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1.
Microorganisms ; 10(9)2022 Sep 09.
Article in English | MEDLINE | ID: covidwho-2033059

ABSTRACT

Background: Despite a vaccination rate of 82.0% (n = 123/150), a SARS-CoV-2 (Alpha) outbreak with 64.7% (n = 97/150) confirmed infections occurred in a nursing home in Bavaria, Germany. Objective: the aim of this retrospective cohort study was to examine the effects of the Corminaty vaccine in a real-life outbreak situation and to obtain insights into the antibody response to both vaccination and breakthrough infection. Methods: the antibody status of 106 fully vaccinated individuals (54/106 breakthrough infections) and epidemiological data on all 150 residents and facility staff were evaluated. Results: SARS-CoV-2 infections (positive RT-qPCR) were detected in 56.9% (n = 70/123) of fully vaccinated, compared to 100% (n = 27/27) of incompletely or non-vaccinated individuals. The proportion of hospitalized and deceased was 4.1% (n = 5/123) among fully vaccinated and therewith lower compared to 18.5% (n = 5/27) hospitalized and 11.1% (n = 3/27) deceased among incompletely or non-vaccinated. Ct values were significantly lower in incompletely or non-vaccinated (p = 0.02). Neutralizing antibodies were detected in 99.1% (n = 105/106) of serum samples with significantly higher values (p < 0.001) being measured post-breakthrough infection. α-N-antibodies were detected in 37.7% of PCR positive but not in PCR negative individuals. Conclusion: Altogether, our data indicate that SARS-CoV-2 vaccination does provide protection against infection, severe disease progression and death with regards to the Alpha variant. Nonetheless, it also shows that infection and transmission are possible despite full vaccination. It further indicates that breakthrough infections can significantly enhance α-S- and neutralizing antibody responses, indicating a possible benefit from booster vaccinations.

3.
PLoS One ; 16(11): e0259370, 2021.
Article in English | MEDLINE | ID: covidwho-1504003

ABSTRACT

BACKGROUND: The efficacy of the BioNTech-Pfizer BNT162b2 vaccination in the elderly (≥80 years) could not be fully assessed in the BioNTech-Pfizer trial due to low numbers in this age group. We aimed to evaluate the effectiveness of the BioNTech-Pfizer (BNT162b2) vaccine to prevent SARS-CoV-2 infection and severe outcomes in octo- and novo-generians in a German state setting. METHODS AND FINDINGS: A prospective observational study of 708,187 persons aged ≥80 years living in Bavaria, Germany, was conducted between Jan 9 to Apr 11, 2021. We assessed the vaccine effectiveness (VE) for two doses of the BNT162b2 vaccine with respect to SARS-CoV-2 infection and related hospitalisations and mortality. Additionally, differences in VE by age groups ≥80 to ≤89 years and ≥90 years were studied. Analyses were adjusted by sex. By the end of follow-up, 63.8% of the Bavarian population ≥80 years had received one dose, and 52.7% two doses, of the BNT162b2 vaccine. Two doses of the BNT162b2 vaccine lowered the proportion of SARS-CoV-2 infections and related outcomes, resulting in VE estimates of 68.3% (95% confidence interval (CI) 65.5%, 70.9%) for infection, 73.2% (95% CI 65.3%, 79.3%) for hospitalisation, and 85.1% (95% CI 80.0%, 89.0%) for mortality. Sex differences in the risk of COVID-19 outcomes observed among unvaccinated persons disappeared after two BNT162b2 vaccine doses. Overall, the BNT162b2 vaccine was equally effective in octo- and novo-genarians. CONCLUSIONS: Two doses of BioNTech-Pfizer's BNT162b2 vaccine is highly effective against COVID-19 outcomes in elderly persons.


Subject(s)
COVID-19 Vaccines/therapeutic use , COVID-19/prevention & control , Age Factors , Aged , Aged, 80 and over , Female , Germany/epidemiology , Hospitalization , Humans , Immunization Programs , Male , Prospective Studies , SARS-CoV-2 , Sex Factors , Treatment Outcome , Vaccination
4.
Epidemiol Infect ; 149: e150, 2021 06 23.
Article in English | MEDLINE | ID: covidwho-1338505

ABSTRACT

We assessed severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) reverse transcriptase-polymerase chain reaction (RT-PCR) diagnostic sensitivity and cycle threshold (Ct) values relative to symptom onset in symptomatic coronavirus disease-2019 (COVID-19) patients from Bavaria, Germany, of whom a subset was repeatedly tested. Locally weighted scatterplot smoothing method was used to assess the relationship between symptom onset and Ct-values. Kaplan-Meier plots were used to visualise the empirical probability of detecting viral ribonucleic acid (RNA) over time and estimate the time until clearance of viral RNA among the repeatedly tested patients. Among 721 reported COVID-19 cases, the viral RNA was detected in specimens taken between three days before and up to 48 days after symptom onset. The mean Ct-value was 28.6 (95% confidence interval (CI) 28.2-29.0) with the lowest mean Ct-value (26.2) observed two days after symptom onset. Up to 7 days after symptom onset, the diagnostic sensitivity of the RT-PCR among repeatedly sampled patients (n = 208) remained above 90% and decreased to 50% at day 12 (95% CI 10.5-21.5). Our data provide valuable estimates to optimise the timing of sampling of individuals for SARS-CoV-2 detection. A considerable proportion of specimens sampled before symptom onset had Ct-values comparable with Ct-values after symptom onset, suggesting the probability of presymptomatic transmission.


Subject(s)
COVID-19/virology , SARS-CoV-2/isolation & purification , Virus Shedding , Adolescent , Adult , Aged , Asymptomatic Infections , COVID-19/diagnosis , Child , Child, Preschool , Female , Germany , Humans , Infant , Male , Middle Aged , Nasopharynx/virology , RNA, Viral/isolation & purification , Reverse Transcriptase Polymerase Chain Reaction , Sputum/virology , Time Factors , Young Adult
6.
Infection ; 49(5): 1029-1032, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1198524

ABSTRACT

The Bavarian Influenza Sentinel (BIS) monitors the annual influenza season by combining virological and epidemiological data. The 2019/2020 influenza season overlapped with the beginning COVID-19 pandemic thus allowing to investigate whether there was an unnoticed spread of SARS-CoV-2 among outpatients with acute respiratory infections in the community prior to the first COVID-19 cluster in Bavaria. Therefore, we retrospectively analysed oropharyngeal swabs obtained in BIS between calendar week (CW) 39 in 2019 and CW 14 in 2020 for the presence of SARS-CoV-2 RNA by RT-PCR. 610 of all 1376 BIS swabs-contained sufficient material to test for SARS-CoV-2, among them 260 oropharyngeal swabs which were collected prior to the first notified German COVID-19 case in CW 04/2020. In none of these swabs SARS-CoV-2 RNA was detected suggesting no SARS-CoV-2 spread prior to late January 2020 in Bavaria.


Subject(s)
COVID-19 , Germany/epidemiology , Humans , Pandemics , RNA, Viral , Retrospective Studies , SARS-CoV-2
7.
Biom J ; 63(3): 490-502, 2021 03.
Article in English | MEDLINE | ID: covidwho-950921

ABSTRACT

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 R e ( 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.


Subject(s)
COVID-19/epidemiology , Models, Statistical , Bayes Theorem , Germany/epidemiology , Humans , Pandemics , Retrospective Studies
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