Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Diagnostic performance of four SARS-CoV-2 antibody assays in patients with COVID-19 or with bacterial and non-SARS-CoV-2 viral respiratory infections.

SARS-CoV-2 antibody assays are used for epidemiological studies and for the assessment of vaccine responses in highly vulnerable patients. So far, data on cross-reactivity of SARS-CoV-2 antibody assays is limited. Here, we compared four enzyme-linked immunosorbent assays (ELISAs; Vircell SARS-CoV-2 IgM/IgA and IgG, Euroimmun SARS-CoV-2 IgA and IgG) for detection of anti-SARS-CoV-2 antibodies in 207 patients with COVID-19, 178 patients with serological evidence of different bacterial infections, 107 patients with confirmed viral respiratory disease, and 80 controls from the pre-COVID-19 era. In COVID-19 patients, the assays showed highest sensitivity in week 3 (Vircell-IgM/A and Euroimmun-IgA: 78.9% each) and after week 7 (Vircell-IgG: 97.9%; Euroimmun-IgG: 92.1%). The antibody indices were higher in patients with fatal disease. In general, IgM/IgA assays had only limited or no benefit over IgG assays. In patients with non-SARS-CoV-2 respiratory infections, IgG assays were more specific than IgM/IgA assays, and bacterial infections were associated with more false-positive results than viral infections. The specificities in bacterial and viral infections were 68.0 and 81.3% (Vircell-IgM/IgA), 84.8 and 96.3% (Euroimmun-IgA), 97.8 and 86.0% (Vircell-IgG), and 97.8 and 99.1% (Euroimmun-IgG), respectively. Sera from patients positive for antibodies against Mycoplasma pneumoniae, Chlamydia psittaci, and Legionella pneumophila yielded particularly high rates of unspecific false-positive results in the IgM/IgA assays, which was revealed by applying a highly specific flow-cytometric assay using HEK 293 T cells expressing the SARS-CoV-2 spike protein. Positive results obtained with anti-SARS-CoV-2 IgM/IgA ELISAs require careful interpretation, especially if there is evidence for prior bacterial respiratory infections.

Association of mortality and recent Mycoplasma pneumoniae infection in COVID-19 patients.

To compare characteristics and outcomes of patients who had COVID-19 with Mycoplasma pneumoniae immunoglobulin M (IgM) antibodies to those without M. pneumoniae antibodies. We retrospectively reviewed cases admitted over a 4-week period between 17 March 2020 and 14 April 2020 to the Hoboken University Medical Center, NJ, USA. We compared the outcomes of COVID-19 patients who were positive for M. pneumoniae IgM with those who were negative for M. pneumoniae IgM. The primary outcome was mortality. The adjusted odds ratio was calculated after controlling for baseline differences. Of 139 patients admitted with COVID-19, 79 were positive for M. pneumoniae IgM. The mortality among those who were M. pneumoniae IgM positive was significantly higher (adjusted odds ratio: 2.28, 95% confidence interval: 1.03 to 5.03) compared with those who were M. pneumoniae IgM negative. Patients with coinfection (COVID-19 and mycoplasma) have higher mortality compared with patients with just COVID-19 disease.