Abstract Respiratory diseases cause considerable morbidity in autumn and winter and are a priority in public health monitoring. In Germany, they are subject to a number of surveillance systems, including both pathogen-specific and syndromic indicators. In this paper we present a collaborative multi-target and multi-model real-time forecasting system rolled out during the 2024/25 season, and discuss differences to earlier efforts carried out during the COVID-19 pandemic. A total of nine models were run to generate forecasts of general practitioner consultations for acute respiratory infections (ARI), hospitalizations for severe acute respiratory infections (SARI) and confirmed cases of seasonal influenza and RSV. As all indicators were subject to retrospective revisions, forecasting models were combined with a nowcasting step. Whenever multiple models were available for the same indicator, we combined them into an ensemble. Nowcasts showed convincing performance, even though for some models Christmas break effects led to an upward bias in early January. Forecasts were overall well-calibrated and most models outperformed simple benchmark models. These improvements were generally more substantial for age-stratified than pooled targets, and concentrated at lead times of two to three weeks. Anticipating the peak timing and magnitude proved to be challenging, with many models predicting too flat curves with a too early turnaround (e.g. already in late January rather than mid-February for SARI). The combined ensemble forecast was among the best-performing approaches, but unlike in previous related projects did not consistently outperform individual models. We conclude by discussing learnings on the organization of collaborative forecasting projects in post-COVID-19 times and the potential of AI-supported modelling.

Background and Aims: Hepatitis C virus (HCV) infections were previously treated with interferon (IFN) but today direct acting antivirals (DAAs) with cure rates >95% are available. DAA treatment failure is primarily attributed to resistance associated mutations (RAMs), often imposing a fitness cost. Interferon treatment outcome was shown to be associated with the interferon sensitivity determining region (ISDR), which is part of the replication enhancing domain (ReED) in non-structural protein (NS) 5A. We found that accumulation of mutations in the ReED was indicative of elevated viral genome replication fitness. This study investigates the impact of HCV replication fitness on antiviral treatment outcomes. Methods: We utilized chimeric HCV subgenomic replicons containing RAMs and ReED sequences from patients after interferon treatment or DAA failure to assess replication fitness of patient isolates in presence and absence of inhibitors. Results: Replication fitness did not impact on IFN sensitivity in cell culture but resulted in higher remaining antigen levels for highly replicating variants at a given IFN concentration. Furthermore, we identified ReED variants substantially increasing HCV replication in several patients who failed DAA therapy across different genotypes. High replicator ReEDs rescued the fitness loss caused by RAMs like Y93C/H (NS5A) and S282T (NS5B). While high replication fitness did not intrinsically increase drug sensitivity (IC50), it allowed the virus to sustain robust replication despite antiviral pressure. Conclusions: Elevated replication fitness might support interferon treatment due to increased antigen presentation, facilitating adaptive immune responses. Furthermore, ReED mediated increase in replication fitness could contribute to DAA treatment failure by preserving higher replication upon treatment and compensating for RAM associated fitness costs. Thus, patients failing DAA treatment should be monitored for RAMs and ReED mutations. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, 519777725, 272983813 – TRR 179 Deutsches Zentrum für Infektionsforschung (DZIF), DZIF academy stipend, TTU Hepatitis 05.821

Background: The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to pose significant public health challenges globally. This study focuses on the surveillance and discovery of SARS-CoV-2 variants in schools in Portugal and Italy, emphasizing the importance of monitoring variant spread in educational settings. Methods: We used a non-invasive, Lolli-Method for pooled saliva sampling and RT-qPCR among students across various educational settings, between November 2022 and March 2024. Pooled sampling was performed twice a week as part of routine school activities without medical personnel. In case a class tested positive, a round of individual Lolli testing was carried out in that class on the following day. Sequencing was performed through Sanger and NGS sequencing. Sequencing data analysis was performed using SeqMan, Nextclade, MAFFT alignment, comparison with GISAID sequences, and IQ-TREE 2 for phylogenetic analysis. Findings: A total of 5·191 samples were collected, including 2·171 (41·8%) from Portugal and 3·020 (58·2%) from Italy. Portugal showed higher positivity rates for both individual (8·85%) and pooled samples (5·47%). Of the 73 sequenced samples, 23 were from Portugal and 50 from Italy. Twelve distinct variants were detected, with XBB.1.5 (36%) and CH.1.1 (20%) being the most prevalent. Variants FL.14 and HU.1.1 were detected specifically in children in our study. Variant JN.1 was detected circulating in schools two months earlier than it was first reported in Denmark. Interpretation: The Lolli-Method proved effective for school-based surveillance and early detection of emerging variants and other infectious threats.

Background:HIV-1 sub-subtype A6 is predominant in Eastern Europe and was associated with increased risk of treatment failure with the long-acting cabotegravir plus rilpivirine regimen. In this study, we aimed to evaluate the in vitro susceptibility and the genetic barrier to resistance to INSTI in recombinant viruses harboring clinically derived A6 integrase coding regions. Methods:We generated 23 NL4-3 strain-based recombinant viruses harboring clinically derived integrase coding region. We measured their susceptibility to second-generation INSTIs dolutegravir, bictegravir, and cabotegravir in a TZM-bl cell-based phenotypic assay. The genetic barrier to resistance was evaluated by exposing MT-2 cell cultures infected with 4 A6 integrase recombinant viruses, as well as the NL4-3 and HXB2 subtype B reference strains. Results:All 23 recombinant viruses generated with clinically derived A6 integrase displayed full susceptibility to dolutegravir, bictegravir, and cabotegravir, showing median (interquartile range) fold-change values of 1.2 (0.9-1.5), 1.1 (0.7-1.5), and 0.9 (0.6-1.1), respectively. Of 4 A6 viruses assessed for their genetic barrier to resistance in vitro, only 1 showed emerging integrase mutations E138K or Q148R at subinhibitory concentrations of dolutegravir or cabotegravir, respectively. Conclusions:These data suggest that sub-subtype A6 integrase has full susceptibility and largely maintains a high genetic barrier to resistance to second-generation integrase strand transfer inhibitors.

OBJECTIVE:The objective of the study was to establish whether HIV-1 sub-subtype A6 (HIV-1A6) is a risk factor for virological failure in people with HIV (PWH) treated with the high genetic barrier integrase strand transfer inhibitors (INSTIs) dolutegravir (DTG) or bictegravir (BIC). METHODS:The virological outcome of first-line DTG or BIC-containing antiretroviral therapy (ART) was assessed in 261 people with HIV-1A6 (PWH-1A6) and 1042 people with HIV-1B (PWH-1B) starting treatment between January 2014 and May 2025 with follow-up for at least one year in the EuResist Integrated Database. RESULTS:Most PWH-1A6 were recent migrants from Ukraine. The event rates per 100 person-years follow-up were higher in PWH-1A6 for low-level viremia (LLV, 2.71 vs. 1.71, P = 0.049) and virological failure with more than 1000 HIV RNA copies/mL (4.12 vs. 2.03, P < 0.001) than in PWH-1B. At the end of follow-up, 226/261 (86.6%) PWH-1A6 and 936/1042 (89.8%) PWH-1B had viral load below 50 copies/ml (P = 0.132). INSTI DRMs were observed in 5/219 (2.3%) available integrase sequences of PWH-1A6, including two cases detected at virological failure with more than 1000 HIV RNA copies/ml and two cases in virologically suppressed PWH-1A6. In total, 12/261 PWH-1A6 discontinued DTG- or BIC-containing ART, including three individuals who were not virologically suppressed at discontinuation. CONCLUSION:In PWH treated with DTG or BIC-containing first-line ART, LLV and virological failure with more than 1000 HIV RNA copies/ml were observed more frequently in PWH-1A6 than in PWH-1B. However, such events rarely resulted in INSTI resistance or discontinuation of INSTI-containing ART.

Chlamydia pneumoniae (C. pneumoniae) is a recognized cause of respiratory infections in children and adolescents, while it is often considered a negligible pathogen in adults outside of outbreaks. We performed a retrospective analysis from a nationwide surveillance network in Germany that collected data from 2018 to 2024. A multivariate analysis (binomial model) was performed to assess the influence of year, hospitalization status, gender and age on C. pneumoniae detection rate. Our analysis showed an increasing C. pneumoniae detection rate in 2024 compared to 2019, especially in children below 15 years and adults aged 30–50 years, mostly in patients who were treated as outpatients.

In the beginning of 2020, the outbreak of the COVID-19 pandemic led to a crisis in which diagnostic methods for the genome detection of SARS-CoV-2 were urgently needed. Based on the very early publication of the basic principles for a diagnostic test for the genome detection of SARS-CoV-2, the first noncommercial laboratory-developed tests (LDTs) and commercial tests were introduced. As there was considerable uncertainty about the reliability and performance of different tests and different laboratories, INSTAND established external quality assessment (EQA) schemes for the detection of SARS-CoV-2 starting in April 2020. In close partnership in a scientific network, the EQA schemes were enhanced, especially the April, June and November 2020 terms. The enhancement included: (i) immediate provision of suitable virus including variants of concern at the beginning of the pandemic outbreak, (ii) short frequency of EQA schemes, (iii) concentration dependency of the testing and sensitivity check, achieved by using SARS-CoV-2-positive samples from a 10-fold dilution series of the same starting material, (iv) specificity check of the testing, achieved by using SARS-CoV-2-negative samples containing human coronaviruses or MERS CoV, (v) revealed samples for orientation on test performance during an ongoing or at the start of an EQA scheme using a pre-quantified SARS-CoV-2-positive EQA sample with a low viral RNA load of only 1 570 copies/mL assigned by digital PCR (dPCR) in June 2020 and (vi) quantified reference materials based on the experiences of the first two EQA schemes with dPCR-assigned values in copies/mL beginning in November 2020 for self-evaluation of the applied test system. This manuscript summarizes the results of a total of 13 EQA schemes for the detection of SARS-CoV-2 between April 2020 and June 2023 in which a total of 1 413 laboratories from 49 countries participated. The qualitative results for the detection of SARS-CoV-2-positive samples were between 95.8 % and 99.7 % correct positive, excluding extremely low concentration samples. For all SARS-CoV-2-negative EQA samples, the qualitative success rates ranged from 95.1 % to 99.4 % correct negative results. The widely varying values for the cycle threshold (Ct)/crossing point (Cq) reported for the different target genes and test systems were striking. A few laboratories reported quantitative results in copies/mL for several VOCs with an acceptable rate of over 93 % correct positive results in the majority of cases. The description of the enhanced EQA schemes for SARS-CoV-2 detection in terms of timing and scope can serve as a blueprint for the rapid development of a quality assessment of diagnostics for an emerging pathogen.

Motivation:The emergence of multidrug class resistance (MDR) in Human Immunodeficiency Virus (HIV) is a rare but significant challenge in antiretroviral therapy (ART). MDR, which may arise from prolonged drug exposure, treatment failures, or transmission of resistant strains, accelerates disease progression and poses particular challenges in resource-limited settings with restricted access to resistance testing and advanced therapies. Early prediction of future MDR development is important to inform therapeutic decisions and mitigate its occurrence. Results:In this study, we employ various machine learning classifiers to predict future resistance to all four major antiretroviral drug classes using features extracted from clinical HIV sequence data. We systematically explore several variations of the problem that differ in the pre-existing resistance level and the temporal gap between sample collection and observed MDR occurrence. Our models show the ability to predict multidrug class resistance even in the most challenging variations, albeit at a reduced accuracy. Feature importance analysis reveals that our models primarily utilize known drug resistance mutations for easier classification tasks, but rely on new mutations for the difficult task of distinguishing four class drug resistance from three class drug resistance. Availability and implementation:All analysis was performed using the Euresist Integrated DataBase (EIDB). Researchers wishing to reproduce, validate or extend these findings can request access to the latest EIDB release via the Euresist Network.

BACKGROUND:Respiratory viral diseases are one of the greatest challenges facing our healthcare system, with them being one of the main causes of death. This has been demonstrated once again by the impact of the SARS-CoV-2 pandemic in recent years. We study the impact of the SARS-CoV-2 pandemic on the prevalence of respiratory viruses by analysing a subset of the Clinical Virology network database, covering 2,216,198 samples tested for 18 different viral pathogens in the time span from 2010 to 2024. METHODS:We calculated the prevalence of 17 respiratory viruses before and after onset of the SARS-CoV-2 pandemic and compared the degree of seasonality shift with a newly developed a metric dubbed seasonal disruption index. In addition, we compared coinfection statistics prior to and after the pandemic onset, and also studied the correlation of infection counts with non-pharmaceutical interventions in the time frame from early 2020 to end of 2022. RESULTS:We found that the viral pathogens show a varying degree of seasonality disruption. It is largest among those that are known to show a highly seasonal behavior, namely Influenza and RSV, the latter having the highest seasonal disruption index. Most perennial viruses continued to appear throughout the year. Coinfections occurred before and after the pandemic; patterns before and after pandemic onset are surprisingly similar. The occurrence of most viruses is nonlinearly correlated with the degree of non-pharmaceutical interventions. CONCLUSION:The SARS-CoV-2 pandemic had a considerable impact on the occurrence and seasonality of other respiratory viruses. While nearly all seasonality patterns were initially disrupted due to the heavy non-pharmaceutical interventions, viruses are regaining their pre-pandemic seasonality.

To quantify virologic failure (VF), identify predictors, characterize resistance patterns at failure, and evaluate time to resuppression in the RESINA cohort. ART-naïve adults initiating ART in 2001–2024 were followed. VF was defined as at least one HIV-1 RNA > 200 copies/mL after suppression or ≥ 0.5-log₁₀ rebound. Participants were grouped by treatment era (2001–2007, 2008–2013, ≥ 2014), reflecting availability of drug classes. Genotypes at baseline and VF were interpreted using the HIV-GRADE algorithm. Predictors of VF were assessed with logistic regression; time to resuppression (< 50 copies/mL) after first VF with Cox models and Kaplan–Meier plots. Among 5136 participants, 139 (2.7

BACKGROUND:Immunocompromised individuals, hemato-oncologic diseases or post-transplantation included, are, due to impaired immune response, at increased risk for severe and prolonged COVID-19. Observational Studies showed that SARS-CoV-2 RNAemia has been associated with poorer prognosis and higher disease severity. OBJECTIVE:The aim of this study was to investigate the occurrence of RNAemia and its association with anti-SARS-CoV-2 antibodies in immunocompromised COVID-19 patients. Risk factors for RNAemia were included in the analysis. STUDY DESIGN:A retrospective study was conducted in 55 immunocompromised patients tested positive for SARS-CoV-2, who received treatment with monoclonal antibodies (mAb) between December 2021 and March 2022. Serological and virological tests were performed before mAb administration and clinical data were collected from electronic health records. RESULTS:Out of 55 patients, 35 % showed SARS-CoV-2 RNAemia. RNAemia was present in the 2 reported fatal cases. It was associated with negative testing for anti-receptor binding domain (RBD) IgG, anti-S2 domain of spike protein (S2) IgG and a lower leukocyte count. No association was found between previous COVID-19 vaccinations and the risk for RNAemia in immunocompromised patients. CONCLUSION:The study underscores the importance of humoral response in controlling SARS-CoV-2 replication. RNAemia can serve as a potential biomarker for disease severity in immunocompromised individuals. Therefore, it should be considered in clinical settings for appropriate therapy decisions. Further research is needed to evaluate the pathophysiology and implications of RNAemia in immunodeficient patients with COVID-19.

Respiratory syncytial virus (RSV) is a leading cause of respiratory infections in young children, elderly people, and patients with underlying diseases. Solid data on its epidemiology and burden of disease are essential for the implementation of preventive strategies. This review provides for the first time a comprehensive overview on publicly available RSV surveillance resources in Germany. Methods: Public RSV surveillance systems in Germany were identified and, where possible, exemplary data was extracted to provide an overview of the scope of available data, their strengths and limitations. Results: German RSV surveillance systems provide data on both outpatient and inpatient incidence rates, age distribution, and seasonality. Germany’s public health institution, the Robert Koch Institute (RKI), documents RSV cases nationwide based on mandatory reporting. Further, sentinel surveillance by RKI captures outpatient RSV infections as well as severe hospitalized cases. Nationwide, data on inpatients is collected and reported by hospital discharge diagnostic codes. Additional surveillance systems (e.g., clinical-virology.net) provide data on RSV positivity rates stratified by age and gender. Regional surveillance efforts by ten German states provide data on the infection dynamics. Pediatric documentation of age distribution and severity of respiratory diseases via surveillance was initiated by the German Society for Pediatric Infectious Diseases. Reviewing all available sources and data underlines the high clinical burden, especially in infants and older adults during the winter season. Conclusions: Germany’s RSV surveillance systems on the national and regional level support the tracking of incidence rates and seasonal patterns. Notably, pediatric data collection is more thorough, yielding a more comprehensive dataset than that available for adults. Contextualizing reported incidence rates in light of prospective or modeling studies suggests that the official documentation of RSV cases—particularly among adults—is underestimated.

Context Subacute thyroiditis (SAT) is a painful inflammatory disorder of the thyroid gland, which-after a phase of thyrotoxicosis-leads to transient, or less frequently permanent hypothyroidism. Apart from a strong association with specific human leukocyte antigen alleles, the causes are uncertain. Viral disease has been hypothesized as a trigger, with enteroviruses, namely echovirus and coxsackievirus, showing a seasonal distribution that coincides with the incidence of SAT.Objective In the first year of the COVID-19 pandemic, strict hygiene measures led to a sharp decline in infections and thus offered the opportunity to test this hypothesis.Methods We analyzed national registry data of hospitalized patients from Germany during the years 2015 to 2022 (Federal Statistical Office [Destatis], Wiesbaden, Germany) and surveillance data on infectious diseases from the same years (clinical-virology.net and RKI). Statistical analysis includes modeling of seasonality by month, polynomial autoregression, and Granger causality to assess dependency of future SAT frequencies from past ones, and association of virus incidence to SAT frequency, respectively.Results Our study confirms previously described epidemiological findings with higher incidence in women and a seasonal peak in late summer coinciding with the seasonality of enteroviruses until 2019. In 2020, the pattern remained unchanged, except for the marked reduction of enteroviruses and other pathogens (except SARS-CoV-2) due to hygienic measures. Moreover, the SAT seasonality in the years 2021 and 2022 was apparently unaltered through the COVID-19 pandemic.Conclusion Our study provides strong evidence that despite their seasonal pattern, Echoviruses and Coxsackieviruses are not the cause of SAT. Moreover, no other analyzed virus (including Influenza A and B, Parainfluenza, Rhinovirus, Human Coronaviruses including SARS-CoV-2) showed any association.

Human immunodeficiency virus type 2 (HIV-2) is an attenuated retroviral infection characterized by specific natural susceptibility to antiretroviral drugs and acquired resistance profiles. Based on the latest knowledge of phenotypic data and clinical follow-up, HIV-2 resistance interpretation rules have been updated and implemented in a freely available resistance analysis tool.

Background Antiretroviral therapy (ART) is a life saving option for people living with HIV-1 (PLWH) and is effective against many viral strains. The most common ARTs involve combinations of drugs targeting viral or cellular proteins. Most of these drugs have to be taken daily. An alternative to ARTs with established inhibitors comprises broadly neutralizing antibodies (bNAbs). However, bNAbs share the problem of viral resistance with protein inhibitors. We developed a web service geno2pheno[bNAbs] that allows users to upload viral genotypes and estimates the respective resistance to many common bNAbs. The service uses trained statistical models to classify the virus into sensitive and resistant, respectively or to regress the IC50. Methods We used two linear models as well as two neural nets for each task and multi-task (MT) learning to train both models for IC50 prediction and classification simultaneously. During multi-task learning we penalize divergence of class and IC50 score in addition to the loss individual to each of the models. Findings We compared the linear models of geno2pheno[bNAbs] to other state-of-the-art methods like recurrent neural nets and self-attention, and found them to be competitive in regard to accuracy and have the benefit of fast computation and being easily interpretable in regard to features, i.e., positions on the envelope. Interpretation We developed a web service for the prediction of antibody resistance (geno2pheno[bNAbs]) to HIV-1, which is free to use and can be extended to other viruses, like Sars-Cov2, in the future. ### Competing Interest Statement The authors have declared no competing interest.

ABSTRACTHepatitis C virus infection is a significant global health concern, affecting millions worldwide. Although direct‐acting antivirals achieve over 90% success rate, treatment failures still occur, particularly when pan‐genotypic DAAs are unavailable, and drugs need to be chosen based on the present HCV genotype. Genotyping tests can be misleading, especially in cases involving the 2k/1b recombinant variant. The 2k/1b variant was first discovered in Saint Petersburg in 2002 and is most commonly observed in Eastern European countries, including Russia, Georgia, and Ukraine. Due to migration, the 2k/1b variant has spread to Western Europe and other regions, potentially increasing HCV transmission and changing the virus's epidemiological landscape. The situation highlights the importance of molecular epidemiology in monitoring the spread of the 2k/1b variant. Accurate detection and characterization of the 2k/1b variant are crucial for an effective treatment if no pan‐genotypic DAAs are available. To address this need, machine learning models were developed to predict the 2k/1b variant based on 1b and 2k/1b sequence data from nonstructural proteins. They were integrated into the geno2pheno[HCV] tool, providing physicians and researchers with an open‐access resource for determining HCV genotypes, including the 2k/1b variant.