viii.0% in July 2020, but dialysis patients tend to be significantly older than US adults overall [30]. Other seroprevalence studies accept used diverse strategies to minimize bias, including the use of proprietary sampling frames (4% in Los Angeles April 2020 [23]), use of remnant blood specimens from blood donors (1.8% prevalence in June 2020 –August 2020 [31]) or specimens submitted for other laboratory testing (range of one.0%–6.9% beyond 10 US sites in March 2020–May 2020 [32]), and flow sampling through grocery stores (12.5% in New York City in March 2020 [33]). The CDC publishes state-specific seroprevalence estimates from commercial laboratory samples, which was >20% in many states as of February 2021 [34]. The CDC reported results from local population-based household samples in metropolitan Atlanta, Georgia (two.5% in April 2020–May 2020 [35]), and Indiana (seroprevalence 1.0% in May 2020–June 2020 [36]). Reports of previous surveys have recognized the limitations of seroprevalence studies alone to gauge cumulative incidence and have called for representative surveys to minimize sampling bias [37].
Our crude antibody prevalence was adjusted in ii means. Kickoff, we applied sampling weights to our observed data to account for the sampling process, resulting in a modest increase in the seroprevalence guess. Second, we accounted for waning antibodies [vii]. Although studies conducted in the kickoff half of 2020 might take been minimally impacted past waning antibodies, serology studies that collected data in the second half of 2020 were subject to substantial misclassification bias, perhaps differentially past symptomatology [38, 39]. In a period prevalence survey that spanned several months, people with a previous SARS-CoV-two infection might lose detectable antibodies and exist misclassified; on the other hand, in periods of high incidence (eg, December 2020), people with positive PCR tests indicating infection might be misclassified equally non beingness a cumulative incident case because antibodies had non however developed. These potentially misclassified statuses are temporally varying during the beginning of an epidemic: misclassification due to waning antibodies will be a more than prominent bias in later months, and misclassification of infection condition by antibody measurement will exist greater during periods of high incidence. The combined effect of these biases was likely large through the fall of 2020. In Figure 2, daily seroprevalence stabilized even every bit cumulative incidence rose: each twenty-four hour period some people caused a new detectable antibody result, and others lost detectable antibodies).
Our guess of the reported fraction is higher than estimates from some previous reports. Based on projections from remnant blood donors and clinical samples, the CDC estimated in June 2020 that just 10% of cumulative SARS-CoV-2 infections had been reported [40]. Information technology might exist that the reported fraction has increased every bit testing capacity has increased. Our data ostend that the reported disproportionate bear on on Blackness [41-45] and Hispanic [45-48] people likewise persists in the representative sample, every bit did previously reported associations of college positivity with lower historic period and metropolitan residence [37]. Establishing these associations in a representative report is important because measures of relative impact developed using reported data are impacted by differences in testing availability by race or urbanicity [49]. Others have reported disparities by race, residence and historic period based on diagnosed cases; we found that these disparities are as well observed in a representative sample of Usa respondents corrected for waning, which indicates that these previously reported disparities were not an antiquity of a higher a adventure of symptoms or testing in certain groups. Our data likewise propose that the geographic areas of higher burden have shifted toward the South since earlier in the epidemic [fifty, 51].
Our written report is field of study to limitations. Nosotros used a representative sampling frame, but our response rate was 12.6%, which is low but typical for mailed surveys using accost-based sampling frames [52]. The CDC'south 2 household samples, conducted as a door-to-door offer of enrollment, likewise had low response rates (23.half-dozen%–23.7% [35]). Weighting for nonresponse addresses selection bias for some traits known for households, but residual pick bias exists. Our results are likely subject to differential response bias; nosotros addressed this past oversampling specific groups (eg, Black and Hispanic households) with lower response rates and by weighting for nonresponse of households. We were only able to address differential nonresponse using characteristics of the population that were available to us on the frame (eg, population distributions past race/ethnicity or household income levels). Characteristics that may be associated with COVID-19 risk merely not available at the population level, such as higher general propensity to take risks, were not available for extrapolation to the underlying population and therefore may contribute to uncorrected option bias. Our laboratory results were bailiwick to misclassification based on the latent period for seroconversion and waning antibodies. Unlike most other studies reported to date, nosotros deemed for these biases through our modeling arroyo.
We conducted additional testing to quantify potential biases associated with our choice of an antibody test targeting the NC protein, which is more than subject to waning; the results indicated minimal bias toward misclassifying true antibody-positive tests as negative. We were too at risk for misclassification because DBS cards take less biological material available for use in assays. Equally office of our CLIA validation, DBS vs venipuncture specimens for both serology assays showed 100% sensitivity and specificity for DBS tests compared with a serum gilt standard (north = 30 positives and 30 negatives, unpublished results, available upon request).
Our written report furthers previous seroprevalence surveys by estimating cumulative incidence in a national probability sample of US households, addressing many of the limitations of previous estimates of SARS-CoV-two burden in the United states of america. We found somewhat higher estimates of reported fraction than others, which have ranged from 4%–sixteen% [32, 37]. Our findings suggest substantially higher cumulative incidence than has been reported in previous studies that did non adjust for waning antibodies [53]. A related finding is that our judge of IFR is somewhat lower than had been suggested past studies that did non include waning-adjusted estimates of cumulative incidence (0.85% vs 1.39% [54]); the timing of analyses likely also influenced these differences. Representative population-based samples provide minimally biased data as a contextual framework for other types of studies. Adjusting for waning antibodies is disquisitional to developing credible estimates of cumulative incidence and will get increasingly important over time.
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online. Consisting of information provided by the authors to benefit the reader, the posted materials are non copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
ciab626_suppl_Supplementary_Table_1
Notes
Potential conflicts of interest. Thou. F. reports receiving a consulting fee from Emory Academy exterior the conduct of the report. B. A. Fifty. reports grant back up from the National Science Foundation/Rapid Response Research (2032084); the National Institutes of Health/National Plant of Allergy and Infectious Diseases (NIH/NIAID; R01 AI143875); and the NIH/National Institute of General Medical Sciences (R01 GM124280) during the carry of the report. A. J. S. reports grant support from the NIH/NIAID (3R01AI143875-02S1), the Woodruff Foundation, Centers for Disease Control and Prevention (CK19-1904 (NU50CK000539), National Scientific discipline Foundation (2032084), and the California Department of Public Wellness, paid to their institution, during the carry of the study. P. S. S. reports payments to their institution from NIH during the conduct of the report and reports grant payments (paid to their institution) and consulting fees (paid to them) from the NIH, the Centers for Disease Command and Prevention, and Gilead Sciences outside the submitted work. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript take been disclosed.
References
1. Self WH, Tenforde MW, Stubblefield WB, et al. ; CDC COVID-19 Response Squad; IVY Network. Refuse in SARS-CoV-2 antibodies after mild infection among frontline health care personnel in a multistate infirmary network—12 states, Apr-August 2020. MMWR Morb Mortal Wkly Rep 2020; 69:1762–6. [PMC costless article] [PubMed] [Google Scholar]
2. Dan JM, Mateus J, Kato Y, et al. . Immunological memory to SARS-CoV-2 assessed for up to viii months after infection. Scientific discipline 2021; doi: x.1126/science.abf4063. [PMC free commodity] [PubMed] [Google Scholar]
3. Stadlbauer D, Tan J, Jiang K, et al. . Repeated cross-sectional sero-monitoring of SARS-CoV-ii in New York City. Nature 2021; 590:146–50. [PubMed] [Google Scholar]
5. Fenwick C, Croxatto A, Coste AT, et al. . Changes in SARS-CoV-2 spike versus nucleoprotein antibiotic responses impact the estimates of infections in population-based seroprevalence studies. J Virol 2021; 95. doi: 10.1128/JVI.01828-20. [PMC free commodity] [PubMed] [Google Scholar]
half-dozen. Siegler AJ, Sullivan PS, Sanchez T, et al. . Protocol for a national probability survey using domicile specimen collection methods to assess prevalence and incidence of SARS-CoV-2 infection and antibody response. Ann Epidemiol 2020; 49:fifty–60. [PMC complimentary article] [PubMed] [Google Scholar]
vii. Shioda 1000, Lau MSY, Kraay ANM, et al. . Estimating the cumulative incidence of SARS-CoV-ii infection and the infection fatality ratio in light of waning antibodies. Epidemiology 2021; 32:518–24. [PMC free article] [PubMed] [Google Scholar]
eight. Lavange LM, Kalsbeek WD, Sorlie PD, et al. . Sample design and cohort selection in the Hispanic community health study/study of Latinos. Ann Epidemiol 2010; twenty:642–9. [PMC costless commodity] [PubMed] [Google Scholar]
9. Chido-Amajuoyi OG, Yu RK, Agaku I, Shete Southward. Exposure to courtroom-ordered tobacco industry antismoking advertisements among US adults. JAMA Netw Open 2019; 2:e196935. [PMC free article] [PubMed] [Google Scholar]
10. Cerel J, Maple Thou, van de Venne J, Moore 1000, Flaherty C, Brown M. Exposure to suicide in the customs: prevalence and correlates in one U.S. land. Public Health Rep 2016; 131:100–vii. [PMC free commodity] [PubMed] [Google Scholar]
11. Sullivan PS, Sailey C, Guest JL, et al. . Detection of SARS-CoV-two RNA and antibodies in diverse samples: protocol to validate the sufficiency of provider-observed, home-collected blood, saliva, and oropharyngeal samples. JMIR Public Wellness and Surveillance 2021; 6:e19054. [PMC free article] [PubMed] [Google Scholar]
12. Guest JL, Sullivan PS, Valentine-Graves M, et al. . Suitability and sufficiency of telehealth clinician-observed, participant-collected samples for SARS-CoV-2 testing: the iCollect cohort pilot study. JMIR Public Wellness Surveill 2020; 6:e19731. [PMC free article] [PubMed] [Google Scholar]
14. Sempos CT, Tian L. Adjusting coronavirus prevalence estimates for laboratory examination kit error. Am J Epidemiol 2021; 190:109–fifteen. [PMC free article] [PubMed] [Google Scholar]
15. DiCiccio TJ, Efron B. Bootstrap confidence intervals. SSO Schweiz Monatsschr Zahnheilkd 1996; 11:189–228. [Google Scholar]
16. Andridge RR, Footling RJ. A review of hot deck imputation for survey non-response. Int Stat Rev 2010; 78:40–64. [PMC free article] [PubMed] [Google Scholar]
18. National Academies of Sciences, Engineering, Medicine. Rapid practiced consultations on the COVID-xix pandemic: March 14, 2020–April 8, 2020. National Academies Press, 2020. [Google Scholar]
19. Long QX, Tang XJ, Shi QL, et al. . Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections. Nat Med 2020; 26:1200–four. [PubMed] [Google Scholar]
21. Eberly LE, Casella Thou. Estimating Bayesian credible intervals. J Stat Plan Inference 2003; 112:115–32. [Google Scholar]
22. Dyer O. Covid-xix: US testing ramps up as early on response draws harsh criticism. BMJ 2020; 368:m1167. [PubMed] [Google Scholar]
23. Sood Northward, Simon P, Ebner P, et al. . Seroprevalence of SARS-CoV-2-specific antibodies among adults in Los Angeles Canton, California, on April x–eleven, 2020. JAMA 2020; 323:2425–seven. [PMC gratis article] [PubMed] [Google Scholar]
24. Stokes EK, Zambrano LD, Anderson KN, et al. . Coronavirus disease 2019 case surveillance—United States, January 22–May 30, 2020. MMWR Morb Mortal Wkly Rep 2020; 69:759–65. [PMC free article] [PubMed] [Google Scholar]
25. Killerby ME, Link-Gelles R, Haight SC, et al. ; CDC COVID-nineteen Response Clinical Team. Characteristics associated with hospitalization among patients with COVID-xix - metropolitan Atlanta, Georgia, March–April 2020. MMWR Morb Mortal Wkly Rep 2020; 69:790–four. [PMC free commodity] [PubMed] [Google Scholar]
26. Stephens DS, McElrath MJ. COVID-19 and the path to amnesty. JAMA 2020; 324:1279–81. [PubMed] [Google Scholar]
27. Kalish H, Klumpp-Thomas C, Hunsberger Due south, et al. . Mapping a pandemic: SARS-CoV-ii seropositivity in the U.s.. medRxiv 2021. doi: x.1101/2021.01.27.21250570. [Google Scholar]
28. Lai CC, Wang JH, Hsueh PR. Population-based seroprevalence surveys of anti-SARS-CoV-ii antibody: an upward-to-date review. Int J Infect Dis 2020; 101:314–22. [PMC free article] [PubMed] [Google Scholar]
29. Angulo FJ, Finelli L, Swerdlow DL. Estimation of The states SARS-CoV-ii infections, symptomatic infections, hospitalizations, and deaths using seroprevalence surveys. JAMA Netw Open 2021; 4:e2033706. [PMC gratuitous article] [PubMed] [Google Scholar]
30. Anand Due south, Montez-Rath M, Han J, et al. . Prevalence of SARS-CoV-two antibodies in a large nationwide sample of patients on dialysis in the USA: a cross-sectional report. Lancet 2020; 396:1335–44. [PMC costless article] [PubMed] [Google Scholar]
31. Dodd RY, Xu M, Stramer SL. Change in donor characteristics and antibodies to SARS-CoV-2 in donated claret in the US, June–August 2020. JAMA 2020; doi: x.1001/jama.2020.18598. [PMC complimentary article] [PubMed] [Google Scholar]
32. Havers FP, Reed C, Lim T, et al. . Seroprevalence of antibodies to SARS-CoV-two in 10 sites in the United States, March 23–May 12, 2020. JAMA Intern Med 2020; 180:1576–86. [PubMed] [Google Scholar]
33. Rosenberg ES, Tesoriero JM, Rosenthal EM, et al. . Cumulative incidence and diagnosis of SARS-CoV-ii infection in New York. Ann Epidemiol 2020; 48:23–29.e4. [PMC free article] [PubMed] [Google Scholar]
34. Centers for Disease Control and Prevention. COVID information tracker: nationwide commercial laboratory seroprevalence survey. Available at: https://covid.cdc.gov/covid-information-tracker/#national-lab. Accessed 11 February 2021.
35. Biggs HM, Harris JB, Breakwell Fifty, et al. ; CDC Field Surveyor Squad. Estimated community seroprevalence of SARS-CoV-ii antibodies—two Georgia counties, April 28–May 3, 2020. MMWR Morb Mortal Wkly Rep 2020; 69:965–70. [PMC free commodity] [PubMed] [Google Scholar]
36. Menachemi N, Yiannoutsos CT, Dixon Be, et al. . Population betoken prevalence of SARS-CoV-2 infection based on a statewide random sample—Indiana, April 25–29, 2020. MMWR Morb Mortal Wkly Rep 2020; 69:960–4. [PMC free commodity] [PubMed] [Google Scholar]
37. Bajema KL, Wiegand RE, Cuffe G, et al. . Estimated SARS-CoV-2 seroprevalence in the U.s. equally of September 2020. JAMA Intern Med 2020; doi: x.1001/jamainternmed.2020.7976. [PMC free commodity] [PubMed] [Google Scholar]
38. Perreault J, Tremblay T, Fournier MJ, et al. . Waning of SARS-CoV-two RBD antibodies in longitudinal convalescent plasma samples within four months afterwards symptom onset. Blood 2020; 136:2588–91. [PMC gratuitous article] [PubMed] [Google Scholar]
39. Choe PG, Kang CK, Suh HJ, et al. . Waning antibody responses in asymptomatic and symptomatic SARS-CoV-2 infection. Emerg Infect Dis 2021; doi: 10.3201/eid2701.203515. [PMC free article] [PubMed] [Google Scholar]
41. Millett GA, Jones AT, Benkeser D, et al. . Assessing differential impacts of COVID-19 on black communities. Ann Epidemiol 2020; 47:37–44. [PMC complimentary article] [PubMed] [Google Scholar]
42. Poulson M, Geary A, Annesi C, et al. . National disparities in COVID-19 outcomes between Black and White Americans. J Natl Med Assoc 2020; doi: 10.1016/j.jnma.2020.07.009. [PMC gratuitous article] [PubMed] [Google Scholar]
43. Holtgrave DR, Barranco MA, Tesoriero JM, Blog DS, Rosenberg ES. Assessing racial and ethnic disparities using a COVID-nineteen outcomes continuum for New York State. Ann Epidemiol 2020; 48:9–fourteen. [PMC free article] [PubMed] [Google Scholar]
44. Egede LE, Walker RJ. Structural racism, social run a risk factors, and Covid-xix—a dangerous convergence for black Americans. North Engl J Med 2020; 383:e77. [PMC costless article] [PubMed] [Google Scholar]
45. Moore JT, Ricaldi JN, Rose CE, et al. ; COVID-nineteen State, Tribal, Local, and Territorial Response Squad. Disparities in incidence of COVID-19 among underrepresented racial/ethnic groups in counties identified as hotspots during June 5–eighteen, 2020–22 States, February–June 2020. MMWR Morb Mortal Wkly Rep 2020; 69:1122–6. [PMC free article] [PubMed] [Google Scholar]
46. Rodriguez-Diaz CE, Guilamo-Ramos V, Mena L, et al. . Run a risk for COVID-19 infection and death among Latinos in the United states of america: examining heterogeneity in manual dynamics. Ann Epidemiol 2020; 52:46–53.e2. [PMC free article] [PubMed] [Google Scholar]
47. Macias Gil R, Marcelin JR, Zuniga-Blanco B, Marquez C, Mathew T, Piggott DA. COVID-xix pandemic: disparate health impact on the Hispanic/Latinx population in the United States. J Infect Dis 2020; 222:1592–5. [PMC free article] [PubMed] [Google Scholar]
48. Bui DP, McCaffrey K, Friedrichs K, et al. . Racial and ethnic disparities amid COVID-19 cases in workplace outbreaks by industry sector—Utah, March 6–June v, 2020. MMWR Morb Mortal Wkly Rep 2020; 69:1133–8. [PMC free article] [PubMed] [Google Scholar]
49. Tao R, Downs J, Beckie TM, Chen Y, McNelley W. Examining spatial accessibility to COVID-19 testing sites in Florida. Ann GIS 2020; 26:319–27. [Google Scholar]
l. Oster AM, Kang GJ, Cha AE, et al. . Trends in number and distribution of COVID-xix hotspot counties—United States, March 8–July 15, 2020. MMWR Morb Mort Wkly Rep 2020; 69:1127–32. [PMC gratuitous article] [PubMed] [Google Scholar]
51. Oster AM, Caruso Due east, DeVies J, Hartnett KP, Boehmer TK. Transmission dynamics past historic period group in COVID-xix hotspot counties—United states, April–September 2020. MMWR Morb Mortal Wkly Rep 2020; 69:1494–vi. [PMC free commodity] [PubMed] [Google Scholar]
52. Fahimi M, Link K, Schwartz DA, Levy P, Mokdad A. Tracking chronic illness and run a risk behavior prevalence as survey participation declines: statistics from the behavioral risk cistron surveillance system and other national surveys. Prev Chronic Dis 2008:07_0097a. [PMC free article] [PubMed] [Google Scholar]
53. O'Driscoll 1000, Ribeiro Dos Santos G, Wang L, et al. . Age-specific mortality and amnesty patterns of SARS-CoV-2. Nature 2021; 590:140–5. [PubMed] [Google Scholar]
54. Yang Due west, Kandula S, Huynh M, et al. . Estimating the infection-fatality risk of SARS-CoV-ii in New York Urban center during the spring 2020 pandemic wave: a model-based analysis. Lancet Infect Dis 2021; 21:203–12. [PMC free article] [PubMed] [Google Scholar]
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