Comparison of humoral responses in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccinees, those with SARS-CoV-2 infection, or combinations of vaccine/ infection (“hybrid immunity”) may clarify predictors of vaccine immunogenicity. We studied 2660 US Military Health System beneficiaries with a history of SARS-CoV-2 infection-alone (n = 705), vaccination-alone (n = 932), vaccine-after-infection (n = 869), and vaccine-breakthrough-infection (n = 154). Peak anti-spike–immunoglobulin G (IgG) responses through 183 days were compared, with adjustment for vaccine product, demography, and comorbidities. We excluded those with evidence of clinical or subclinical SARS-CoV-2 reinfection from all groups. Multivariable regression results indicated that vaccine-after-infection anti-spike–IgG responses were higher than infection-alone (P < .01), regardless of prior infection severity. An increased time between infection and vaccination was associated with greater post-vaccination IgG response (P < .01). Vaccination-alone elicited a greater IgG response but more rapid waning of IgG (P < .01) compared with infection-alone (P < .01). BNT162b2 and mRNA-1273 vaccine-receipt was associated with greater IgG responses compared with JNJ-78436735 vaccine-receipt (P < .01), regardless of infection history. Those with vaccine-after-infection or vaccine-breakthrough-infection had a more durable anti-spike–IgG response compared to infection-alone (P < .01). Vaccine-receipt elicited higher anti-spike–IgG responses than infection-alone, although IgG levels waned faster in those vaccinated (compared to infection-alone). Vaccine-after-infection elicits a greater humoral response compared with vaccine or infection alone; and the timing, but not disease severity, of prior infection predicted these post-vaccination IgG responses. While differences between groups were small in magnitude, these results offer insights into vaccine immunogenicity variations that may help inform vaccination timing strategies.

Successful management of an event where health-care needs exceed regional health-care capacity requires coordinated strategies for scarce resource allocation. Publications for rapid development, training, and coordination of regional hospital triage teams to manage the allocation of scarce resources during coronavirus disease 2019 (COVID-19) are lacking. Over a period of 3 weeks, over 100 clinicians, ethicists, leaders, and public health authorities convened virtually to achieve consensus on how best to save the most lives possible and share resources. This is referred to as population-based crisis management. The rapid regionalization of 22 acute care hospitals across 4500 square miles in the midst of a pandemic with a shifting regulatory landscape was challenging, but overcome by mutual trust, transparency, and confidence in the public health authority. Because many cities are facing COVID-19 surges, we share a process for successful rapid formation of health-care care coalitions, Crisis Standard of Care, and training of Triage Teams. Incorporation of continuous process improvement and methods for communication is essential for successful implementation. Use of our regional health-care coalition communications, incident command system, and the crisis care committee helped mitigate crisis care in the San Diego and Imperial County region as COVID-19 cases surged and scarce resource collaborative decisions were required.

Background: Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles. Methods: 1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations. Results: We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 (“Nasal cluster”) is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 (“Sensory cluster”) is highly correlated with loss of smell or taste, and cluster 3 (“Respiratory/Systemic cluster”) is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates ( P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset ( P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster ( P < 0.01). Conclusions: We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.