Modeling the impact of the Omicron infection wave in Germany.

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.

Finding disease outbreak locations from human mobility data.

Finding the origin location of an infectious disease outbreak quickly is crucial in mitigating its further dissemination. Current methods to identify outbreak locations early on rely on interviewing affected individuals and correlating their movements, which is a manual, time-consuming, and error-prone process. Other methods such as contact tracing, genomic sequencing or theoretical models of epidemic spread offer help, but they are not applicable at the onset of an outbreak as they require highly processed information or established transmission chains. Digital data sources such as mobile phones offer new ways to find outbreak sources in an automated way. Here, we propose a novel method to determine outbreak origins from geolocated movement data of individuals affected by the outbreak. Our algorithm scans movement trajectories for shared locations and identifies the outbreak origin as the most dominant among them. We test the method using various empirical and synthetic datasets, and demonstrate that it is able to single out the true outbreak location with high accuracy, requiring only data of N = 4 individuals. The method can be applied to scenarios with multiple outbreak locations, and is even able to estimate the number of outbreak sources if unknown, while being robust to noise. Our method is the first to offer a reliable, accurate out-of-the-box approach to identify outbreak locations in the initial phase of an outbreak. It can be easily and quickly applied in a crisis situation, improving on previous manual approaches. The method is not only applicable in the context of disease outbreaks, but can be used to find shared locations in movement data in other contexts as well. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1140/epjds/s13688-021-00306-6.

Trends in respiratory virus circulation following COVID-19-targeted nonpharmaceutical interventions in Germany, January - September 2020: Analysis of national surveillance data.

BACKGROUND: During the initial COVID-19 response, Germany's Federal Government implemented several nonpharmaceutical interventions (NPIs) that were instrumental in suppressing early exponential spread of SARS-CoV-2. NPI effect on the transmission of other respiratory viruses has not been examined at the national level thus far. METHODS: Upper respiratory tract specimens from 3580 patients with acute respiratory infection (ARI), collected within the nationwide German ARI Sentinel, underwent RT-PCR diagnostics for multiple respiratory viruses. The observation period (weeks 1-38 of 2020) included the time before, during and after a far-reaching contact ban. Detection rates for different viruses were compared to 2017-2019 sentinel data (15350 samples; week 1-38, 11823 samples). FINDINGS: The March 2020 contact ban, which was followed by a mask mandate, was associated with an unprecedented and sustained decline of multiple respiratory viruses. Among these, rhinovirus was the single agent that resurged to levels equalling those of previous years. Rhinovirus rebound was first observed in children, after schools and daycares had reopened. By contrast, other nonenveloped viruses (i.e. gastroenteritis viruses reported at the national level) suppressed after the shutdown did not rebound. INTERPRETATION: Contact restrictions with a subsequent mask mandate in spring may substantially reduce respiratory virus circulation. This reduction appears sustained for most viruses, indicating that the activity of influenza and other respiratory viruses during the subsequent winter season might be low,whereas rhinovirus resurgence, potentially driven by transmission in educational institutions in a setting of waning population immunity, might signal predominance of rhinovirus-related ARIs. FUNDING: Robert Koch-Institute and German Ministry of Health.

COVID-19 lockdown induces disease-mitigating structural changes in mobility networks.

In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural changes in the mobility network. We find that long-distance travel was reduced disproportionately strongly. The trimming of long-range network connectivity leads to a more local, clustered network and a moderation of the "small-world" effect. We demonstrate that these structural changes have a considerable effect on epidemic spreading processes by "flattening" the epidemic curve and delaying the spread to geographically distant regions.