Nirmatrelvir/Ritonavir is an oral treatment for mild to moderate COVID-19 cases with a high risk for a severe course of the disease. For this paper, a comprehensive literature review was performed, leading to a summary of currently available data on Nirmatrelvir/Ritonavir’s ability to reduce the risk of progressing to a severe disease state. Herein, the focus lies on publications that include comparisons between patients receiving Nirmatrelvir/Ritonavir and a control group. The findings can be summarized as follows: Data from the time when the Delta-variant was dominant show that Nirmatrelvir/Ritonavir reduced the risk of hospitalization or death by 88.9% for unvaccinated, non-hospitalized high-risk individuals. Data from the time when the Omicron variant was dominant found decreased relative risk reductions for various vaccination statuses: between 26% and 65% for hospitalization. The presented papers that differentiate between unvaccinated and vaccinated individuals agree that unvaccinated patients benefit more from treatment with Nirmatrelvir/Ritonavir. However, when it comes to the dependency of potential on age and comorbidities, further studies are necessary. From the available data, one can conclude that Nirmatrelvir/Ritonavir cannot substitute vaccinations; however, its low manufacturing cost and easy administration make it a valuable tool in fighting COVID-19, especially for countries with low vaccination rates.

This study conducts a detailed analysis of population mobility during the COVID-19 pandemic, utilizing a unique approach that contrasts two types of mobile phone data: GPS-based and cell-based. The primary objective is to evaluate the effects of governmental restrictions on a variety of activities including school attendance, work, shopping, and leisure. We compare both data sets by using a set of defined criteria, including anticipated activity reductions during full and partial closures, as well as the timing of activity changes in response to policy implementations. Our research reveals that while cell-based data lacks the precision to differentiate between various out-of-home activities effectively, GPS-based data, especially when integrated with OpenStreetMap, proves significantly more adept at identifying and categorizing specific activity types. The GPS-based data shows, for example, that school activities fell by more than 80% while work activities were only reduced by around 50%.

Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to $$70\% \cdot 70\% = 49\%$$70 % · 70 % = 49 % of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero.

Background: Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods: We compared July 2022 projections from the European COVID-19 Scenario Modelling Hub. Five modelling teams projected incidence in Belgium, the Netherlands, and Spain. We compared projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results: By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions: We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts. Data availability: All code and data available on Github: https://github.com/covid19-forecast-hub-europe/aggregation-info-loss

Background: After COVID-19 was declared a pandemic by the WHO in March 2020, global responses relied heavily on non-pharmaceutical interventions such as physical distancing and mask mandates. These measures were guided by mathematical models built on empirical data. Although traditional methods such as surveys and observational studies provide high-quality data, they are often slow and resource-intensive. Social media polls (SMPs) offer a faster, more cost-effective alternative. This study evaluates the feasibility of SMPs as a rapid supplementary tool for collecting epidemiological data and compares their representativeness and quality with conventional approaches. Methods: In this cross-sectional observational study in Germany, we utilized SMPs to collect data on infections and demographic attributes via Twitter and Mastodon. To assess data quality, SMP results were compared with conventional data sources, including the Multilocal and Serial Prevalence Study of Antibodies Against Respiratory Infectious Diseases (MuS-PAD), COVID-19 Snapshot Monitoring (COSMO) survey, official Robert-Koch-Institute reports, and German Federal Statistical Office demographics. The timeframe covered was from 2019 to 2024. Data were analyzed for infection rates, sociodemographic representativeness, and overall data quality, employing descriptive statistics. Findings: SMPs demonstrated feasibility as a rapid data collection tool. Self-reported infection frequency aligned closely with conventional sources such as MuSPAD, with similar proportions of respondents reporting zero, one, or multiple infections. However, demographic analyses revealed biases: individuals aged 40–59 and those with higher education were over-represented, while one-person households were underrepresented. We used bootstrapping to address these issues, indicating that the effect of sampling bias on overall infection numbers was low. By design, SMPs do not provide detailed demographic data, limiting options for subgroup analyses. Interpretation: We found SMPs to be a practical and cost-effective method for quickly gathering epidemiological insights. In particular, self-reported infection frequency can aid during a period of high availability of self-testing during epidemics. One can argue that SMPs alone are insufficient for comprehensive public health modeling, as they do not allow real-time monitoring of, e.g., serological indicator-based population-based infection frequency estimates. However, they complement traditional methods by providing near-real-time, cost-effective data to guide interventions, inform policymaking, and refine epidemiological models. Further refinement and integration with established approaches could enhance their utility for public health decision-making. Funding: This study was conducted within the infoXpand project, which is funded by the German Federal Ministry of Education and Research (Funding Code: 031L0300A, 031L0300C, 031L0300D). This work was additionally supported by The Helmholtz Association, the European Union’s Horizon 2020 research and innovation program (Grant Number 101003480), and intramural funds of the Helmholtz Center for Infection Research.