Collaborative nowcasting of COVID-19 hospitalization incidences in Germany.

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.

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021.

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.

We compare forecasts of weekly case and death numbers for COVID-19 in Germany and Poland based on 15 different modelling approaches. These cover the period from January to April 2021 and address numbers of cases and deaths one and two weeks into the future, along with the respective uncertainties. We find that combining different forecasts into one forecast can enable better predictions. However, case numbers over longer periods were challenging to predict. Additional data sources, such as information about different versions of the SARS-CoV-2 virus present in the population, might improve forecasts in the future.

A retrospective assessment of different endodontic treatment protocols.

BACKGROUND: The aim of this study was to assess the clinical impact of non-surgical root canal treatments (NSRCT) performed with different treatment protocols on the probability of tooth survival without untoward events and to identify predictors influencing the outcome. METHODS: During the period from July 1999 to October 2016, 5,858 patients were identified in which 9,967 NSRCTs were performed. The treatments were followed up and divided into three groups. In Group 1 root canal treatment was performed with hand instruments, in Group 2 with multiple file rotary instruments and passive ultrasonic irrigation (PUI), and Group 3 was treated with Reciproc instruments and PUI. Untoward events were defined as orthograde retreatment, apicoectomy or extraction of the tooth after initial treatment. Weibull regression was used to analyse the data. RESULTS: A total of 9,938 cases could be included into the analyses. The results showed 5-years predicted survival rates without untoward events of 73.9% (95% CI [71.7%-76.1%]), 75.1% (95% CI [71.7%-78.0%]) and 78.4% (95% CI [75.1%-81.4%]) for study group 1 (N = 5,580), 2 (N = 1,700) and 3 (N = 2,658), respectively. The differences between Group 1 and 3 were statistically significant (p < 0.006). Higher age of the patient (per year increase) and number of earlier NSRCTs (per unit increase) reduce the survival without untoward events statistically significant (both p < 0.02), while treatment of premolars had a statistically significant lower hazard ratio [0.89 (95% CI [0.79-0.99]; p = 0.030)] compared to treatment of molars and anterior teeth. A higher number of supportive periodontal treatments (per unit increase) improved tooth survival without untoward events highly significant (p < 0.0001). DISCUSSION: More recent endodontic treatment protocols involving reciprocating instruments and PUI appear to be associated with higher tooth survival rates without untoward events compared to hand instruments.