Background: Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, data sharing, and the application of computational methods, including artificial intelligence. These barriers complicate collaboration across regions and organizations and hinder technological progress in public health. Objective: This study aims to propose and release the first open, machine-readable format for representing case and syndrome definitions, together with tools and resources that enable their standardized and scalable use. Methods: We developed the Open Syndrome Definition, a structured, machine-readable schema for representing case and syndrome definitions. We compiled official public health case definitions from multiple institutions and converted them into standardized, machine-readable representations using open-source tools. These tools, available through GitHub under the Massachusetts Institute of Technology license, automate the translation of narrative definitions into structured data. We also created a platform for browsing, analyzing, and contributing new definitions on our initiative website. Results: The Open Syndrome Definition format enabled consistent, automated representation of case definitions across different diseases and jurisdictions. The conversion tools achieved high semantic fidelity, as assessed by qualitative expert review, between narrative and structured representations, supporting human verification and automated analysis. The dataset and accompanying tools demonstrated structural and semantic interoperability by standardizing definitions from various health systems into a unified format and integrating existing medical ontologies through JSON for Linked Data. To further illustrate practical applicability and downstream usage, we introduced a data filtering prototype that allows users to upload their own datasets and verify the results against the standardized definitions. Conclusions: The Open Syndrome Definition establishes a foundation for consistent and machine-readable public health definitions, facilitating reproducible research and interoperability at scale. By enabling systematic data exchange and artificial intelligence-driven analysis, it strengthens public health preparedness and supports more rapid, coordinated responses to emerging health threats.

Background: Mass gathering events (MGEs) are associated with several public health challenges and may cause a strain on healthcare services. Literature findings on the impact of MGEs on emergency departments (EDs) are heterogeneous. Objectives: To examine shifts in ED attendance characteristics during a major sporting tournament, namely the UEFA European Football Championship 2024 held in Germany. Methods: We conducted a retrospective observational study using ED data from the Emergency Department Data Registry. We compared baseline ED attendance characteristics between the tournament and the reference period, defined as two weeks before and two weeks after the tournament, and between Germany game days and non-Germany game days. Hourly attendance patterns were analysed for all Germany games using a reference range. Results: We included data from 41 EDs, totalling 253,493‬ attendances during the study period. A 1.57% increase in attendance was observed during the tournament compared to the reference period, with baseline characteristics remaining similar. The median daily attendance within all EDs was slightly lower on Germany game days (4066) compared to non-Germany game days (4128). Modest changes were observed in the hourly attendance on Germany game days, most notable during the last Germany game where a decrease in attendance below the reference range extended over three hours. Conclusions: The observed shifts in ED attendance were minimal, suggesting that no major changes of public health relevance occurred in ED attendance during the tournament. We highlight the utility of using ED data for monitoring and for enhancing the understanding of the public health risks and challenges associated with MGEs. ### Competing Interest Statement The authors have declared no competing interest. ### 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: The Emergency Department Data Registry received medical ethical approval from the Ethics Committee of the Otto von Guericke University Magdeburg, Medical Faculty (160/15). For this study, data usage approval was obtained from the AKTIN Data Use and Access Committee (Data access request ID 2019-003, 3rd amendment). 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 Aggregated data can be made available upon request after agreement with the Emergency Department Data registry.

Automated outbreak detection can enhance infectious disease surveillance by enabling early identification of outbreaks and supporting timely public health measures. However, information on its current use by national public health institutes (NPHI) remains limited. This paper provides an updated and extended overview of automated outbreak detection usage in the European Union (EU) and United Kingdom (UK). Key findings were gathered through the Joint Action United4Surveillance via an online survey of 21 countries, an in-presence workshop, and online meetings with NPHI, focusing on three objectives: assessing current demand for automated outbreak detection, examining the availability of necessary prerequisites within existing surveillance systems, and identifying challenges and requirements for implementation. Findings indicate that seven countries currently have automated outbreak detection systems (AODS) in place. While many countries have sufficient surveillance data and a clear demand for automated outbreak detection, adoption is often limited by constrained funding and lack of IT resources. While the specific methods in existing AODS differ, overall demands and outputs are similar, suggesting a single tool could serve multiple countries. Capacity building as part of EU-funded Joint Actions can bridge these gaps by developing sustainable tools and fostering cross-country collaboration. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was conducted as part of the project UNITED4Surveillance, co-funded by the European Union under the EU4Health Programme (EU4H), project ID 101102070. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authority. Neither the European Union nor the granting authority can be held responsible for them. ### 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: Data protection procedures were discussed with the relevant institutional representatives: Robert Koch-Institute, Datenschutzbeauftragte / Data protection officer Nordufer 20, 13353 Berlin, Germany Email: datenschutz{at}rki.de As the study did not involve the collection or processing of personal data and used only aggregated country-level data, review by an ethics oversight body was not required and ethical approval was formally waived/not necessary under institutional guidelines. 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 All data produced in the present study are available upon reasonable request to the authors.

Background:Emergency department (ED) routine data offer a unique opportunity for syndromic surveillance of communicable and noncommunicable diseases (NCDs). In 2020, the Robert Koch Institute established a syndromic surveillance system using ED data from the AKTIN registry. The system provides daily insights into ED utilization for infectious diseases. Adding NCD indicators to the surveillance is of great public health importance, especially during acute events, where timely monitoring enables targeted public health responses and communication. Objective:This study aimed to develop and validate syndrome definitions for the NCD indicators of acute coronary syndrome (ACS), myocardial infarction (MI), and stroke (STR). Methods:First, syndrome definitions were developed with clinical experts combining ED diagnosis, chief complaints, diagnostic certainty, and discharge information. Then, using the multicenter retrospective routine ED data provided by the AKTIN registry, we conducted internal validation by linking ED cases fulfilling the syndrome definition with the hospital discharge diagnoses and calculating sensitivity, specificity, and accuracy. Lastly, external validation comprised the comparison of the ED cases fulfilling the syndrome definition with the federal German hospital diagnosis statistic. Ratios comparing the relative number of cases for all syndrome definitions were calculated and stratified by age and sex. Results:We analyzed data from 9 EDs, totaling 704,797 attendances from January 1, 2019, to March 5, 2021. Syndrome definitions were based on ICD-10 (International Statistical Classification of Diseases and Related Health Problems 10th Revision-German Modification) diagnoses, chief complaints, and discharge information. We identified 4.3% of all cases as ACS, 0.6% as MI, and 3.2% as STR. Patients with ACS and MI were more likely to be male (58.3% and 64.7%), compared to the overall attendances (52.7%). For all syndrome definitions, the prevalence was higher in the older age groups (60-79 years and >80 years), and the highest proportions of cases were assigned an urgency level (3=urgent or 2=very urgent). The internal validation showed accuracy and specificity levels above 96% for all syndrome definitions. The sensitivity was 85.3% for ACS, 56.6% for MI, and 80.5% for STR. The external validation showed high levels of correspondence between the ED data and the German hospital statistics, with most ratios ranging around 1, indicating congruence, particularly in older age groups. The highest differences were noted in younger age groups, with the highest ratios in women aged between 20 and 39 years (4.57 for MI and 4.17 for ACS). Conclusions:We developed NCD indicators for ACS, MI, and STR that showed high levels of internal and external validity. The integration of these indicators into the syndromic surveillance system for EDs could enable daily monitoring of NCD patterns and trends to enhance timely public health surveillance in Germany.

Early warning systems (EWSs) for detecting disease outbreaks can help make informed public health decisions and organize necessary responses. During the COVID-19 pandemic, several EWSs were proposed that use covariates such as mobility or social media data for improved timeliness and precision. Evaluating these EWSs is not trivial, since we do not have the ground truth knowledge about outbreaks of COVID-19. Workarounds for missing labels are to simulate them or produce them post hoc. Simulating COVID-19 outbreaks for evaluation is not feasible with highly complex covariates such as mobility. Furthermore, existing post hoc labeling methods do not perform well on heterogeneous COVID-19 time series. To address this evaluation gap, we propose an adaptive labeling method that produces useful labels (time-indexed annotations marking outbreak-like periods) for highly heterogeneous, nonstationary COVID-19 time series. To this end, we develop a customized ensemble of labeling methods. We find that our method consistently produces useful labels for various outbreak types, such as waves and short peaks occurring at different spatial resolutions. Lastly, we use our self-produced labels to train machine learning models and compare their performance with traditional outbreak detection methods. We find that models trained with our labels outperform classical, unsupervised outbreak detection algorithms.

Whole-genome sequencing (WGS) is increasingly used as the primary typing method for foodborne disease surveillance. It offers high-resolution cluster analysis, interoperability, and comprehensive pathogen characterization. However, implementing WGS-based foodborne surveillance also poses challenges. This paper outlines these challenges and provides practical recommendations. It requires a business plan that details the financial, technical and human resources needed, since setting up WGS-based surveillance requires substantial initial investments. During the initial phase, the per sample costs of WGS are likely higher than with traditional typing method. However, this will align or even go below that when fully transitioned to WGS-based surveillance because WGS data can be used for multiple purposes such as (sero)typing and antimicrobial and virulence characterization. It is advisable to start with a single pathogen to establish a solid foundation, with the aim of having one institutional sequencing facility. Validating accuracy and consistency of results is crucial before expanding to other pathogens. While cross-disciplinary collaboration has always played an important role in foodborne surveillance, the complexity of WGS results now makes it essential for transforming findings into effective interventions. Despite its challenges, advancements in technology and computation capabilities have made it increasingly accessible, ultimately improving public health surveillance and response.

Emergency department (ED) routine data represent a unique opportunity for syndromic surveillance for both communicable and non-communicable diseases. In 2020 the Robert Koch Institute (RKI) established a syndromic surveillance system, using ED data from the AKTIN registry. In this context, this study aims to develop and validate syndrome definitions for acute coronary syndrome (ACS), myocardial infarction (MI), and stroke (STR). First, syndrome definitions including ED diagnosis, chief complaints, diagnostic certainty and disposition were developed with clinical experts. Then, using the retrospective routine ED data provided by the AKTIN registry, we conducted internal validation by linking ED cases fulfilling the syndrome definition criteria with hospital discharge diagnoses and calculating sensitivity, specificity, and accuracy. Lastly, external validation comprised the comparison of the ED cases fulfilling the syndrome definition criteria with the German hospital diagnosis statistic. We analyzed data from nine EDs, totaling 704,797 attendances from January 1, 2019, to March 5, 2021. Syndrome definitions were based on ICD-10 diagnoses, chief complaints, and disposition information. The internal validation showed high levels of accuracy for the syndrome definitions. For ACS, the sensitivity was 85.3%, specificity 96.3 %, and accuracy 95.9 %. For MI, sensitivity was 56.6 %, specificity 99.6 %, and accuracy 99.0 %. Sensitivity for STR was 80.5 %, specificity 97.5 %, and accuracy 96.7 %. The external validation showed high levels of correspondence between the ED data and the German hospital statistics, particularly in older age groups. Differences in younger age groups, especially in the detection of MI in younger women were noted. Our syndrome definitions showed high levels of internal and external validity. The integration of these indicators into the ED syndromic surveillance system could enhance timely public health surveillance in Germany.

ObjectiveDaily COVID-19 data reported by WHO may provide the basis for political ad hoc decisions including travel restrictions. Data reported by countries, however, are heterogeneous and metrics to evaluate its quality are scarce. In this work, we analysed COVID-19 case counts provided by WHO and developed tools to evaluate country-specific reporting behaviours. MethodsIn this retrospective cross-sectional study, COVID-19 data reported daily to WHO from 3 January 2020 until 14 June 2021 were analysed. We proposed the concepts of binary reporting rate and relative reporting behaviour and performed descriptive analyses for all countries with these metrics. We developed a score to evaluate the consistency of incidence and binary reporting rates. Further, we performed spectral clustering of the binary reporting rate and relative reporting behaviour to identify salient patterns in these metrics. ResultsOur final analysis included 222 countries and regions. Reporting scores varied between -0.17, indicating discrepancies between incidence and binary reporting rate, and 1.0 suggesting high consistency of these two metrics. Median reporting score for all countries was 0.71 (IQR 0.55-0.87). Descriptive analyses of the binary reporting rate and relative reporting behaviour showed constant reporting with a slight 'weekend effect' for most countries, while spectral clustering demonstrated that some countries had even more complex reporting patterns. ConclusionThe majority of countries reported COVID-19 cases when they did have cases to report. The identification of a slight 'weekend effect' suggests that COVID-19 case counts reported in the middle of the week may represent the best data basis for political ad hoc decisions. A few countries, however, showed unusual or highly irregular reporting that might require more careful interpretation. Our score system and cluster analyses might be applied by epidemiologists advising policy makers to consider country-specific reporting behaviours in political ad hoc decisions.

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods:We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1-4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models' predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models' forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models' past predictive performance. Results:Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models' forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models' forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models' forecasts of deaths (N=763 predictions from 20 models). Across a 1-4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions:Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding:AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

Background During the COVID-19 pandemic and associated public health and social measures, decreasing patient numbers have been described in various healthcare settings in Germany, including emergency care. This could be explained by changes in disease burden, e.g. due to contact restrictions, but could also be a result of changes in utilisation behaviour of the population. To better understand those dynamics, we analysed routine data from emergency departments to quantify changes in consultation numbers, age distribution, disease acuity and day and hour of the day during different phases of the COVID-19 pandemic. Methods We used interrupted time series analyses to estimate relative changes for consultation numbers of 20 emergency departments spread throughout Germany. For the pandemic period (16-03-2020 – 13-06-2021) four different phases of the COVID-19 pandemic were defined as interruption points, the pre-pandemic period (06-03-2017 – 09-03-2020) was used as the reference. Results The most pronounced decreases were visible in the first and second wave of the pandemic, with changes of − 30.0% (95%CI: − 32.2%; − 27.7%) and − 25.7% (95%CI: − 27.4%; − 23.9%) for overall consultations, respectively. The decrease was even stronger for the age group of 0–19 years, with − 39.4% in the first and − 35.0% in the second wave. Regarding acuity levels, consultations assessed as urgent, standard, and non-urgent showed the largest decrease, while the most severe cases showed the smallest decrease. Conclusions The number of emergency department consultations decreased rapidly during the COVID-19 pandemic, without extensive variation in the distribution of patient characteristics. Smallest changes were observed for the most severe consultations and older age groups, which is especially reassuring regarding concerns of possible long-term complications due to patients avoiding urgent emergency care during the pandemic.

Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges.

Abstract Background: During the COVID-19 pandemic and associated public health and social measures, decreasing patient numbers have been described in various healthcare settings in Germany, including emergency care. This could be explained by changes in disease burden, e.g. due to contact restrictions, but could also be a result of changes in utilization behaviour of the population. To better understand those dynamics, we analysed routine data from emergency departments to quantify changes in consultation numbers, age distribution, disease acuity and day and hour of the day during different phases of the COVID-19 pandemic. Methods: We used interrupted time series analyses to estimate relative changes for consultation numbers of 20 emergency departments spread throughout Germany. For the study period of 06-03-2017 to 13-06-2021 four different phases of the COVID-19 pandemic were defined as interruption points. Results: The most pronounced decreases were visible in the first and second wave of the pandemic, with changes of -30.0% (95%CI: -32.2%; -27.7%) and -25.7% (95%CI: -27.4%; -23.9%) for overall consultations, respectively. The decrease was even stronger for the age group of 0-19 years, with -39.4% in the first and -35.0% in the second wave. Regarding acuity levels, consultations assessed as urgent, standard and non-urgent showed the largest decrease, while the most severe cases showed the smallest decrease.Conclusions: The number of emergency department consultations decreased rapidly during the COVID-19 pandemic, without extensive variation in the distribution of patient characteristics. Smallest changes were observed for the most severe consultations and older age groups, which is especially reassuring regarding concerns of possible long-term complications due to patients avoiding urgent emergency care during the pandemic.

Background The occurrence of suicide attempts is a key indicator of the population's mental health and therefore belongs in the domain of Mental Health Surveillance at the Robert Koch Institute. No data source is currently being used systematically for the continuous observation of psychiatric emergencies (including suicide attempts) in Germany. Therefore, the use of routine data from emergency departments will be explored in this work. Methods We included routine data from 12 emergency departments between 1 January 2018 and 28 March 2021. We developed syndrome definitions for suicide attempts, psychiatric emergencies based on combinations of chief complaints, and diagnoses from patients presenting with psychopathological symptoms. A descriptive analysis over time was presented and stratified by age and sex. Results In total 1,516,883 emergency department attendances were included, among which we identified 5,133 cases (0.3%) as suicide attempts, 31,085 (2.1%) as psychiatric emergencies, and 34,230 (2.3%) as cases with psychiatric symptoms. Among psychiatric emergencies, 16.5% presented because of a suicide attempt. Of cases presenting with a suicide attempt, 53.4% were male and 20.2% were aged between 25 and 34 years. Cases identified by all 3 syndrome definitions and their temporal variations could be displayed over the entire observation period. Conclusion Syndromic surveillance using emergency department data indicates a potential for continuous surveillance of suicide attempts and psychiatric emergencies and provides a basis for further validation and analysis. The display of changes in real time extends the current research opportunities for psychiatric emergencies in Germany. Systematic surveillance of suicide attempts can contribute to evidence-based suicide prevention.

BackgroundThe COVID-19 pandemic expanded the need for timely information on acute respiratory illness at population level.AimWe explored the potential of routine emergency department data for syndromic surveillance of acute respiratory illness in Germany.MethodsWe used routine attendance data from emergency departments, which continuously transferred data between week 10 2017 and 10 2021, with ICD-10 codes available for > 75% of attendances. Case definitions for acute respiratory infection (ARI), severe acute respiratory infection (SARI), influenza-like illness (ILI), respiratory syncytial virus infection (RSV) and COVID-19 were based on a combination of ICD-10 codes, and/or chief complaints, sometimes combined with information on hospitalisation and age.ResultsWe included 1,372,958 attendances from eight emergency departments. The number of attendances dropped in March 2020 during the first COVID-19 pandemic wave, increased during summer, and declined again during the resurge of COVID-19 cases in autumn and winter of 2020/21. A pattern of seasonality of respiratory infections could be observed. By using different case definitions (i.e. for ARI, SARI, ILI, RSV) both the annual influenza seasons in the years 2017-2020 and the dynamics of the COVID-19 pandemic in 2020/21 were apparent. The absence of the 2020/21 influenza season was visible, parallel to the resurge of COVID-19 cases. SARI among ARI cases peaked in April-May 2020 (17%) and November 2020-January 2021 (14%).ConclusionSyndromic surveillance using routine emergency department data can potentially be used to monitor the trends, timing, duration, magnitude and severity of illness caused by respiratory viruses, including both influenza viruses and SARS-CoV-2.

Background During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. Methods We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. Results We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. Conclusions Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

German national surveillance data analysis shows that hospitalisation odds associated with Omicron lineage BA.1 or BA.2 infections are up to 80% lower than with Delta infection, primarily in ≥ 35-year-olds. Hospitalised vaccinated Omicron cases' proportions (2.3% for both lineages) seemed lower than those of the unvaccinated (4.4% for both lineages). Independent of vaccination status, the hospitalisation frequency among cases with Delta seemed nearly threefold higher (8.3%) than with Omicron (3.0% for both lineages), suggesting that Omicron inherently causes less severe disease.

Background: Utilising national surveillance data, we investigated the impact of the COVID-19 immunisation campaign on COVID-19 morbidity and mortality between December/2020 and October/2021 in Germany. Methods: We compared patterns in immunisation coverage, incidence, hospitalisations, and deaths among 12-17, 18-59, and 60+ year-olds and examined these patterns within the context of anti pandemic measures. Results: COVID-19 incidence increased in all age groups following the end of lockdown restrictions in March/2021, but as Germany experienced successive peaks in incidence, age groups with higher immunisation coverage experienced successively smaller peaks. Notwithstanding corresponding increases during periods of higher incidence, among those aged 60+ years, COVID-19 related hospitalisations and deaths declined considerably as immunisation coverage increased, despite circulation of virus variants known to cause more severe illness. Conclusion: Although ecological in nature, this study allows us to demonstrate clear patterns of decline in COVID-19 morbidity and mortality in Germany during the course of the immunisation campaign. (c) 2022 Elsevier Ltd. All rights reserved.

Background: The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) affect healthcare seeking behaviour, access to healthcare, test strategies, disease notification and workload at public health authorities, but may also lead to a true change in transmission dynamics. We aimed to assess the impact of the pandemic and NPIs on other notifiable infectious diseases under surveillance in Germany. Methods: We included 32 nationally notifiable disease categories with case numbers >100/year in 2016–2019. We used quasi-Poisson regression analysis on a weekly aggregated time-series incorporating trend and seasonality, to compute the relative change in case numbers during week 2020–10 to 2020–32 (pandemic/NPIs), in comparison to week 2016–01 to 2020–09. Findings: During week 2020–10 to 2020–32, 216,825 COVID-19 cases, and 162,942 (-35%) cases of other diseases, were notified. Case numbers decreased across all ages and notification categories (all p<0•05), except for tick-borne encephalitis, which increased (+58%). The number of cases decreased most for respiratory diseases (from -86% for measles, to -12% for tuberculosis), gastro-intestinal diseases (from -83% for rotavirus gastroenteritis, to -7% for yersiniosis) and imported vector-borne diseases (-75% dengue fever, -73% malaria). The less affected infections were healthcare associated pathogens (from -43% infection/colonisation with carbapenem-non-susceptible Acinetobacter, to -28% for Methicillin-resistant Staphylococcus aureus invasive infection) and sexually transmitted and blood-borne diseases (from -28% for hepatitis B, to -12% for syphilis). Interpretation: During the COVID-19 pandemic a drastic decrease of notifications for most infectious diseases and pathogens was observed. Our findings suggest effects of NPIs on overall disease transmission that require further investigation. Funding: The Robert Koch Institute is the National Public Health Institute of Germany, and is an institute within the portfolio of the Federal Ministry of Health.