Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January–March 2022)

In January 2022, after the implementation of broad vaccination programs, the Omicron wave was propagating across Europe. There was an urgent need to understand how population immunity affects the dynamics of the COVID-19 pandemic when the loss of vaccine protection was concurrent with the emergence of a new variant of concern. In particular, assessing the risk of saturation of the healthcare systems was crucial to manage the pandemic and allow a transition towards the endemic course of SARS-CoV-2 by implementing more refined mitigation strategies that shield the most vulnerable groups and protect the healthcare systems. We investigated the epidemic dynamics by means of compartmental models that describe the age-stratified social-mixing and consider vaccination status, type, and waning of the efficacy. In response to the acute situation, our model aimed at (i) providing insight into the plausible scenarios that were likely to occur in Switzerland and Germany in the midst of the Omicron wave, (ii) informing public health authorities, and (iii) helping take informed decisions to minimize negative consequences of the pandemic. Despite the unprecedented numbers of new positive cases, our results suggested that, in all plausible scenarios, the wave was unlikely to create an overwhelming healthcare demand;due to the lower hospitalization rate and the effectiveness of the vaccines in preventing a severe course of the disease. This prediction came true and the healthcare systems in Switzerland and Germany were not pushed to the limit, despite the unprecedentedly large number of infections. By retrospective comparison of the model predictions with the official reported data of the epidemic dynamic, we demonstrate the ability of the model to capture the main features of the epidemic dynamic and the corresponding healthcare demand. In a broader context, our framework can be applied also to endemic scenarios, offering quantitative support for refined public health interventions in response to recurring waves of COVID-19 or other infectious diseases.

Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January-March 2022).

In January 2022, after the implementation of broad vaccination programs, the Omicron wave was propagating across Europe. There was an urgent need to understand how population immunity affects the dynamics of the COVID-19 pandemic when the loss of vaccine protection was concurrent with the emergence of a new variant of concern. In particular, assessing the risk of saturation of the healthcare systems was crucial to manage the pandemic and allow a transition towards the endemic course of SARS-CoV-2 by implementing more refined mitigation strategies that shield the most vulnerable groups and protect the healthcare systems. We investigated the epidemic dynamics by means of compartmental models that describe the age-stratified social-mixing and consider vaccination status, type, and waning of the efficacy. In response to the acute situation, our model aimed at (i) providing insight into the plausible scenarios that were likely to occur in Switzerland and Germany in the midst of the Omicron wave, (ii) informing public health authorities, and (iii) helping take informed decisions to minimize negative consequences of the pandemic. Despite the unprecedented numbers of new positive cases, our results suggested that, in all plausible scenarios, the wave was unlikely to create an overwhelming healthcare demand; due to the lower hospitalization rate and the effectiveness of the vaccines in preventing a severe course of the disease. This prediction came true and the healthcare systems in Switzerland and Germany were not pushed to the limit, despite the unprecedentedly large number of infections. By retrospective comparison of the model predictions with the official reported data of the epidemic dynamic, we demonstrate the ability of the model to capture the main features of the epidemic dynamic and the corresponding healthcare demand. In a broader context, our framework can be applied also to endemic scenarios, offering quantitative support for refined public health interventions in response to recurring waves of COVID-19 or other infectious diseases.

Performance of point-of care molecular and antigen-based tests for SARS-CoV-2: a living systematic review and meta-analysis.

BACKGROUND: Molecular and antigen point-of-care tests (POCTs) have augmented our ability to rapidly identify and manage SARS-CoV-2 infection. However, their clinical performance varies among individual studies. OBJECTIVES: The evaluation of the performance of molecular and antigen-based POCTs in confirmed, suspected, or probable COVID-19 cases compared with that of laboratory-based RT-PCR in real-life settings. DATA SOURCES: MEDLINE/PubMed, Scopus, Embase, Web of Science, Cochrane Library, Cochrane COVID-19 study register, and COVID-19 Living Evidence Database from the University of Bern. STUDY ELIGIBILITY CRITERIA: Peer-reviewed or preprint observational studies or randomized controlled trials that evaluated any type of commercially available antigen and/or molecular POCTs for SARS-CoV-2, including multiplex PCR panels, approved by the United States Food and Drug Administration, with Emergency Use Authorization, and/or marked with Conformitè Europëenne from European Commission/European Union. PARTICIPANTS: Close contacts and/or patients with symptomatic and/or asymptomatic confirmed, suspected, or probable COVID-19 infection of any age. TEST/S: Molecular and/or antigen-based SARS-CoV-2 POCTs. REFERENCE STANDARD: Laboratory-based SARS-CoV-2 RT-PCR. ASSESSMENT OF RISK OF BIAS: Eligible studies were subjected to quality-control and risk-of-bias assessment using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. METHODS OF DATA SYNTHESIS: Summary sensitivities and specificities with their 95% CIs were estimated using a bivariate model. Subgroup analysis was performed when at least three studies informed the outcome. RESULTS: A total of 123 eligible publications (97 and 26 studies assessing antigen-based and molecular POCTs, respectively) were retrieved from 4674 initial records. The pooled sensitivity and specificity for 13 molecular-based POCTs were 92.8% (95% CI, 88.9-95.4%) and 97.6% (95% CI, 96.6-98.3%), respectively. The sensitivity of antigen-based POCTs pooled from 138 individual evaluations was considerably lower than that of molecular POCTs; the pooled sensitivity and specificity rates were 70.6% (95% CI, 67.2-73.8%) and 98.9% (95% CI, 98.5-99.2%), respectively. DISCUSSION: Further studies are needed to evaluate the performance of molecular and antigen-based POCTs in underrepresented patient subgroups and different respiratory samples.

Comorbidity defines asthmatic patients' risk of COVID-19 hospitalization: A global perspective.

BACKGROUND: The global epidemiology of asthma among patients with coronavirus disease 2019 (COVID-19) presents striking geographic differences, defining prevalence zones of high and low co-occurrence of asthma and COVID-19. OBJECTIVE: We aimed to compare asthma prevalence among hospitalized patients with COVID-19 in major global hubs across the world by applying common inclusion criteria and definitions. METHODS: We built a network of 6 academic hospitals in Stanford (Stanford University)/the United States; Frankfurt (Goethe University), Giessen (Justus Liebig University), and Marburg (Philipps University)/Germany; and Moscow (Clinical Hospital 52 in collaboration with Sechenov University)/Russia. We collected clinical and laboratory data for patients hospitalized due to COVID-19. RESULTS: Asthmatic individuals were overrepresented among hospitalized patients with COVID-19 in Stanford and underrepresented in Moscow and Germany as compared with their prevalence among adults in the local community. Asthma prevalence was similar among patients hospitalized in an intensive care unit and patients hospitalized in other than an intensive care unit, which implied that the risk for development of severe COVID-19 was not higher among asthmatic patients. The numbers of males and comorbidities were higher among patients with COVID-19 in the Stanford cohort, and the most frequent comorbidities among these patients with asthma were other chronic inflammatory airway disorders such as chronic obstructive pulmonary disease. CONCLUSION: The observed disparity in COVID-19-associated risk among asthmatic patients across countries and continents is connected to the varying prevalence of underlying comorbidities, particularly chronic obstructive pulmonary disease.

Model Selection in the World of Maximum Entropy

Science aims at identifying suitable models that best describe a population based on a set of features. Lacking information about the relationships among features there is no justification to a priori fix a certain model. Ideally, we want to incorporate only those relationships into the model which are supported by observed data. To achieve this goal the model that best balances goodness of fit with simplicity should be selected. However, parametric approaches to model selection encounter difficulties pertaining to the precise definition of the invariant content that enters the selection procedure and its interpretation. A naturally invariant formulation of any statistical model consists of the joint distribution of features, which provides all the information that is required to answer questions in classification tasks or identification of feature relationships. The principle of Maximum Entropy (maxent) offers a framework to directly estimate a model for this joint distribution based on phenomenological constraints. Reformulating the inverse problem to obtain a model distribution as an under-constrained linear system of equations, where the remaining degrees of freedom are fixed by entropy maximization, tremendously simplifies large-N expansions around the optimal distribution of Maximum Entropy. We have exploited this conceptual advancement to clarify the nature of prominent model-selection schemes providing an approach to systematically select significant constraints evidenced by the data. To facilitate the treatment of higher-dimensional problems, we propose hypermaxent - a clustering method to efficiently tackle the maxent selection procedure. We demonstrate the utility of our approach by applying the advocated methodology to analyze long-range interactions from spin glasses and uncover three-point effects in COVID-19 data.

New-onset IgG autoantibodies in hospitalized patients with COVID-19.

COVID-19 is associated with a wide range of clinical manifestations, including autoimmune features and autoantibody production. Here we develop three protein arrays to measure IgG autoantibodies associated with connective tissue diseases, anti-cytokine antibodies, and anti-viral antibody responses in serum from 147 hospitalized COVID-19 patients. Autoantibodies are identified in approximately 50% of patients but in less than 15% of healthy controls. When present, autoantibodies largely target autoantigens associated with rare disorders such as myositis, systemic sclerosis and overlap syndromes. A subset of autoantibodies targeting traditional autoantigens or cytokines develop de novo following SARS-CoV-2 infection. Autoantibodies track with longitudinal development of IgG antibodies recognizing SARS-CoV-2 structural proteins and a subset of non-structural proteins, but not proteins from influenza, seasonal coronaviruses or other pathogenic viruses. 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.