The aim of the present study is to analyse the secondary bacterial infections in a large group of patients with seasonal influenza A and influenza A(H1N1) pdm09. Patients diagnosed with seasonal influenza A and influenza A(H1N1) pdm09 between 2005 and 2009 were enrolled in the study. Data was retrieved from medical records and laboratory information systems (LIS). In total, 1094 patients with laboratory confirmed influenza were studied. There were 352 patients with seasonal influenza A and 742 patients with influenza A(H1N1) pdm09. The patients with influenza A were older and had higher comorbidity than patients with influenza A(H1N1) pdm09 (P < 0.001 and P < 0.05, resp.). Hospital admission was higher in influenza A group (P = 0.01). In contrast, ICU admission was higher in patients with influenza A(H1N1) pdm09 than influenza A patients (P < 0.05). There were higher numbers of bacterial samples taken and culture positivity in patients with influenza A than patients with influenza A(H1N1) pdm09 (P < 0.0001 and P = 0.01, resp.). In both groups, the majority of the patients with positive bacterial cultures had underlying diseases. The present study shows that the patient characteristics and the frequency of secondary bacterial infections were different in patients with seasonal influenza A and in patients with influenza A(H1N1) pdm09.

Serological studies are critical for understanding pathogen−specific immune responses and informing public health measures (1,2). By developing highly sensitive and specific trimeric spike (S)−based antibody tests, we report IgM, IgG and IgA responses to SARS−CoV−2 in COVID−19 patients (n=105) representing different categories of disease severity. All patients surveyed were IgG positive against S. Elevated anti−SARS−CoV−2 antibody levels were associated with hospitalization, with IgA titers, increased circulating IL−6 and strong neutralizing responses indicative of intensive care status. Antibody−positive blood donors and pregnant women sampled during the pandemic in Stockholm, Sweden (weeks 14−25), displayed on average lower titers and weaker neutralizing responses compared to patients; however, inter−individual anti−viral IgG titers differed up to 1,000−fold. To provide more accurate estimates of seroprevalence, given the frequency of weak responders and the limitations associated with the dichotomization of a continuous variable (3,4), we used a Bayesian approach to assign likelihood of past infection without setting an assay cut−off. Analysis of blood donors (n=1,000) and pregnant women (n=900) sampled weekly demonstrated SARS-CoV-2-specific IgG in 7.2% (95% Bayesian CI [5.1−9.5]) of individuals two months after the peak of spring 2020 COVID−19 deaths. Seroprevalence in these otherwise healthy cohorts increased steeply before beginning to level−off, following the same trajectory as the Stockholm region deaths over this time period.

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Objectives Population‐level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data‐driven manner, leading to uncertainty when classifying low‐titer responses. To improve upon this, we evaluated cutoff‐independent methods for their ability to assign likelihood of SARS‐CoV‐2 seropositivity to individual samples. Methods Using robust ELISAs based on SARS‐CoV‐2 spike (S) and the receptor‐binding domain (RBD), we profiled antibody responses in a group of SARS‐CoV‐2 PCR+ individuals ( n = 138). Using these data, we trained probabilistic learners to assign likelihood of seropositivity to test samples of unknown serostatus ( n = 5100), identifying a support vector machines‐linear discriminant analysis learner (SVM‐LDA) suited for this purpose. Results In the training data from confirmed ancestral SARS‐CoV‐2 infections, 99% of participants had detectable anti‐S and ‐RBD IgG in the circulation, with titers differing > 1000‐fold between persons. In data of otherwise healthy individuals, 7.2% ( n = 367) of samples were of uncertain serostatus, with values in the range of 3‐6SD from the mean of pre‐pandemic negative controls ( n = 595). In contrast, SVM‐LDA classified 6.4% ( n = 328) of test samples as having a high likelihood (> 99% chance) of past infection, 4.5% ( n = 230) to have a 50–99% likelihood, and 4.0% ( n = 203) to have a 10–49% likelihood. As different probabilistic approaches were more consistent with each other than conventional SD‐based methods, such tools allow for more statistically‐sound seropositivity estimates in large cohorts. Conclusion Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.