Michael Höhle

Background: In Germany, primary vaccination against invasive meningococcal disease (IMD) serogroup C aims to reduce the highest burden of IMD in infants aged 12–23 month. Due to another IMD-peak in adolescents, we modelled the potential impact of introducing adolescent boosters with conjugate meningococcal C or ACWY (MenC/MenACWY) vaccines. Methods: We built an age- and serogroup-structured dynamic-transmission model for Germany, which we calibrated to national surveillance data in 2005–2019. We simulated five vaccination scenarios of either continuing with the current MenC primary vaccination (scenario 1), or additionally introducing MenC or MenACWY boosters at age 13 years (scenarios 2–3) or 16 years (scenarios 4–5). We performed comprehensive sensitivity analyses, including on the protection against carriage and serogroup replacement. Results: The calibrated model projected for scenario 1 an annual mean of 243 (95%-uncertainty interval: 220–258) expected IMD cases over a 10-year period. Introducing the MenC booster prevented an estimated 5 (3.9–6.7) and the MenACWY booster 8 (6.7–9.1) IMD cases per year on average (scenario 2 and 3). The number-needed-to-vaccinate (NNVs) to prevent one IMD case were 140,000 (100,000-180,000) and 91,000 (76,000-100,000), respectively. To prevent one sequela or death, NNVs were higher (i.e., less efficient). Results were broadly similar for scenarios 4–5. Simulations suggested relevant serogroup replacement starting eight-to-ten years after introducing the MenACWY booster. Conclusions: Introducing adolescent MenC or MenACWY boosters marginally reduces the expected IMD burden in Germany. Effectiveness and efficiency of evaluated strategies depend on future incidence. The magnitude of future serogroup replacement for the MenACWY vaccine is highly uncertain.

Background In Germany, primary vaccination against invasive meningococcal disease (IMD) serogroup C aims to reduce the highest burden of IMD in infants aged 12-23 month. Due to another IMD-peak in adolescents, we modelled the potential impact of introducing adolescent boosters with conjugate meningococcal C or ACWY (MenC/MenACWY) vaccines. Methods We built an age- and serogroup-structured dynamic-transmission model for Germany, which we calibrated to national surveillance data in 2005-2019. We simulated five vaccination scenarios of either continuing with the current MenC primary vaccination (scenario 1), or additionally introducing MenC or MenACWY boosters at age 13 years (scenarios 2-3) or 16 years (scenarios 4-5). We performed comprehensive sensitivity analyses, including on the protection against carriage and serotype replacement. Results The calibrated model projected for scenario 1 an annual mean of 243 (95%-uncertainty interval: 220-258) expected IMD cases over a 10-year period. Introducing the MenC booster prevented an estimated 5 (3.9-6.7) and the MenACWY booster 8 (6.7-9.1) IMD cases per year on average (scenario 2 and 3). The number-needed-to-vaccinate (NNVs) to prevent one IMD case were 140,000 (100,000-180,000) and 91,000 (76,000-100,000), respectively. To prevent one sequela or death, NNVs were higher (i.e., less efficient). Results were broadly similar for scenarios 4-5. Simulations suggested relevant serotype replacement starting eight-to-ten years after introducing the MenACWY booster. Conclusions Introducing adolescent MenC or MenACWY boosters marginally reduces the expected IMD burden in Germany. Effectiveness and efficiency of evaluated strategies depend on future incidence. The magnitude of future serotype replacement for the MenACWY vaccine is highly uncertain.

Background Globally, since 1 January 2020 and as of 24 January 2023, there have been over 664 million cases of COVID-19 and over 6.7 million deaths reported to WHO. WHO developed an evidence-based alert system, assessing public health risk on a weekly basis in 237 countries, territories and areas from May 2021 to June 2022. This aimed to facilitate the early identification of situations where healthcare capacity may become overstretched. Methods The process involved a three-stage mixed methods approach. In the first stage, future deaths were predicted from the time series of reported cases and deaths to produce an initial alert level. In the second stage, this alert level was adjusted by incorporating a range of contextual indicators and accounting for the quality of information available using a Bayes classifier. In the third stage, countries with an alert level of ‘High’ or above were added to an operational watchlist and assistance was deployed as needed. Results Since June 2021, the system has supported the release of more than US$27 million from WHO emergency funding, over 450 000 rapid antigen diagnostic testing kits and over 6000 oxygen concentrators. Retrospective evaluation indicated that the first two stages were needed to maximise sensitivity, where 44% (IQR 29%–67%) of weekly watchlist alerts would not have been identified using only reported cases and deaths. The alerts were timely and valid in most cases; however, this could only be assessed on a non-representative sample of countries with hospitalisation data available. Conclusions The system provided a standardised approach to monitor the pandemic at the country level by incorporating all available data on epidemiological analytics and contextual assessments. While this system was developed for COVID-19, a similar system could be used for future outbreaks and emergencies, with necessary adjustments to parameters and indicators.

The real-time analysis of infectious disease surveillance data is essential in obtaining situational awareness about the current dynamics of a major public health event such as the COVID-19 pandemic. This analysis of e.g., time-series of reported cases or fatalities is complicated by reporting delays that lead to under-reporting of the complete number of events for the most recent time points. This can lead to misconceptions by the interpreter, for instance the media or the public, as was the case with the time-series of reported fatalities during the COVID-19 pandemic in Sweden. Nowcasting methods provide real-time estimates of the complete number of events using the incomplete time-series of currently reported events and information about the reporting delays from the past. In this paper we propose a novel Bayesian nowcasting approach applied to COVID-19-related fatalities in Sweden. We incorporate additional information in the form of time-series of number of reported cases and ICU admissions as leading signals. We demonstrate with a retrospective evaluation that the inclusion of ICU admissions as a leading signal improved the nowcasting performance of case fatalities for COVID-19 in Sweden compared to existing methods.

Background: The European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts. Aim: This study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats. Methods: We calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and accuracy and performance of tweet geolocation and signal detection algorithms obtained from epitweetr and the manual monitoring of 1,200 tweets were compared. Results: The epitweetr geolocation algorithm had an accuracy of 30.1% at national, and 25.9% at subnational levels. The signal detection algorithm had 3.0% general PPV and 74.6% specific PPV. Compared to manual monitoring, epitweetr had greater sensitivity (47.9% and 78.6%, respectively), and reduced PPV (97.9% and 74.6%, respectively). Median validation time difference between 16 common events detected by epitweetr and manual monitoring was -48.6 hours (IQR: −102.8 to −23.7). Conclusion: Epitweetr has shown sufficient performance as an early warning tool for public health threats using Twitter data. Since epitweetr is a free, open-source tool with configurable settings and a strong automated component, it is expected to increase in usability and usefulness to public health experts.

We comment the paper by Jahn et al. (On the role of data, statistics and decisions in a pandemic, 2022).

Background: For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. We study a setting where the decision to classify hospitals as performance outliers or non-outliers is based on the observed result of a single binary quality indicator. Methods: We propose to embed the classification of providers into a Bayesian decision theoretical framework which enables the derivation of optimal decision rules with respect to the expected decision consequences. We argue that these consequences depend upon for which pathway to quality improvement the profiling of hospitals takes place. We propose paradigmatic utility functions for the two pathways external reporting and change in care delivery and compare the resulting optimal decision rules with regard to their threshold values, sensitivity and specificity. We further apply them to the area of hip replacement surgeries by analyzing data from the mandatory German hospital profiling program. Based on five quality indicators, we re-evaluate the performance of 1,277 hospitals which treated over 180,000 patients for hip-replacement surgeries during 2017. Results: Based on the utilities we assigned to the classification decisions, the decision rule for change in care delivery classifies more high-volume providers as outliers compared to the decision rule for external reporting, with consequences for both sensitivity and specificity. The re-evaluation of the five quality indicators illustrates that classification decisions are highly dependent upon the underlying utilities. Conclusion: Analyzing the classification of hospitals as a decision theoretic problem and considering pathway-specific consequences of decisions can help to derive an appropriate decision rule. Contrasting decision rules with regard to their underlying assumptions about the utilities of classification consequences can be helpful to make implicit assumptions transparent and justifiable.

Summary: Background: ECDC performs epidemic intelligence activities to systematically collate information from a variety of sources, including Twitter, to rapidly detect public health events. The lack of a freely available, customisable and automated early warning tool using Twitter data, prompted ECDC to develop epitweetr. The specific objectives are to assess the performance of the geolocation and signal detection algorithms used by epitweetr and to assess the performance of epitweetr in comparison with the manual monitoring of Twitter for early detection of public health threats. Methods: Epitweetr collects, geolocates and aggregates tweets to generate signals and email alerts. Firstly, we evaluated manually the tweet geolocation characteristics of 1,200 tweets, and assessed its accuracy in extracting the correct location and its performance in detecting tweets with available information on the tweet geolocation. Secondly, we evaluated signals generated by epitweetr between 19 October and 30 November 2020 and we calculated the positive predictive value (PPV). Then, we evaluated the sensitivity, specificity and timeliness of epitweetr in comparison with Twitter manual monitoring. Findings: The epitweetr geolocation algorithm had an accuracy of 30.1% and 25.9% at national and subnational levels, respectively. General and specific PPV of the signal detection algorithm was 3.0% and 74.6%, respectively. Epitweetr and/or manual monitoring detected 570 signals and 454 events. Epitweetr had a sensitivity of 78.6% [75.2% - 82.0%] and PPV of 74.6% [70.5% - 78.6%]; and the manual monitoring had a sensitivity of 47.9% [43.8% - 52.0%] and PPV of 97.9% [95.8% - 99.9%]. The median validation time difference between sixteen common events detected by epitweetr and manual monitoring was −48.6 hours [(−102.8) - (−23.7) hours]. Interpretation: Epitweetr has shown to have sufficient performance as an early warning tool for public health threats using Twitter data. Having developed epitweetr as a free, open-source tool with several configurable settings and a strong automated component, it is expected to increase its usability and usefulness to public health experts. Funding: Not applicable Research in context: Evidence before this study: Previous reviews have shown how social media, including Twitter, have been used for public health purposes. Most recent studies, in relation to the COVID-19 pandemic, have shown the added value of early warning tools based on Twitter and other social media platforms. They also noted the lack of an open-source tool for real-time monitoring and surveillance. Added value of this study: Epitweetr is a free, open-source and R-based early warning tool for automatic Twitter data monitoring that will support public health experts in rapidly detecting public health threats. The evaluation of epitweetr presented in this study shows the strengths of the tool which include having good performance, high degree of automation, being a near-real-time tool and being publicly available with various customisable settings. Furthermore, it shows which are the areas of improvement for the next versions of epitweetr. Implications of all the available evidence: This tool can be further developed to include more automation and machine learning components to increase usability and information processing time by users.

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

To assess the current dynamics of an epidemic, it is central to collect information on the daily number of newly diseased cases. This is especially important in real‐time surveillance, where the aim is to gain situational awareness, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred‐but‐not‐yet‐reported events. Here, we present a novel application of nowcasting to data on the current COVID‐19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time‐varying case reproduction number based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID‐19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current pandemic, and evaluate the model based on synthetic and retrospective data on COVID‐19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.