Sleep is fundamental to health, yet large-scale, objective data on how geography shapes sleep behavior remain scarce. We analyzed over 45 million nights of sensor data from 105,741 German adults wearing consumer-grade wearables across 2.7 years. Sleep timing displayed a continuous east–west gradient, with later onset, midsleep, and offset in western regions, consistent with solar progression. This effect was strongest on weekends and in rural areas, where midsleep was delayed by 2.2 minutes per degree longitude and sleep duration increased by 1.0 minute. A north–south gradient also emerged. Weekday midsleep advanced by 0.9 minutes per degree latitude, while weekend midsleep was delayed by 0.2 minutes, resulting in greater social jetlag in the north. Sleep duration declined toward higher latitudes across both day types. Seasonal analyses revealed consistent annual rhythms. Sleep duration increased by 24.7 minutes in winter relative to summer, and sleep offset closely followed sunrise. These patterns highlight the joint influence of solar and social time on sleep, with implications for regionally tailored public health strategies. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), 492631324, 450622422 Joachim Herz Stiftung

Understanding the factors associated with persistent symptoms after SARS-CoV-2 infection is critical to improving long-term health outcomes. Using a wearable-derived behavioral and physiological dataset (n = 20,815), we identified individuals characterized by self-reported persistent fatigue and shortness of breath after SARS-CoV-2 infection. Compared with symptom-free COVID-19 positive (n = 150) and negative controls (n = 150), these individuals (n = 50) had higher resting heart rates (mean difference 2.37/1.49 bpm) and lower daily step counts (mean 3030/2909 steps fewer), even at least three weeks prior to SARS-CoV-2 infection. In addition, persistent fatigue and shortness of breath were associated with a significant reduction in mean quality of life (WHO-5, EQ-5D), even before infection. Here we show that persistent symptoms after SARS-CoV-2 infection may be associated with pre-existing lower fitness levels or health conditions. These findings additionally highlight the potential of wearable devices to track health dynamics and provide valuable insights into long-term outcomes of infectious diseases.

In asexual populations, clonal interference, the competition between strains carrying different beneficial mutations, plays a crucial role in shaping evolutionary outcomes. This study investigates how this phenomenon unfolds in complex heterogeneous networks, demonstrating that network structure significantly affects the origin and spread of high-fitness strains. Using computational modeling and a novel analytical approach, we demonstrate that the interplay between mutation rates and network topology creates distinct evolutionary regimes. We observe that the mutations driving adaptation originate from network locations that facilitate mutation spread within a time-frame given by the mutation rate. If the window widens or narrows, the contribution of different network regions grows or diminishes. Using analytical approaches from epidemic modeling and network geometry, our analysis efficiently captures this evolutionary dynamic in arbitrary network configurations. This work provides new insights into how spatial heterogeneity shapes evolutionary trajectories and indicates that attempts to alter the speed of adaptation through network modifications will succeed or fail depending critically on the prevailing evolutionary regime—insights relevant to epidemiology, conservation biology, and beyond. ### Competing Interest Statement The authors have declared no competing interest.

Disease propagation between countries strongly depends on their effective distance, a measure derived from the world air transportation network (WAN). It reduces the complex spreading patterns of a pandemic to a wave-like propagation from the outbreak country, establishing a linear relationship to the arrival time of the unmitigated spread of a disease. However, in the early stages of an outbreak, what concerns decision-makers in countries is understanding the relative risk of active cases arriving in their country-essentially, the likelihood that an active case boarding an airplane at the outbreak location will reach them. While there are data-fitted models available to estimate these risks, accurate mechanistic, parameter-free models are still lacking. Therefore, we introduce the 'import risk' model in this study, which defines import probabilities using the effective-distance framework. The model assumes that airline passengers are distributed along the shortest path tree that starts at the outbreak's origin. In combination with a random walk, we account for all possible paths, thus inferring predominant connecting flights. Our model outperforms other mobility models, such as the radiation and gravity model with varying distance types, and it improves further if additional geographic information is included. The import risk model's precision increases for countries with stronger connections within the WAN, and it reveals a geographic distance dependence that implies a pull- rather than a push-dynamic in the distribution process.

Smartphones, smartwatches, linked wearables, and associated wellness apps have had rapid uptake. These tools become ever ‘smarter’ in sensing intimate aspects of our surroundings and physiology over time, including activity, metabolites, electrical signals, blood pressure and oxygenation. Proposed EU law stipulates the ‘involuntary donation’ of depersonalized health and wellness data. There has been pushback against the ever-increasing gathering and sharing of wellness data in this context, increasing with every app purchased or updated. Is the potential of this data now lost to research? Consent-led COVID-19 data donation projects signpost a participative, standardized, and scalable approach to data sharing.

Objective After infection with SARS-CoV-2, a substantial proportion of patients develop long-lasting sequelae. These sequelae include fatigue (potentially as severe as that seen in ME/CFS cases), cognitive dysfunction, and psychiatric symptoms. Because the pathophysiology of these sequelae remains unclear, existing therapeutic concepts address the symptoms through pacing strategies, cognitive training, and psychological therapy. Methods Here, we present a protocol for a digital multimodal structured intervention addressing common symptoms through three intervention modules: BRAIN, BODY, and SOUL. This intervention includes an assessment conducted via a mobile “post-COVID-19 bus” near the patient's home, as well as the use of wearable devices and mobile applications to support pacing strategies and collection of data, including ecological momentary assessment. Results We will focus on physical component subscore of the SF36 as Quality of Life parameter as the primary outcome parameter for WATCH to take into account the holistic approach that is necessary for care of post-COVID patients Conclusion In the current project, we present a protocol for a holistic and multimodal structured therapeutic concept which is easily accessible, and scalable for post-COVID patients.

Background Evidence based findings on long-term health-related consequences of a SARS-CoV-2 infection remain scarce. Data from wearable devices, well suited for continuous measurement of heart rate and physical activity, offers a unique opportunity to assess the impact of such infections on an individual’s health. Here we aim to characterize comprehensively how persistent self-reported symptoms during both acute and post-acute infection correlate to changes in resting heart rate (RHR) and physical activity, as measured by consumer-grade wearable sensors. Methods Using a wearable-derived dataset of behavior and physiology (n = 20,815), we identified 137 individuals who are characterized by persistent fatigue and shortness of breath after a reported positive SARS-CoV-2 test. We compared this cohort with COVID-19 positive without persistent symptoms and negative controls. The comparison is based on measurements of RHR and physical activity as well as self-reported health-related Quality of Life (QoL) through WHO-5 and EQ-5D before, during, and after the infection. Findings We identified a unique phenotype of persistent COVID-19 symptoms and associated wearable data characteristics and compared this phenotype to COVID-19 positive and negative controls. Individuals who reported persistent symptoms (coexisting shortness of breath and fatigue) showed higher RHRs (mean difference of 2 · 37/1 · 49 bpm), and lower daily step count (on average 3,030/2,909 steps less) compared to positive/negative controls, even at least three weeks prior to a SARS-CoV-2 infection. During the acute phase (0-4 weeks after a positive COVID-19 test), individuals with persistent shortness of breath and fatigue exhibited a decrease in mean RHR, 1 · 86 times that of individuals in the positive control cohort. Similarly, the persistent symptom phenotype took an average of seven days longer to return to normal compared to positive controls. Additionally we found that self-reported persistent COVID-19 symptoms are linked to a substantial reduction in mean QoL, even before infection. Interpretation The analysis of individual wearable time-series suggests that the persistent symptom phenotype, characterized by shortness of breath and fatigue, may have been more exposed to pre-existing health conditions and/or exhibited lower levels of fitness prior to a SARS-CoV-2 infection. Our approach demonstrates the enormous potential in tracking the dynamics of physiological and physical activity under natural conditions in the context of infectious and chronic diseases. Funding This study was funded in part by funds from the overall funding program of the City of Vienna MA7. Funding was also received from the Federal Ministry of Health of Germany (Grants “Corona-Datenspende”, CD21, DS22 and DS23). Research in Context Evidence before this study: Previous research on persistent symptoms of the post-COVID-19 condition on heart rate and physical activity (measured in step count) often lacks a valid control group and/or information on the health status of individuals prior to the SARS-CoV-2 infection. The majority of studies have been conducted in clinical settings with a potential selection bias and do not account for post-COVID-19 conditions in the general population or its imprint on everyday life. Furthermore, knowledge on how lingering symptoms affect objectively measurable vital signals (such as heart rate and step count) in different phases of acute and post-acute infection regulation, is useful for enabling timely and targeted treatment interventions in clinical monitoring. Added value of this study: By incorporating detailed data obtained from wearables prior to, during, and after infection, including symptoms, overall wellbeing, and pre-existing health conditions, we could effectively identify and thoroughly characterize individuals with persistent COVID-19 symptoms. This unique advantage of our approach enhances the interpretation of phases in post-acute infection regulation. It further facilitates a comprehensive analysis of both, perceived and physiological health status, providing a multifaceted view of post-COVID-19 condition. Additionally, the characterization of patient demographics, comorbidities, and Quality of Life (QoL) enriches our understanding of the population at risk for developing persistent symptoms. Implications of the entire available evidence: We found that individuals experiencing persistent shortness of breath and fatigue, previously identified as core symptoms of post-COVID-19 condition, exhibit on average elevated resting heart rate (RHR), lower daily activity levels, lower QoL, and a higher count of pre-existing conditions already prior to an infection with SARS-CoV-2 compared to two control cohorts. In addition, the average decrease in RHR (bradycardia) during the acute phase of the infection was more pronounced and prolonged in those with persistent symptoms compared to the controls. These findings have helped to identify individuals at risk of developing persistent symptoms following SARS-CoV-2 infection and potentially assist tailoring diagnosis and treatment at an individual level. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded in part with funds from the funding program of the City of Vienna MA7 with no role of the study sponsor in study design, collection, analysis, interpretation of the data, writing of the report and decision to submit. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Participation in the study was voluntary and self-recruited. All individuals participating in the Corona Data Donation Project provided informed consent electronically via the app. Consent was provided separately for submitting vital data and participating in the in-app surveys. Participation is only possible for German residents age 16 and older and data is only stored pseudonymously, using a randomly generated unique user ID. Participant age is rounded to 5 years. The study is subject to strict compliance with the data protection provisions set out in the EU General Data Protection Regulation (GDPR) and the Federal Data Protection Act (BDSG). A comprehensive privacy impact assessment was conducted through an external law-firm specialized in e-Health and research projects. The study was reviewed and approved by the Data Privacy Officer at the Robert Koch Institute (internal operation number 2021-009) in agreement with the Federal Commissioner for Data Protection and Freedom of Information (BfDI), Germany's highest independent supreme federal authority for data protection and freedom of information. Ethical approval for this study was obtained from the ethics board at the University of Erfurt (approval number 20220414). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes

A considerable number of patients who contracted SARS-CoV-2 are affected by persistent multi-systemic symptoms, referred to as Post-COVID Condition (PCC). Post-exertional malaise (PEM) has been recognized as one of the most frequent manifestations of PCC and is a diagnostic criterion of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Yet, its underlying pathomechanisms remain poorly elucidated. In this review, we describe current evidence indicating that key pathophysiological features of PCC and ME/CFS are involved in physical activity-induced PEM. Upon physical activity, affected patients exhibit a reduced systemic oxygen extraction and oxidative phosphorylation capacity. Accumulating evidence suggests that these are mediated by dysfunctions in mitochondrial capacities and microcirculation that are maintained by latent immune activation, conjointly impairing peripheral bioenergetics. Aggravating deficits in tissue perfusion and oxygen utilization during activities cause exertional intolerance that are frequently accompanied by tachycardia, dyspnea, early cessation of activity and elicit downstream metabolic effects. The accumulation of molecules such as lactate, reactive oxygen species or prostaglandins might trigger local and systemic immune activation. Subsequent intensification of bioenergetic inflexibilities, muscular ionic disturbances and modulation of central nervous system functions can lead to an exacerbation of existing pathologies and symptoms.

One of the most important tools available to limit the spread and impact of infectious diseases is vaccination. It is therefore important to understand what factors determine people's vaccination decisions. To this end, previous behavioural research made use of, (i) controlled but often abstract or hypothetical studies (e.g., vignettes) or, (ii) realistic but typically less flexible studies that make it difficult to understand individual decision processes (e.g., clinical trials). Combining the best of these approaches, we propose integrating real-world Bluetooth contacts via smartphones in several rounds of a game scenario, as a novel methodology to study vaccination decisions and disease spread. In our 12-week proof-of-concept study conducted with $N$ = 494 students, we found that participants strongly responded to some of the information provided to them during or after each decision round, particularly those related to their individual health outcomes. In contrast, information related to others' decisions and outcomes (e.g., the number of vaccinated or infected individuals) appeared to be less important. We discuss the potential of this novel method and point to fruitful areas for future research.

As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75, and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.

Vaccines are among the most powerful tools to combat the COVID-19 pandemic. They are highly effective against infection and substantially reduce the risk of severe disease, hospitalization, ICU admission, and death. However, their potential for attenuating long-term changes in personal health and health-related wellbeing after a SARS-CoV-2 infection remains a subject of debate. Such effects can be effectively monitored at the individual level by analyzing physiological data collected by consumer-grade wearable sensors. Here, we investigate changes in resting heart rate, daily physical activity, and sleep duration around a SARS-CoV-2 infection stratified by vaccination status. Data were collected over a period of 2 years in the context of the German Corona Data Donation Project with around 190,000 monthly active participants. Compared to their unvaccinated counterparts, we find that vaccinated individuals, on average, experience smaller changes in their vital data that also return to normal levels more quickly. Likewise, extreme changes in vitals during the acute phase of the disease occur less frequently in vaccinated individuals. Our results solidify evidence that vaccines can mitigate long-term detrimental effects of SARS-CoV-2 infections both in terms of duration and magnitude. Furthermore, they demonstrate the value of large-scale, high-resolution wearable sensor data in public health research.

Abstract After the winter of 2021/2022, the coronavirus disease 2019 (COVID-19) pandemic had reached a phase where a considerable number of people in Germany have been either infected with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, vaccinated or both, the full extent of which was difficult to estimate, however, because infection counts suffer from under-reporting, and the overlap between the vaccinated and recovered subpopulations is unknown. Yet, reliable estimates regarding population-wide susceptibility were of considerable interest: Since both previous infection and vaccination reduce the risk of severe disease, a low share of immunologically naïve individuals lowers the probability of further severe outbreaks, given that emerging variants do not escape the acquired susceptibility reduction. Here, we estimate the share of immunologically naïve individuals by age group for each of the sixteen German federal states by integrating an infectious-disease model based on weekly incidences of SARS-CoV-2 infections in the national surveillance system and vaccine uptake, as well as assumptions regarding under-ascertainment. We estimate a median share of 5.6% of individuals in the German population have neither been in contact with vaccine nor any variant up to 31 May 2022 (quartile range [2.5%–8.5%]). For the adult population at higher risk of severe disease, this figure is reduced to 3.8% [1.6%–5.9%] for ages 18–59 and 2.1% [1.0%–3.4%] for ages 60 and above. However, estimates vary between German states mostly due to heterogeneous vaccine uptake. Excluding Omicron infections from the analysis, 16.3% [14.1%–17.9%] of the population in Germany, across all ages, are estimated to be immunologically naïve, highlighting the large impact the first two Omicron waves had until the beginning of summer in 2022. The method developed here might be useful for similar estimations in other countries or future outbreaks of other infectious diseases.

Abstract Background Animals are expected to adjust their social behaviour to cope with challenges in their environment. Therefore, for fish populations in temperate regions with seasonal and daily environmental oscillations, characteristic rhythms of social relationships should be pronounced. To date, most research concerning fish social networks and biorhythms has occurred in artificial laboratory environments or over confined temporal scales of days to weeks. Little is known about the social networks of wild, freely roaming fish, including how seasonal and diurnal rhythms modulate social networks over the course of a full year. The advent of high-resolution acoustic telemetry enables us to quantify detailed social interactions in the wild over time-scales sufficient to examine seasonal rhythms at whole-ecosystems scales. Our objective was to explore the rhythms of social interactions in a social fish population at various time-scales over one full year in the wild by examining high-resolution snapshots of a dynamic social network. Methods To that end, we tracked the behaviour of 36 adult common carp, Cyprinus carpio, in a 25 ha lake and constructed temporal social networks among individuals across various time-scales, where social interactions were defined by proximity. We compared the network structure to a temporally shuffled null model to examine the importance of social attraction, and checked for persistent characteristic groups over time. Results The clustering within the carp social network tended to be more pronounced during daytime than nighttime throughout the year. Social attraction, particularly during daytime, was a key driver for interactions. Shoaling behavior substantially increased during daytime in the wintertime, whereas in summer carp interacted less frequently, but the interaction duration increased. Therefore, smaller, characteristic groups were more common in the summer months and during nighttime, where the social memory of carp lasted up to two weeks. Conclusions We conclude that social relationships of carp change diurnally and seasonally. These patterns were likely driven by predator avoidance, seasonal shifts in lake temperature, visibility, forage availability and the presence of anoxic zones. The techniques we employed can be applied generally to high-resolution biotelemetry data to reveal social structures across other fish species at ecologically realistic scales.

Intro: Controlling the spread of infectious diseases requires correctly targeting preventative resources. When poorly deployed, these resources are misspent and have an inefficient impact. In a dynamic environment where sources of outbreaks are ever-changing, how to optimally deploy these resources is difficult to identify and can moreover change over time. In addressment, we outline and test network-driven framework that accounts for underlying patient mobility, and outbreak dynamics in hospitals to predict the temporal and spatial arrival of carbapenemase-producing Enterobacteriaceae (CPE) outbreaks. Methods: We reconstructed CPE-outbreaks using a novel formulation based on transmission-dynamics, contact-interactions, and microbiology data. For each outbreak, we then examine their spatial evolution and, using background hospital population movement (entire patient population from Imperial College Healthcare NHS Trust between 2018-08-17 and 2022-02-03), we predict the arrival times of new CPE cases across wards. Findings: The background mobility-network contained ward-transitions from 181,512 patients (178 wards). Based on the construction, the network comprises a single giant component and acts as a medium for disease transmission. For the results of a fitted regression predicting outbreak arrival, we included locational attributes, in addition to network distances. Overall, we found that effective distance (a graphbased path measure shown previously epidemiologically predictive) contained unique predictive power compared to edge weight. However, the effective distance could be complemented by information regarding patient demographics (Age and Sex) of patient transfers; their predictive power is suggestive of specific sub-population mobility as more important drivers of CPE. Conclusion: We investigated a network-driven framework showing the potential to anticipate arrival-times of hospital CPE outbreaks. In including additional information, we also showed how specific hospital population movements were key drivers of CPE. In furthering our results, we next plan to investigate additional diseases, validate our findings beyond our current dataset, and explore further locational attributes.

In November 2021, the first infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VOC) B.1.1.529 ('Omicron') was reported in Germany, alongside global reports of reduced vaccine efficacy (VE) against infections with this variant. The potential threat posed by its rapid spread in Germany was, at the time, difficult to predict. We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered infectious-disease model that included information about variant-specific and waning VEs based on empirical data available at the time. Compared to other approaches, our method aimed for minimal structural and computational complexity and therefore enabled us to respond to changes in the situation in a more agile manner while still being able to analyze the potential influence of (non-)pharmaceutical interventions (NPIs) on the emerging crisis. Thus, the model allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs), the efficacy of contact reduction strategies, and the success of the booster vaccine rollout campaign. We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity, nevertheless, even without additional NPIs. The projected figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid-February and mid-March. Most surprisingly, our model showed that early, strict, and short contact reductions could have led to a strong 'rebound' effect with high incidences after the end of the respective NPIs, despite a potentially successful booster campaign. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.

Die Daten von Fitnessarmbändern und Smartwatches, sogenannten Wearables, können Hinweise auf Symptome einer Infektion mit COVID-19 liefern. Mit Hilfe der Corona-Datenspende-App (CDA) können Bürger:innen dem Robert Koch-Institut diese Daten zur wissenschaftlichen Auswertung zur Verfügung stellen. Zusammen mit Informationen aus anderen Quellen, z.B. offiziellen Meldedaten zu Fallzahlen, helfen diese Daten den Wissenschaftler:innen, die Ausbreitung des Coronavirus besser zu erfassen und zu verstehen. In ihrer ersten, seit April 2020 verfügbaren, Version erhob die CDA nur sogenannte Vitaldaten der Nutzer:innen, insbesondere den Ruhepuls, körperliche Aktivität und Schlafverhalten. In einem Update im Oktober 2021 wurden der App Umfragemodule zu verschiedenen, für die Pandemieforschung relevanten Fragestellungen, hinzugefügt. Die hier bereitgestellten Daten entstammen der Teilstudie "Erleben und Verhalten in der Pandemie" die momentan als eines von drei Befragungsmodulen innerhalb der CDA durchgeführt wird. Sie basiert auf dem COVID-19 Snapshot Monitoring (COSMO), einem sich wiederholenden querschnittlichen Monitoring von Wissen, Risikowahrnehmung, Schutzverhalten und Vertrauen während des aktuellen COVID-19 Ausbruchsgeschehens (Betsch et al., 2022). In dieser Studie wollen das Robert Koch-Institut sowie führende Verhaltensforscher:innen erfahren, wie sich die Bevölkerung unter pandemischen Bedingungen verhält. Durch die Beantwortung regelmäßiger Fragebögen können Teilnehmer:innen dabei helfen, Strategien zur Bekämpfung des Coronavirus zu optimieren. Die Forscher:innen wollen hierzu mehr über den Arbeitsalltag, die persönliche Belastung und die Risikowahrnehmung lernen. Die hier bereitgestellten Daten dienen der Reproduktion aller Ergebnisse in der Studie "From Delta to Omicron: The role of individual factors and social context in compliance with pandemic regulations and recommendations" (Sprengholz et al., 2022, in Begutachtung). Es werden hier daher jene Datenpunkte aus der Studie "Erleben und Verhalten in der Pandemie" bereitgestellt, die zu diesem Zweck benötigt werden. Das heißt auch, dass nicht alle im Rahmen dieser Studie erhobenen Datenpunkte, beispielsweise Informationen zu COVID-19-Testergebnissen, sowie alle Zeitpunkte der Erhebung im vorliegenden Datensatz enthalten sind. Ein Link zur oben genannten Publikation folgt nach der Veröffentlichung in einer Fachzeitschrift.

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 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 nextgeneration 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.

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.