RESUMO
After the implementation of broad vaccination programs, there is an urgent need to understand how the population immunity affects the dynamics of the COVID-19 pandemic in presence of the protection waning and of the emergence of new vari-ants of concern. In the current Omicron wave that is propagating across Europe, assessing the risk of saturation of the healthcare systems is crucial for pandemic management, as it allows us to support the transition towards the endemic course of SARS-CoV-2 and implement more refined mitigation strategies that shield the most vulnerable groups and protect the healthcare systems. We investigated the current pandemic dynamics by means of compartmental models that describe the age-stratified social-mixing, and consider vaccination status, vaccine types, and their waning efficacy. Our goal is to provide insight into the plausible scenarios that are likely to be seen in Switzerland and Germany in the coming weeks and help take informed decisions. Despite the huge numbers of new positive cases, our results suggest that the current wave is unlikely to create an overwhelming health-care demand: owing to the lower hospitalization rate of the novel variant and the effectiveness of the vaccines. Our findings are robust with respect to the plausible variability of the main parameters that govern the severity and the progression of the Omicron infection. In a broader context, our framework can be applied also to future endemic scenarios, offering quantitative support for refined public health interventions in response to recurring COVID-19 waves.
RESUMO
Coronavirus Disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. We developed three different protein arrays to measure hallmark IgG autoantibodies associated with Connective Tissue Diseases (CTDs), Anti-Cytokine Antibodies (ACA), and anti-viral antibody responses in 147 hospitalized COVID-19 patients in three different centers. Autoantibodies were identified in approximately 50% of patients, but in <15% of healthy controls. When present, autoantibodies largely targeted autoantigens associated with rare disorders such as myositis, systemic sclerosis and CTD overlap syndromes. Anti-nuclear antibodies (ANA) were observed in [~]25% of patients. Patients with autoantibodies tended to demonstrate one or a few specificities whereas ACA were even more prevalent, and patients often had antibodies to multiple cytokines. Rare patients were identified with IgG antibodies against angiotensin converting enzyme-2 (ACE-2). A subset of autoantibodies and ACA developed de novo following SARS-CoV-2 infection while others were transient. Autoantibodies tracked with longitudinal development of IgG antibodies that recognized SARS-CoV-2 structural proteins such as S1, S2, M, N and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. COVID-19 patients with one or more autoantibodies tended to have higher levels of antibodies against SARS-CoV-2 Nonstructural Protein 1 (NSP1) and Methyltransferase (ME). We conclude that SARS-CoV-2 causes development of new-onset IgG autoantibodies in a significant proportion of hospitalized COVID-19 patients and are positively correlated with immune responses to SARS-CoV-2 proteins.
RESUMO
Several tests based on chemiluminescence immunoassay techniques have become available to test for SARS-CoV-2 antibodies. There is currently insufficient data on serology assay performance beyond 35 days after symptoms onset. We aimed to evaluate SARS-CoV-2 antibody tests on three widely used platforms. A chemiluminescent microparticle immunoassay (CMIA; Abbott Diagnostics, USA), a luminescence immunoassay (LIA; Diasorin, Italy), and an electrochemiluminescence immunoassay (ECLIA; Roche Diagnostics, Switzerland) were investigated. In a multi-group study, sensitivity was assessed in a group of participants with confirmed SARS-CoV-2 (n=145), whereas specificity was determined in two groups of participants without evidence of COVID-19 (i.e. healthy blood donors, n=191, and healthcare workers, n=1002). Receiver operating characteristic (ROC) curves, multilevel likelihood ratios (LR), and positive (PPV) and negative (NPV) predictive values were characterized. Finally, analytical specificity was characterized in samples with evidence of Epstein-Barr virus (EBV) (n=9), cytomegalovirus (CMV) (n=7) and endemic common cold coronavirus infections (n=12) taken prior to the current SARS-CoV-2 pandemic. The diagnostic accuracy was comparable in all three assays (AUC 0.98). Using the manufacturers cut-offs, the sensitivities were 90%, 95% confidence interval,[84,94] (LIA), 93% [88,96] (CMIA), and 96% [91,98] (ECLIA). The specificities were 99.5% [98.9,99.8](CMIA) 99.7% [99.3,99,9] (LIA) and 99.9% [99.5,99.98] (ECLIA). The LR at half of the manufacturers cut-offs were 60 (CMIA), 82 (LIA), and 575 (ECLIA) for positive and 0.043 (CMIA) and 0.035 (LIA, ECLIA) for negative results. ECLIA had higher PPV at low pretest probabilities than CMIA and LIA. No interference with EBV or CMV infection was observed, whereas endemic coronavirus in some cases provided signals in LIA and/or CMIA. Although the diagnostic accuracy of the three investigated assays is comparable, their performance in low-prevalence settings is different. Introducing gray zones at half of the manufacturers cut-offs is suggested, especially for orthogonal testing approaches that use a second assay for confirmation.
RESUMO
Knowledge of the sensitivities of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antibody tests beyond 35 days after the clinical onset of COVID-19 is insufficient. We aimed to describe positivity rate of SARS-CoV-2 assays employing three different measurement principles over a prolonged period. Two hundred sixty-eight samples from 180 symptomatic patients with COVID-19 and a reverse transcription polymerase chain reaction (RT-PCR) test followed by serological investigation of SARS-CoV-2 antibodies were included.. We conducted three chemiluminescence (including electrochemiluminscence, ECLIA), four enzyme linked immunosorbent assay (ELISA), and one lateral flow immunoassay (LFIA) test formats. Positivity rates, as well as positive (PPV) and negative predictive values (NPV) were calculated for each week after the first clinical presentation for COVID-19. Furthermore, combinations of tests were assessed within an orthogonal testing approach employing two independent assays and predictive values were calculated. Heat maps were constructed to graphically illustrate operational test characteristics. During a follow-up period of more than 9 weeks, chemiluminescence assays and one ELISA IgG test showed stable positivity rates after the third week. With the exception of ECLIA, the PPVs of the other chemiluminescence assays were [≥]95% for COVID-19 only after the second week. ELISA and LFIA had somewhat lower PPVs. IgM exhibited insufficient predictive characteristics. An orthogonal testing approach provided PPVs [≥]95% for patients with a moderate pretest probability (e.g., symptomatic patients), even for tests with a low single test performance. After the second week, NPVs of all but IgM assays were [≥]95% for patients with low to moderate pretest probability. The confirmation of negative results using an orthogonal algorithm with another assay provided lower NPVs than the single assays. When interpreting results from SARS-CoV-2 tests, the pretest probability, time of blood draw and assay characteristics must be carefully considered. An orthogonal testing approach increases the accuracy of positive, but not negative, predictions.
