Germany’s fourth COVID-19 wave was mainly driven by the unvaccinated

Background While the majority of the German population was fully vaccinated at the time (about 65%), COVID-19 incidence started growing exponentially in October 2021 with about 41% of recorded new symptomatic cases aged twelve or above being symptomatic breakthrough infections, presumably also contributing to the dynamics. So far, it remained elusive how significant this contribution was and whether targeted non-pharmaceutical interventions (NPIs) may have stopped the amplification of the crisis. Methods We develop and introduce a contribution matrix approach based on the next-generation matrix of a population-structured compartmental infectious disease model to derive contributions of respective inter- and intragroup infection pathways of unvaccinated and vaccinated subpopulations to the effective reproduction number and new infections, considering empirical data of vaccine efficacies against infection and transmission. Results Here we show that about 61%–76% of all new infections were caused by unvaccinated individuals and only 24%–39% were caused by the vaccinated. Furthermore, 32%–51% of new infections were likely caused by unvaccinated infecting other unvaccinated. Decreasing the transmissibility of the unvaccinated by, e. g. targeted NPIs, causes a steeper decrease in the effective reproduction number R than decreasing the transmissibility of vaccinated individuals, potentially leading to temporary epidemic control. Reducing contacts between vaccinated and unvaccinated individuals serves to decrease R in a similar manner as increasing vaccine uptake. Conclusions A minority of the German population—the unvaccinated—is assumed to have caused the majority of new infections in the fall of 2021 in Germany. Our results highlight the importance of combined measures, such as vaccination campaigns and targeted contact reductions to achieve temporary epidemic control.Peer Reviewe

Edge Transfer Lithography Using Alkanethiol Inks

Edge lithographic patterning techniques are based on the utilization of the edges of micrometer-sized template features for the reproduction of submicrometer structures. Edge transfer lithography (ETL) permits local surface modification in a single step by depositing self-assembled monolayers onto a metal substrate selectively along the feature edges of an elastomeric stamp. In this report two stamp designs are described that now allow for the use of alkanethiol inks in ETL and their use as etch resists to reproduce submicrometer structures in gold. Anisotropically modified stamps are shown to combine the potential for very high-resolution patterning with the versatility and simplicity of microcontact printing

Flipped Lab Ein verdrehtes Laborpraktikum

Erstsemesterstudierende fühlen sich gerade in der Studieneingangsphase naturwissenschaftlich-technischer Studiengänge häufig kognitiv überfordert, was sich negativ auf die erreichten Lernergebnisse auswirkt. In chemischen Studiengängen zeigt sich dies besonders deutlich in den Einführungspraktika in die Laborarbeit. An einem Praxisbeispiel wird in diesem Beitrag das Flipped-Lab-Konzept vorgestellt, das sich als geeignet erwiesen hat, dieses Lernhindernis zu überwinden. Durch einen Transfer der Prinzipien des Flipped-Classroom-Modells auf eine laborpraktische Lehrveranstaltung, kombiniert mit Online und Gruppenarbeitselementen, gelang es, Vorbereitung und Qualität der praktischen Arbeit und der laborpraktischen Lernergebnisse insgesamt zu verbessern. Die eingesetzten digitalen Werkzeuge wirkten sich positiv auf die subjektive Lernerfahrung sowie die real genutzte Selbststudienzeit der Studierenden aus. (DIPF/Orig.

Understanding the impact of digital contact tracing during the COVID-19 pandemic.

Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention's efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation

Outbreak size 〈Ω〉/<i>N</i> and relative outbreak size reduction caused by DCT with <i>UA</i>₀ = 12 (upper boundary), <i>UA</i>₀ = 4 (dotted) and <i>UA</i>₀ = 2.4 (lower boundary) caused by <i>q</i> ∈ {0.1, 0.3, 0.5}, respectively, for increasing app participation <i>a</i>.

Simulated on (A) ER, (B) WS, (C) EXP, and (D) WS-EXP networks with (i) the base parameter assumptions (in all subfigures highlighted in color for easier comparison), (ii) with only 50% of traced contacts reacting to a notification (iii) without isolation of susceptible contacts, (iv) where 100% (y = 1) of traced infected contacts can induce further tracing, (v) every app user uploads their result (z = 1) and, (vi) the delay of the event T(a) → X(a) is minimized (χ = 10).</p

We analyzed the efficacy of DCT on four different network topologies: Erdős–Rényi networks (ER, low clustering and narrow (binomial) degree distribution), Watts–Strogatz–like small-world networks (WS, high clustering, narrow degree distribution), configurational model random networks with exponential degree distribution (EXP), and WS-like networks with high clustering and exponential degree distribution (WS-EXP).

We analyzed the efficacy of DCT on four different network topologies: Erdős–Rényi networks (ER, low clustering and narrow (binomial) degree distribution), Watts–Strogatz–like small-world networks (WS, high clustering, narrow degree distribution), configurational model random networks with exponential degree distribution (EXP), and WS-like networks with high clustering and exponential degree distribution (WS-EXP).</p

Relative mean outbreak size reduction 1 − 〈Ω(<i>a</i> = 30%)〉/〈Ω(<i>a</i> = 0)〉 caused by DCT in different network topologies.

App participation was fixed at a = 30%, and symptom-based testing was assumed to lead to initial under-ascertainment factors of UA0 ∈ {12, 4, 2.4} (q = 0.1, 0.3 and 0.5, respectively). Note that 〈Ω(a = 0)〉 depends on the baseline under-ascertainment factor UA0 as well as the contact structure, see also Fig 7. DCT has a stronger effect in WS networks (purple) compared to the ER, EXP and WS-EXP networks. Increasing symptom-based testing (i.e. decreasing the under-ascertainment factor) enhances the efficacy of DCT.</p

Outbreak size 〈Ω〉/<i>N</i> for different network models introduced in Fig 2, shown for app participation of <i>a</i> ∈ {0%, 30%, 50%}.

We compared the absence of symptom-based testing, and testing that would lead to under-ascertainment factors of UA0 ∈ {4, 2.4} caused by q ∈ {0.3, 0.5}, respectively. Empirical observations suggest that several countries reached a ≈ 30% app participation and a under-ascertainment factor on the order of UA0 = 4 (marked as “current”) [25, 27, 28, 31]. We find no significant difference between increasing either symptom-based testing or app participation for three of the four network structures. For WS networks, an increase of symptom-based testing leads to a stronger reduction than an increase in app participation.</p