COVID-19 Science Update released: September 22, 2020

COVID-19 Science Update

From the Office of the Chief Medical Officer, CDC COVID-19 Response, and the CDC Library, Atlanta GA. Intended for use by public health professionals responding to the COVID-19 pandemic.

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COVID-19 Science Updates

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Substantial underestimation of SARS-CoV-2 infection in the United Statesexternal icon. Wu et al. Nature Communications (September 9, 2020).

Key findings:

  • By April 18, 2020, the estimated cumulative SARS-CoV-2 incidence in the US was ~2%.
    • The number of estimated cumulative SARS-CoV-2 infections was 8.6 times the number of confirmed infections: 6,454,951 vs 751,245.
    • 84% of the difference between estimated cumulative and reported confirmed cases was due to incomplete testing and 16% was due to test inaccuracy.
  • The estimated cumulative infection rate (range: 3.1 to 65.0/1,000) and ratio of estimated cumulative to reported confirmed SARS-CoV-2 infections (range: 5 to 33) varied widely by state (Figure).
    • Differences among states were driven by different transmission rates, testing rates, and test positivity rates in each state rather than modeling assumptions.

Methods: Analysis using data from the COVID Tracking Projectexternal icon to assess estimated cumulative SARS-CoV-2 infections by state and evaluate contributions of incomplete testing and imperfect test performance. Used daily test counts and confirmed SARS-CoV-2 positive tests in each state from February 28 to April 18, 2020. The 2019 projected state populations from the 2010 US Census were used to calculate rates. Simulation intervals (2.5th and 97.5th percentiles) were computed from the distribution of estimated infections. Limitations: States with very low testing rates; positive test probabilities might not predict overall population incidence (e.g., if testing was restricted to patients with severe symptoms); county-level data not available.

Implications: Estimated cumulative SARS-CoV-2 infections were greater than confirmed reported infections, due in part possibly to challenges with testing. Monitoring underestimation of reported confirmed cases can provide more accurate estimates of the cumulative burden of SARS-CoV-2 infection.




Note: From Wu et al. A: Gray bars indicate the median rate of estimated infections. B: Ratios of estimated infections to confirmed cases in each state by quintile in descending order, with the darkest shade of blue indicating the largest quintile, and the lightest shade of green indicating the lowest quintile. Horizontal black lines indicate simulation intervals (2.5th and 97.5th percentiles). Licensed under CC-BY 4.0.

Assessment of mental health of Chinese primary school students before and after school closing and opening during the COVID-19 pandemic.external icon Zhang et al. JAMA Network (September 11, 2020).

Key findings:

  • The prevalence of depression and suicidality increased significantly among students after school closing (Wave 2) from levels before school closing (Wave 1) (Figure).

Methods: Longitudinal cohort study of 1,241 Chinese schoolchildren in grades 4 through 8 comparing physical and mental health factors before the COVID-19 outbreak (Wave 1, early November 2019) with 2 weeks after school reopening (Wave 2, mid-May 2020) in a low risk area of China. Limitations: Response and recall bias; unmeasured confounders and measurement errors in mental health outcomes; limited representativeness of the sample.

Implications: School closures adversely affected mental health of students. Healthcare and government agencies need to plan for the impact of prolonged school closures and be prepared to provide increased level of mental health services to the children and their families.




Note: Adapted from Zhang et al. Incidence of mental health symptoms and suicide behaviors in Wave 1 before school closures and Wave 2 after school reopening. a p <0.001; b p = 0.09. Licensed under CC-BY.

Convalescent Plasma

Convalescent plasma (CP) therapy is under evaluation as treatment of COVID-19 and is obtained from persons who have recovered from prior COVID-19. However, not all persons develop the same antibody profile or adequate neutralizing antibody (NAb) titers after SARS-CoV-2 infection. There is a need to determine levels of NAbs in CP that are optimal for treatment and prevention. As assays to measure neutralization are complex, determining if other antibody titers such as those to the receptor binding domain (RBD) or the spike protein may be used as a marker of high neutralizing activity is important. The following two studies present recent findings on characteristics of CP.


A. Convalescent plasma anti-SARS-CoV-2 spike protein ectodomain and receptor binding domain IgG correlate with virus neutralizationexternal icon. Salazar et al. Journal of Clinical Investigation (September 10, 2020).

Key findings:

  • Most CP donors (43/68; 63%) had high NAb titers (≥1:160).
    • High NAb titers were associated with dyspnea, hospitalization, and severity at the time of illness in the donor (Figure 1).
  • Anti-RBD antibodies and anti-spike ectodomain (ECD) antibodies were strongly correlated with NAbs (p <0.001 for each).
  • 80% of CP donors had NAb titer ≥1:160 when their anti-RBD or anti-spike ectodomain (ECD) titer (part of the spike protein) was ≥1:1,350 (Figure 2).
    • In a separate survey of antibodies levels in 73 asymptomatically infected persons, 27 had anti-RBD or anti-spike ECD titers of ≥1:1,350.

Methods: Retrospective assessment of anti-ECD IgG, anti-RBD IgG, and SARS-CoV-2 NAb titers from 68 CP donors who had symptomatic SARS-CoV-2 infection. Dyspnea, hospitalization, and a severity score at the time of illness were recorded. IgG titers were also measured in a separate sample of 73 asymptomatic, seropositive individuals. Limitations: Assessed IgG only; findings may not be applicable to all antibody testing platforms or other sample types; relatively small sample size.

Figure 1

Note: Adapted from Salazar et al. Boxplots of interquartile range, showing the log2 transformed antibody neutralization titer by category in 68 CP donors. Black dots are outliers, whisker bars are upper and lower quartiles. Severity- highest severity score during illness. Open access journal; all content freely available.

Note: Adapted from Salazar et al. Prevalence of CP donors with NAb titer ≥1:160 by IgG titers of anti-spike ECD and anti-RBD. Dashed line is a curve fitted to probability values. Standard error bars are shown. Open access journal; all content freely available.

SARS-CoV-2 antibody avidity responses in COVID-19 patients and convalescent plasma donorsexternal icon. Benner et al. Journal of Infectious Diseases (September 10, 2020).

Key findings:

  • Among hospitalized patients, antibody avidity as well as anti-spike and anti-nucleocapsid IgG titers increased from days 10 to 20 after symptom onset (Figure 1).
  • Among CP donors, age correlated with antibody avidity (strength of binding between antibody and antigen) for men (p = 0.008) but not women (p = 0.872) (Figure 2A).
  • CP donors who were hospitalized had stronger anti-spike IgG avidity than donors who were not hospitalized (Figure 2B).
  • Neutralizing antibody titers had a positive correlation with anti-spike IgG avidity (Spearman’s rho = 0.386; p <0.001) and anti-spike IgG binding (Spearman’s rho = 772, p <0.001).

Methods: Samples from hospitalized patients (n = 16) with confirmed SARS-CoV-2 infection and CP donors (n = 130) were assessed for anti-spike IgG, anti-nucleocapsid IgG titers, and antibody avidity. Limitations: Different methods used for anti-spike IgG versus anti-nucleocapsid IgG antibody titers; antibody assays were semi-quantitative; may not be applicable to mild or asymptomatic infections; short observation period.

Figure 1

Note: Adapted from Benner et al. A: IgG antibody levels. B: Antibody avidity against the SARS-CoV-2 spike protein in hospitalized patients. DC50 – 50% dissociation constant. Colored lines indicate individual patients. Because assays used were semi-quantitative, units used were proxies for quantitative measures. Licensed under CC BY 4.0.

Figure 2

Note: Adapted from Benner et al. Cross-sectional sampling of recovered patients by age tested for IgG antibody avidity against the SARS-CoV-2 spike protein by age (A) and hospitalization status (B). DC50 – 50% dissociation constant. Licensed under CC BY 4.0.

Implications for 2 studies (Salazar et al & Benner et al): Effect of CP in the treatment of COVID-19 likely depends on numerous characteristics including antibody level, avidity, and target as well as neutralization activity. Understanding how to best screen CP donors to identify those with NAb titers and characteristics that may optimize use of CP as treatment may be important.

Clinical Treatment & Management


Reduced maximal aerobic capacity after COVID-19 in young adult recruits, Switzerland, May 2020.external icon Crameri et al. Eurosurveillance (September 10, 2020).

Key findings:

  • The VO2 max (a measure of ability to perform aerobic exercise) was lower in recovered recruits with symptomatic COVID-19 compared with recruits who had not had COVID-19 (p = 0.02).
  • In recovered recruits, there was a significant decrease in VO2 max compared with a baseline measure obtained prior to illness (Figure).
    • 19% of recruits with symptomatic COVID-19 had a loss of >10% VO2

Methods: Cross-sectional study of 199 Swiss Army recruits tested by RT-PCR in May 2020 during a COVID-19 outbreak and divided into 3 groups: RT-PCR positive, symptomatic, RT-PCR positive, asymptomatic, and RT-PCR negative. Endurance and aerobic capacity, including VO2 max, were measured in all recruits at baseline (coincidentally 3-months prior to COVID-19 outbreak). Complete data at baseline and after the COVID-19 outbreak were available for 139 (70%) of the recruits and assessed for differences. Limitations: Timing of testing after the outbreak was unclear.

Implications: A decrease in pulmonary aerobic capacity was observed among military recruits who recovered from COVID-19. Long-term effects on lung function have been noted after mild to moderate influenza infection and may also be present after COVID-19. Additional research to understand the incidence of any long-term consequences is needed.


Note: Adapted from Crameri et al. Difference in predicted maximal aerobic capacity before and after COVID-19 outbreak (n = 139). Licensed under CC BY 4.0

Effect of recombinant human granulocyte colony–stimulating factor for patients with coronavirus disease 2019 (COVID-19) and lymphopenia: A randomized clinical trialexternal icon.external icon Cheng et al. JAMA Internal Medicine (September 10, 2020).

Key findings:

  • Treatment of COVID-19 patients experiencing lymphopenia (lower than normal white blood cell levels) with recombinant human granulocyte colony-stimulating factor (rhG-CSF) to promote production of lymphocytes did not affect time to clinical improvement (12 with lymphopenia vs 13 days without, p = 0.06).
    • Among patients with peripheral blood lymphocytes ≤400 per µL, rhG-CSF treatment reduced time to clinical improvement) compared with the usual care group (12 vs 14 days respectively, p = 0.003) (Figure).
  • The rhG-CSF treatment group were less likely to progress to critical illness (2% vs 15%, difference -13%) and had lower mortality rates (2% vs 10%, hazard ratio 0.19) at Day 21.

Methods: An open-label, randomized clinical trial at three sites in China between February 18 and April 10, 2020 testing the effects of treatment of PCR-confirmed COVID-19 patients with rhG-CSF (N = 100) on days 0, 1, and 2 vs usual care (N = 100). Eligibility requirements were pneumonia, a blood lymphocyte cell count of 800 per μL or lower, and no comorbidities. Time to clinical improvement, progression to critical conditions and mortality was measured. Limitations: Small size and short observational time frame; exclusion of patients with co-morbidities.

Implications: rhG-CSF appears to prevent progression to severe disease and death in COVID-19 patients with lymphocytopenia ≤400 lymphocytes/μL; larger studies with broader patient inclusion are needed.



Note: Adapted from Cheng et al. Improvement in patients with peripheral blood lymphocyte counts of ≤400/µL (A) or >401-800/µL (B). Reproduced with permission from JAMA Intern Med. doi: 10.1001/jamainternmed.2020.5503. Copyright©2020 American Medical Association. All rights reserved.

Phylogenetic Analysis


The emergence of SARS-CoV-2 in Europe and North Americaexternal icon.external icon Worobey et al. Science (September 10, 2020).

Key findings:

  • Epidemic simulations suggest multiple independent entries of SARS-CoV-2 into the US occurred (Figure 1).
    • Models could not identify a scenario in which the viral sequence of the Washington State outbreak beginning February 15, 2020 could have derived from the first known US SARS-CoV-2 case (January 15, 2020).
  • Phylogenetic reconstruction also suggests independent viral introduction from China to both Germany and Italy (Figure 2).

Methods: Using 294 SARS-CoV-2 viral genomes from Washington State collected from January 15 to March 15, 2020 through community surveillance for influenza, epidemic simulations were performed to model emergence of mutations of SARS-CoV-2 genome in the US. To determine if Italy’s outbreak was initiated by a virus imported from the German outbreak, additional phylogeographic analyses were conducted. Limitations: Constraints placed on doubling time; genetic sequence data not available from all countries.

Implications: This study highlights that community-level respiratory virus surveillance combined with genomic analyses can be a useful tool to help distinguish sustained community transmission vs importation of new strains, which helps identify origin of clusters, delineate time and place of outbreak origins and define optimal mitigation measures for potential future outbreaks.

Figure 1

Note: Adapted from Worobey et. al. Phylogenetic tree depicting the evolutionary relationships inferred between WA1 (the first identified US COVID-19 case, red arrow), the clade associated with the Washington State outbreak and related viruses (gray box) and closely related viruses identified in Asia. Dotted lines represent branches with unsampled taxa. Numbers next to each node represent the level of support for that node using posterior probabilities, with 1 being the maximum. Licensed under CC BY 4.0.

Figure 2

Note: Adapted from Worobey et. al. SARS-CoV-2 entry into Europe. A subtree for viruses from the first outbreak in Europe (Germany, bavpat), identical viruses from China, outbreaks in Italy and New York, and other locations in Europe. Dotted lines represent branches with unsampled taxa. Numbers next to each node represent the level of support for that node using posterior probabilities, with 1 being the maximum. Licensed under CC BY 4.0.

In Brief


COVID-19 and Influenza


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Disclaimer: The purpose of the CDC COVID-19 Science Update is to share public health articles with public health agencies and departments for informational and educational purposes. Materials listed in this Science Update are selected to provide awareness of relevant public health literature. A material’s inclusion and the material itself provided here in full or in part, does not necessarily represent the views of the U.S. Department of Health and Human Services or the CDC, nor does it necessarily imply endorsement of methods or findings. While much of the COVID-19 literature is open access or otherwise freely available, it is the responsibility of the third-party user to determine whether any intellectual property rights govern the use of materials in this Science Update prior to use or distribution. Findings are based on research available at the time of this publication and may be subject to change.


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Page last reviewed: September 23, 2020
Content source: Office of the Chief Science Officer - COVID-19