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To examine the risk associated with bus riding and identify transmission chains, we investigated a COVID-19 outbreak in Germany in 2021 that involved index case-patients among bus-riding students. We used routine surveillance data, performed laboratory analyses, interviewed case-patients, and conducted a cohort study. We identified 191 case-patients, 65 (34%) of whom were elementary schoolchildren. A phylogenetically unique strain and epidemiologic analyses provided a link between air travelers and cases among bus company staff, schoolchildren, other bus passengers, and their respective household members. The attack rate among bus-riding children at 1 school was ≈4 times higher than among children not taking a bus to that school. The outbreak exemplifies how an airborne agent may be transmitted effectively through (multiple) short (

Two COVID-19 outbreaks occurred in residential buildings with overcrowded housing conditions in the city of Göttingen in Germany during May and June 2020, when COVID-19 infection incidences were low across the rest of the country, with a national incidence of 2.6/100,000 population. The outbreaks increased the local incidence in the city of Göttingen to 123.5/100,000 in June 2020. Many of the affected residents were living in precarious conditions and experienced language barriers. The outbreaks were characterized by high case numbers and attack rates among the residents, many asymptomatic cases, a comparatively young population, and substantial outbreak control measures implemented by local authorities. We analyzed national and local surveillance data, calculated age-, and gender-specific attack rates and performed whole genome sequencing analysis to describe the outbreak and characteristics of the infected population. The authorities' infection control measures included voluntary and compulsory testing of all residents and mass quarantine. Public health measures, such as the general closure of schools and a public space as well as the prohibition of team sports at local level, were also implemented in the district to limit the outbreaks locally. The outbreaks were under control by the end of June 2020. We describe the measures to contain the outbreaks, the challenges experienced and lessons learned. We discuss how public health measures can be planned and implemented through consideration of the needs and vulnerabilities of affected populations. In order to avoid coercive measures, barrier-free communication, with language translation when needed, and consideration of socio-economic circumstances of affected populations are crucial for controlling infectious disease transmission in an outbreak effectively and in a timely way.

Here, we report on the increasing frequency of the SARS-CoV-2 lineage A.27 in Germany during the first months of 2021. Genomic surveillance identified 710 A.27 genomes in Germany as of 2 May 2021, with a vast majority identified in laboratories from a single German state (Baden-Wuerttemberg, n = 572; 80.5%). Baden-Wuerttemberg is located near the border with France, from where most A.27 sequences were entered into public databases until May 2021. The first appearance of this lineage based on sequencing in a laboratory in Baden-Wuerttemberg can be dated to early January ’21. From then on, the relative abundance of A.27 increased until the end of February but has since declined—meanwhile, the abundance of B.1.1.7 increased in the region. The A.27 lineage shows a mutational pattern typical of VOIs/VOCs, including an accumulation of amino acid substitutions in the Spike glycoprotein. Among those, L18F, L452R and N501Y are located in the epitope regions of the N-terminal- (NTD) or receptor binding domain (RBD) and have been suggested to result in immune escape and higher transmissibility. In addition, A.27 does not show the D614G mutation typical for all VOIs/VOCs from the B lineage. Overall, A.27 should continue to be monitored nationally and internationally, even though the observed trend in Germany was initially displaced by B.1.1.7 (Alpha), while now B.1.617.2 (Delta) is on the rise.

Non-methylated islands (NMIs) of DNA are genomic regions that are important for gene regulation and development. A recent study of genome-wide non-methylation data in vertebrates by Long et al. (eLife 2013;2:e00348) has shown that many experimentally identified non-methylated regions do not overlap with classically defined CpG islands which are computationally predicted using simple DNA sequence features. This is especially true in cold-blooded vertebrates such as Danio rerio (zebrafish). In order to investigate how predictive DNA sequence is of a region's methylation status, we applied a supervised learning approach using a spectrum kernel support vector machine, to see if a more complex model and supervised learning can be used to improve non-methylated island prediction and to understand the sequence properties of these regions. We demonstrate that DNA sequence is highly predictive of methylation status, and that in contrast to existing CpG island prediction methods our method is able to provide more useful predictions of NMIs genome-wide in all vertebrate organisms that were studied. Our results also show that in cold-blooded vertebrates (Anolis carolinensis, Xenopus tropicalis and Danio rerio) where genome-wide classical CpG island predictions consist primarily of false positives, longer primarily AT-rich DNA sequence features are able to identify these regions much more accurately.