Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden.

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.

Hospital profiling using Bayesian decision theory.

For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. The classification of healthcare providers as outliers or non-outliers is a decision under uncertainty, because the true quality is unknown and can only be inferred from an observed result of a quality indicator. We propose to embed the classification of healthcare providers into a Bayesian decision theoretical framework that enables the derivation of optimal decision rules with respect to the expected decision consequences. We propose paradigmatic utility functions for two typical purposes of hospital profiling: the external reporting of healthcare quality and the initiation of change in care delivery. We make use of funnel plots to illustrate and compare the resulting optimal decision rules and argue that sensitivity and specificity of the resulting decision rules should be analyzed. We then apply the proposed methodology to the area of hip replacement surgeries by analyzing data from 1,277 hospitals in Germany which performed over 180,000 such procedures in 2017. Our setting illustrates that the classification of outliers can be highly dependent upon the underlying utilities. We conclude that analyzing the classification of hospitals as a decision theoretic problem helps to derive transparent and justifiable decision rules. The methodology for classifying quality indicator results is implemented in an R package (iqtigbdt) and is available on GitHub.

Parallelized calculation of permutation tests.

MOTIVATION: Permutation tests offer a straightforward framework to assess the significance of differences in sample statistics. A significant advantage of permutation tests are the relatively few assumptions about the distribution of the test statistic are needed, as they rely on the assumption of exchangeability of the group labels. They have great value, as they allow a sensitivity analysis to determine the extent to which the assumed broad sample distribution of the test statistic applies. However, in this situation, permutation tests are rarely applied because the running time of naïve implementations is too slow and grows exponentially with the sample size. Nevertheless, continued development in the 1980s introduced dynamic programming algorithms that compute exact permutation tests in polynomial time. Albeit this significant running time reduction, the exact test has not yet become one of the predominant statistical tests for medium sample size. Here, we propose a computational parallelization of one such dynamic programming-based permutation test, the Green algorithm, which makes the permutation test more attractive. RESULTS: Parallelization of the Green algorithm was found possible by non-trivial rearrangement of the structure of the algorithm. A speed-up-by orders of magnitude-is achievable by executing the parallelized algorithm on a GPU. We demonstrate that the execution time essentially becomes a non-issue for sample sizes, even as high as hundreds of samples. This improvement makes our method an attractive alternative to, e.g. the widely used asymptotic Mann-Whitney U-test. AVAILABILITYAND IMPLEMENTATION: In Python 3 code from the GitHub repository https://github.com/statisticalbiotechnology/parallelPermutationTest under an Apache 2.0 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Epitweetr: Early warning of public health threats using Twitter data.

BackgroundThe 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.AimThis study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats.MethodsWe 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.ResultsThe 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).ConclusionEpitweetr 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.

Analysis of Asymptomatic and Presymptomatic Transmission in SARS-CoV-2 Outbreak, Germany, 2020.

We determined secondary attack rates (SAR) among close contacts of 59 asymptomatic and symptomatic coronavirus disease case-patients by presymptomatic and symptomatic exposure. We observed no transmission from asymptomatic case-patients and highest SAR through presymptomatic exposure. Rapid quarantine of close contacts with or without symptoms is needed to prevent presymptomatic transmission.

Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany.

Despite the wide application of dynamic models in infectious disease epidemiology, the particular modeling of variability in the different model components is often subjective rather than the result of a thorough model selection process. This is in part because inference for a stochastic transmission model can be difficult since the likelihood is often intractable due to partial observability. In this work, we address the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models. For this, we perform inference for six partially observed Markov process models, which assume the same underlying transmission dynamics, but differ with respect to the amount of variability they allow for. The inference framework for the stochastic transmission models is provided by iterated filtering methods, which are readily implemented in the R package pomp by King and others (2016, Statistical inference for partially observed Markov processes via the R package pomp. Journal of Statistical Software69, 1-43). We illustrate our approach on German rotavirus surveillance data from 2001 to 2008, discuss practical difficulties of the methods used and calculate a model based estimate for the basic reproduction number $R_0$ using these data.

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points.

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.

Nowcasting the COVID-19 pandemic in Bavaria.

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 Re(t) 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.

Are People Living Near Modern Swine Production Facilities at Increased Risk of Influenza Virus Infection?

BACKGROUND: Swine can harbor influenza viruses that are pathogenic to humans. Previous studies support an increased risk of human influenza cases among individuals with swine contact. North Carolina has the second-largest swine industry in the United States. METHODS: We investigated the spatiotemporal association between influenza-like illnesses (ILIs) and licensed swine operations from 2008 to 2012 in North Carolina. We determined the week in which ILI cases peaked and statistically estimated their week of onset. This was performed for all 100 North Carolina counties for 4 consecutive influenza seasons. We used linear models to correlate the number of permitted swine operations per county with the weeks of onset and peak ILI activity. RESULTS: We found that during the 2009-2010 and 2010-2011 influenza seasons, both seasons in which the pandemic 2009 H1N1 influenza A virus circulated, ILI peaked earlier in counties with a higher number of licensed swine operations. We did not observe this in 2008-2009 or 2011-2012, nor did we observe a relationship between ILI onset week and number of swine operations. CONCLUSIONS: Our findings suggest that concentrated swine feeding operations amplified transmission of influenza during years in which H1N1 was circulating. This has implications for vaccine strategies targeting swine workers, as well as virologic surveillance in areas with large concentrations of swine.

A system for automated outbreak detection of communicable diseases in Germany.

We describe the design and implementation of a novel automated outbreak detection system in Germany that monitors the routinely collected surveillance data for communicable diseases. Detecting unusually high case counts as early as possible is crucial as an accumulation may indicate an ongoing outbreak. The detection in our system is based on state-of-the-art statistical procedures conducting the necessary data mining task. In addition, we have developed effective methods to improve the presentation of the results of such algorithms to epidemiologists and other system users. The objective was to effectively integrate automatic outbreak detection into the epidemiological workflow of a public health institution. Since 2013, the system has been in routine use at the German Robert Koch Institute.

Bayesian outbreak detection in the presence of reporting delays.

One use of infectious disease surveillance systems is the statistical aberration detection performed on time series of counts resulting from the aggregation of individual case reports. However, inherent reporting delays in such surveillance systems make the considered time series incomplete, which can be an impediment to the timely detection and thus to the containment of emerging outbreaks. In this work, we synthesize the outbreak detection algorithms of Noufaily et al. (2013) and Manitz and Höhle (2013) while additionally addressing right truncation caused by reporting delays. We do so by considering the resulting time series as an incomplete two-way contingency table which we model using negative binomial regression. Our approach is defined in a Bayesian setting allowing a direct inclusion of all sources of uncertainty in the derivation of whether an observed case count is to be considered an aberration. The proposed algorithm is evaluated both on simulated data and on the time series of Salmonella Newport cases in Germany in 2011. Altogether, our method aims at allowing timely aberration detection in the presence of reporting delays and hence underlines the need for statistical modeling to address complications of reporting systems. An implementation of the proposed method is made available in the R package surveillance as the function "bodaDelay".

German outbreak of Escherichia coli O104:H4 associated with sprouts.

BACKGROUND: A large outbreak of the hemolytic-uremic syndrome caused by Shiga-toxin-producing Escherichia coli O104:H4 occurred in Germany in May 2011. The source of infection was undetermined. METHODS: We conducted a matched case-control study and a recipe-based restaurant cohort study, along with environmental, trace-back, and trace-forward investigations, to determine the source of infection. RESULTS: The case-control study included 26 case subjects with the hemolytic-uremic syndrome and 81 control subjects. The outbreak of illness was associated with sprout consumption in univariable analysis (matched odds ratio, 5.8; 95% confidence interval [CI], 1.2 to 29) and with sprout and cucumber consumption in multivariable analysis. Among case subjects, 25% reported having eaten sprouts, and 88% reported having eaten cucumbers. The recipe-based study among 10 groups of visitors to restaurant K included 152 persons, among whom bloody diarrhea or diarrhea confirmed to be associated with Shiga-toxin-producing E. coli developed in 31 (20%). Visitors who were served sprouts were significantly more likely to become ill (relative risk, 14.2; 95% CI, 2.6 to ∞). Sprout consumption explained 100% of cases. Trace-back investigation of sprouts from the distributor that supplied restaurant K led to producer A. All 41 case clusters with known trading connections could be explained by producer A. The outbreak strain could not be identified on seeds from the implicated lot. CONCLUSIONS: Our investigations identified sprouts as the most likely outbreak vehicle, underlining the need to take into account food items that may be overlooked during subjects' recall of consumption.

Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011.

A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends.

Bayesian parameter inference for dynamic infectious disease modelling: rotavirus in Germany.

Understanding infectious disease dynamics using epidemic models based on ordinary differential equations requires the calibration of model parameters from data. A commonly used approach in practice to simplify this task is to fix many parameters on the basis of expert or literature information. However, this not only leaves the corresponding uncertainty unexamined but often also leads to biased inference for the remaining parameters because of dependence structures inherent in any given model. In the present work, we develop a Bayesian inference framework that lessens the reliance on such external parameter quantifications by pursuing a more data-driven calibration approach. This includes a novel focus on residual autocorrelation combined with model averaging techniques in order to reduce these estimates' dependence on the underlying model structure. We applied our methods to the modelling of age-stratified weekly rotavirus incidence data in Germany from 2001 to 2008 using a complex susceptible-infectious-susceptible-type model complemented by the stochastic reporting of new cases. As a result, we found the detection rate in the eastern federal states to be more than four times higher compared with that of the western federal states (19.0% vs 4.3%), and also the infectiousness of symptomatically infected individuals was estimated to be more than 10 times higher than that of asymptomatically infected individuals (95% credibility interval: 8.1­19.6). Not only do these findings give valuable epidemiological insight into the transmission processes, we were also able to examine the considerable impact on the model-predicted transmission dynamics when fixing parameters beforehand.

Estimating the under-reporting of norovirus illness in Germany utilizing enhanced awareness of diarrhoea during a large outbreak of Shiga toxin-producing E. coli O104:H4 in 2011--a time series analysis.

BACKGROUND: Laboratory-confirmed norovirus illness is reportable in Germany since 2001. Reported case numbers are known to be undercounts, and a valid estimate of the actual incidence in Germany does not exist. An increase of reported norovirus illness was observed simultaneously to a large outbreak of Shiga toxin-producing E. coli O104:H4 in Germany in 2011--likely due to enhanced (but not complete) awareness of diarrhoea at that time. We aimed at estimating age- and sex-specific factors of that excess, which should be interpretable as (minimal) under-reporting factors of norovirus illness in Germany. METHODS: We used national reporting data on laboratory-confirmed norovirus illness in Germany from calendar week 31 in 2003 through calendar week 30 in 2012. A negative binomial time series regression model was used to describe the weekly counts in 8∙2 age-sex strata while adjusting for secular trend and seasonality. Overall as well as age- and sex-specific factors for the excess were estimated by including additional terms (either an O104:H4 outbreak period indicator or a triple interaction term between outbreak period, age and sex) in the model. RESULTS: We estimated the overall under-reporting factor to be 1.76 (95% CI 1.28-2.41) for the first three weeks of the outbreak before the outbreak vehicle was publicly communicated. Highest under-reporting factors were here estimated for 20-29 year-old males (2.88, 95% CI 2.01-4.11) and females (2.67, 95% CI 1.87-3.79). Under-reporting was substantially lower in persons aged <10 years and 70 years or older. CONCLUSIONS: These are the first estimates of (minimal) under-reporting factors for norovirus illness in Germany. They provide a starting point for a more detailed investigation of the relationship between actual incidence and reporting incidence of norovirus illness in Germany.

Carrier prevalence, secondary household transmission, and long-term shedding in 2 districts during the Escherichia coli O104:H4 outbreak in Germany, 2011.

BACKGROUND: From May through July 2011, Germany experienced a large outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 infection. Our objective was to identify the prevalence of STEC O104:H4 carriers in households in highly affected areas, the rate of secondary household transmissions, and the duration of long-term shedding. METHODS: In a cross-sectional study, we recruited case and control households to determine STEC household prevalence. We then conducted a prospective cohort study (households with ≥ 2 members and ≥ 1 case) to determine rates of household transmission and shedding duration. RESULTS: For part 1, we recruited 57 case households (62 case patients and 93 household contacts) and 36 control households (89 household members). We only detected cases in previously known case households and identified 1 possible adult-to-adult household transmission. For part 2, we followed 14 households and 20 carriers. No secondary household transmission was detected in the prospective follow-up period. In 1 adult carrier, shedding lasted >7 months. However, the median estimated shedding time was 10-14 days (95% confidence interval, 0-33 days). Three carriers showed intermittent shedding. CONCLUSIONS: The prevalence of STEC O104:H4 carriers even in highly affected areas appears to be low. Despite prolonged shedding in some patients, secondary adult-to-adult household transmissions seem to be rare events in the postdiarrheal disease phase.

[Reliability of peer review-like dialogue in the German statutory quality assurance program]. / Reliabilität des Strukturierten Dialogs in der gesetzlich verpflichtenden Qualitätssicherung.

BACKGROUND: Quality measurement in the German statutory program for quality in health care follows a two-step process. For selected areas of health care, quality is measured via performance indicators (first step). Providers failing to achieve benchmarks in these indicators subsequently enter into a peer review process (second step) and are asked by the respective regional authority to provide a written statement regarding their indicator results. The statements are then evaluated by peers, with the goal to assess the provider's quality of care. In the past, similar peer review-based approaches to the measurement of health care quality in other countries have shown a tendency to lack reliability. So far, the reliability of this component of the German statutory program for quality in health care has not been investigated. METHOD: Using logistic regression models, the influence of the respective regional authority on the peer review component of health care quality measurement in Germany was investigated using three exemplary indicators and data from 2016. RESULTS: Both the probability that providers are asked to provide a statement as well as the results produced by the peer review process significantly depend on the regional authority in charge. This dependence cannot be fully explained by differences in the indicator results or by differences in case volume. CONCLUSIONS: The present results are in accordance with earlier findings, which show low reliability for peer review-based approaches to quality measurement. Thus, different results produced by the peer review component of the quality measurement process may in part be due to differences in the way the review process is conducted. This heterogeneity among the regional authorities limits the reliability of this process. In order to increase reliability, the peer review process should be standardized to a higher degree, with clear review criteria, and the peers should undergo comprehensive training for the review process. Alternatively, the future peer review component could be adapted to focus rather on identification of improvement strategies than on reliable provider comparisons.

Duration of fecal shedding of Shiga toxin-producing Escherichia coli O104:H4 in patients infected during the 2011 outbreak in Germany: a multicenter study.

BACKGROUND: In May-July 2011, Germany experienced a large food-borne outbreak of Shiga toxin 2-producing Escherichia coli (STEC O104:H4) with 3842 cases, including 855 cases with hemolytic uremic syndrome (HUS) and 53 deaths. METHODS: A multicenter study was initiated in 5 university hospitals to determine pathogen shedding duration. Diagnostics comprised culture on selective media, toxin enzyme-linked immunosorbent assay, and polymerase chain reaction. Results were correlated with clinical and epidemiologic findings. Testing for pathogen excretion was continued after discharge of the patient. RESULTS: A total of 321 patients (104 male, 217 female) were included (median age, 40 years [range, 1-89 days]). Median delay from onset of symptoms to hospitalization was 4 days (range, 0-17 days). Two hundred nine patients presented with HUS. The estimate for the median duration of shedding was 17-18 days. Some patients remained STEC O104:H4 positive until the end of the observation time (maximum observed shedding duration: 157 days). There was no significant influence of sex on shedding duration. Patients presenting with HUS had a significantly shortened shedding duration (median, 13-14 days) compared to non-HUS patients (median, 33-34 days). Antimicrobial treatment was also significantly associated with reduced shedding duration. Children (age≤15 years) had longer shedding durations than adults (median, 35-41 vs 14-15 days). CONCLUSIONS: STEC O104:H4 is usually eliminated from the human gut after 1 month, but may sometimes be excreted for several months. Proper follow-up of infected patients is important to avoid further pathogen spread.

Associations of age and sex with the clinical outcome and incubation period of Shiga toxin-producing Escherichia coli O104:H4 infections, 2011.

We pooled data on adults who reported diarrhea or developed life-threatening hemolytic uremic syndrome (HUS) in any of 6 closed cohorts from 4 countries (1 cohort each in Denmark, France, and Sweden and 3 in Germany) that were investigated during a large outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 infection in 2011. Logistic regression and Weibull regression for interval censored data were used to assess the relation of age and sex with clinical outcome and with incubation period. Information on the latter was used in a nonparametric back-projection context to estimate when adult cases reported in Germany were exposed to STEC O104:H4. Overall, data from 119 persons (median age, 49 years; 80 women) were analyzed. Bloody diarrhea and HUS were recorded as the most severe outcome for 44 and 26 individuals, respectively. Older age was significantly associated with bloody diarrhea but not with HUS. Woman had nonsignificantly higher odds for bloody diarrhea (odds ratio = 1.81) and developing HUS (odds ratio = 1.83) than did men. Older participants had a statistically significantly reduced incubation period. The shortest interval that included 75% of exposures in adults spanned only 12 days and preceded outbreak detection. In conclusion, the frequency of bloody diarrhea but not of HUS and the length of the incubation period depended on the age of individuals infected with STEC O104:H4. A large number of people were exposed to STEC O104:H4 for a short period of time.

Clinical aspects and self-reported symptoms of sequelae of Yersinia enterocolitica infections in a population-based study, Germany 2009-2010.

BACKGROUND: Foodborne Yersinia enterocolitica infections continue to be a public health problem in many countries. Consumption of raw or undercooked pork is the main risk factor for yersiniosis in Germany. Small children are most frequently affected by yersiniosis. In older children and young adults, symptoms of disease may resemble those of appendicitis and may lead to hospitalization and potentially unnecessary appendectomies. Y. enterocolitica infections may also cause sequelae such as reactive arthritis (ReA), erythema nodosum (EN), and conjunctivitis. METHODS: We studied clinical aspects of yersiniosis, antimicrobial use, and self-reported occurrence of appendectomies, reactive arthritis, erythema nodosum and conjunctivitis. To assess post-infectious sequelae participants of a large population-based case-control study on laboratory-confirmed Y. enterocolitica infections conducted in Germany in 2009-2010 were followed for 4 weeks. RESULTS: Diarrhea occurred most frequently in children ≤4 years (95%); abdominal pain in the lower right quadrant was most common in children 5-14 years of age (63%). Twenty-seven per cent of patients were hospitalized, 37% were treated with antimicrobials. In 6% of yersiniosis patients ≥5 years of age, appendectomies were performed. Self-reported symptoms consistent with ReA were reported by 12% of yersiniosis patients compared to 5% in a reference group not exposed to yersiniosis. Symptoms consistent with EN were reported by 3% of yersiniosis patients compared to 0.1% in the reference group. Symptoms of conjunctivitis occurred with the same frequency in yersiniosis patients and the reference group. CONCLUSIONS: Acute Y. enterocolitica infections cause considerable burden of illness with symptoms lasting for about 10 days and hospitalizations in more than a quarter of patients. The proportion of yersiniosis patients treated with antimicrobial drugs appears to be relatively high despite guidelines recommending their use only in severe cases. Appendectomies and post-infectious complications (ReA and EN) are more frequently reported in yersiniosis patients than in the reference group suggesting that they can be attributed to infections with Y. enterocolitica. Physicians should keep recent Y. enterocolitica infection in mind in patients with symptoms resembling appendicitis as well as in patients with symptoms of unclear arthritis.