SARS-CoV-2 Vaccine Breakthrough Infections of Omicron and Delta Variants in Healthcare Workers.

Understanding SARS-CoV-2 breakthrough infections in vaccinated healthcare workers is of key importance in mitigating the effects of the COVID-19 pandemic in healthcare facilities. An observational prospective cohort study was conducted in vaccinated employees with acute SARS-CoV-2 infection between October 2021 and February 2022. Serological and molecular testing was performed to determine SARS-CoV-2 viral load, lineage, antibody levels, and neutralizing antibody titers. A total of 571 (9.7%) employees experienced SARS-CoV-2 breakthrough infections during the enrolment period, of which 81 were included. The majority (n = 79, 97.5%) were symptomatic and most (n = 75, 92.6%) showed Ct values < 30 in RT-PCR assays. Twenty-four (30%) remained PCR-positive for > 15 days. Neutralizing antibody titers were strongest for the wildtype, intermediate for Delta, and lowest for Omicron variants. Omicron infections occurred at higher anti-RBD-IgG serum levels (p = 0.00001) and showed a trend for higher viral loads (p = 0.14, median Ct difference 4.3, 95% CI [-2.5-10.5]). For both variants, viral loads were significantly higher in participants with lower anti-RBD-IgG serum levels (p = 0.02). In conclusion, while the clinical course of infection with both the Omicron and Delta variants was predominantly mild to moderate in our study population, waning immune response over time and prolonged viral shedding were observed.

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.

On the Parametrization of Epidemiologic Models-Lessons from Modelling COVID-19 Epidemic.

Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag in case-reporting, changing testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants, non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. This includes data issues such as known delays or biases in reporting. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a specific SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions and provide scenarios of the future course of the epidemic under specified conditions. It can be translated to other data sets, i.e., other countries and other SIR-type models.

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.

Correction: Covid-19 in outpatients-Is fever a useful indicator for SARS-CoV-2 infection?

[This corrects the article DOI: 10.1371/journal.pone.0246312.].

Covid-19 in outpatients-Is fever a useful indicator for SARS-CoV-2 infection?

OBJECTIVE: Understanding mild to moderate symptoms of coronavirus disease 2019 (Covid-19) is important in order to identify active cases early and thus counteract transmission. METHODS: In March 2020, Leipzig University Hospital established an outpatient clinic for patients potentially infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Confirmed cases with mild to moderate symptoms self-isolated at home and were followed-up by daily telephone calls for at least 14 days. Symptoms and course of illness of these patients are reported here. RESULTS: From March 20 to April 17, 2020, 1460 individuals were tested for SARS-CoV-2 by naso- or oropharyngeal swab for real-time polymerase chain reaction (RT-PCR). Covid-19 was confirmed in 91 (6.2%) patients, of which 87 were included in the final analysis. Patients presented for testing after a mean of 5.9 days (IQR = 2.0-8.5). The median age was 37.0 years (IQR = 28.5-53), and 48 (55.2%) were female. Five (5.7%) patients required hospital admission during the course of illness. Most frequently reported symptoms were fatigue (n = 64, 74%), cough (n = 58, 67%), and hyposmia/hypogeusia (n = 44, 51%). In contrast to previous reports, fever occurred in less than a third of patients (n = 25, 29%). By day 14, more than half of the patients had recovered completely (n = 37/70, 52.9%). CONCLUSIONS: Fever seems to be less common in patients of relatively young age diagnosed with mild to moderate Covid-19. This suggests that body temperature alone may be an insufficient indicator of SARS-CoV-2 infection.