Collaborative nowcasting of COVID-19 hospitalization incidences in Germany (preprint)

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. This indicator played an important role in the management of the pandemic 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 uncertainty 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.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations (preprint)

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 from a standardised source over the next one to four 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 and combined up to 28 forecast models for 32 countries. 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 84% of participating models forecasts of incident cases (with a total N=862), and 92% of participating models forecasts of deaths (N=746). Across a one to four week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over four 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 two weeks.

Impact of the COVID-19 pandemic and its associated public health and social measures on emergency department utilization in Germany between March 2017 and June 2021 (preprint)

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.

National and subnational short-term forecasting of COVID-19 in Germany and Poland, early 2021 (preprint)

We report on the second and final part of a pre-registered forecasting study on COVID-19 cases and deaths in Germany and Poland. Fifteen independent research teams provided forecasts at lead times of one through four weeks from January through mid-April 2021. Compared to the first part (October--December 2020), the number of participating teams increased, and a number of teams started providing subnational-level forecasts. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in the first part of our study. In both countries, case counts declined initially, before rebounding due to the rise of the B.1.1.7 variant. Deaths declined through most of the study period in Germany while in Poland they increased after a prolonged plateau. Many, though not all, models outperformed a simple baseline model up to four weeks ahead, with ensemble methods showing very good relative performance. Major trend changes in reported cases, however, remained challenging to predict.

Using routine emergency department data for syndromic surveillance of acute respiratory illness in Germany, week 10-2017 to 10-2021 (preprint)

Background The Coronavirus disease 2019 (COVID-19) pandemic expanded the need for timely information on acute respiratory illness on the population level. Aim We explored the potential of routine emergency department data for syndromic surveillance of acute respiratory illness in Germany. Methods We included routine attendance data from emergency departments who continuously transferred data between week 10-2017 and 10-2021, with ICD-10 codes available for >75% of the attendances. Case definitions for acute respiratory illness (ARI), severe ARI (SARI), influenza-like illness (ILI), respiratory syncytial virus disease (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. Results We included 1,372,958 attendances from eight emergency departments. The number of attendances dropped in March 2020, increased during summer, and declined again during the resurge of COVID-19 cases in autumn and winter of 2020/2021. A pattern of seasonality of acute 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 2020-2021 were apparent. The absence of the 2020/2021 flu season was visible, parallel to the resurge of COVID-19 cases. The percentage SARI among ARI cases peaked in April-May 2020 (17%) and November 2020-January 2021 (14%). Conclusion Syndromic surveillance using routine emergency department data has the potential to monitor the trends, timing, duration, magnitude and severity of illness caused by respiratory viruses, including both influenza and SARS-CoV-2.

Short-term forecasting of COVID-19 in Germany and Poland during the second wave - a preregistered study (preprint)

We report insights from ten weeks of collaborative COVID-19 forecasting for Germany and Poland (12 October - 19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

The Impact of the COVID-19 Pandemic and Associated Public Health Measures on Other Notifiable Infectious Diseases Under National Surveillance in Germany, Week 1-2016 – Week 32-2020 (preprint)

Background: The COVID-19 pandemic and associated public health measures 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 associated public health measures on other notifiable infectious diseases under surveillance in Germany.Methods: We included 32 nationally notifiable diseases 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), 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<0·005), except for tick-borne encephalitis, which increased (+58%). Cases of 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) decreased the most, followed by 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 non-pharmaceutical COVID-19 countermeasures on overall disease transmission that require further investigation.Funding: None.Declaration of Interests: We declare no competing interests.Ethics Approval Statement: Pseudonymized notification data was collected at the RKI based on the German Infection Protection Act.