Using SARS-CoV-2 Sequencing Data to Identify Reinfection Cases in the Global Emerging Infections Surveillance Program, United States
Deanna Muehleman, Bill Gruner, Vivian Hogan, Padraic Fanning, Carol Garrett, Jennifer Meyer, Kelsey Lanter, Sarah Purves, Laurie DeMarcus, Jeffrey Thervil, Bismark Kwaah, Paul Sjoberg, Elizabeth Macias, and Anthony Fries
Author affiliation: US Air Force School of Aerospace Medicine and Defense Centers for Public Health, Dayton, Ohio, USA (D. Muehleman, B. Gruner, V. Hogan, P. Fanning, C. Garrett, J. Meyer, K. Lanter, S. Purves, L. Demarcus, J. Thervil, B. Kwaah, P. Sjoberg, E. Macias, A. Fries); JYG Innovations, Dayton (D. Muehleman, B. Gruner, S. Purves); Henry Jackson Foundation, Rockville, MD, USA (V. Hogan); ERP360 Solutions Group LLC, Washington, DC, USA (P. Fanning, J. Meyer, K. Lanter); Innovative Element LLC, Washington (L. Demarcus, J. Thervil, B. Kwaah, P. Sjoberg)
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Figure 3
Figure 3. Reinfection symptom severity in study using SARS-CoV-2 sequencing data to identify reinfection cases in Department of Defense Global Respiratory Pathogen Surveillance Program, United States. Proportions of reinfections with different symptom severity at the second specimen collection timepoint are compared with the first specimen collection date. Symptom severity was assigned numeric values: 0, asymptomatic; 1, mild; 2, moderate; and 3, severe. Numbers along data line indicate the average infection symptom severity (top number) and number of reinfections (bottom number). Reinfection symptom severity correlated with symptom severity during the first infection. Relationships were determined by linear regression; adjusted p value = 0.0131, adjusted for sex and age.
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