| 1. |
- Einhauser, Sebastian, et al.
(författare)
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Time Trend in SARS-CoV-2 Seropositivity, Surveillance Detection- and Infection Fatality Ratio until Spring 2021 in the Tirschenreuth County-Results from a Population-Based Longitudinal Study in Germany
- 2022
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Ingår i: Viruses. - : MDPI AG. - 1999-4915. ; 14:6
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Tidskriftsartikel (refereegranskat)abstract
- Herein, we provide results from a prospective population-based longitudinal follow-up (FU) SARS-CoV-2 serosurveillance study in Tirschenreuth, the county which was hit hardest in Germany in spring 2020 and early 2021. Of 4203 individuals aged 14 years or older enrolled at baseline (BL, June 2020), 3546 participated at FU1 (November 2020) and 3391 at FU2 (April 2021). Key metrics comprising standardized seroprevalence, surveillance detection ratio (SDR), infection fatality ratio (IFR) and success of the vaccination campaign were derived using the Roche N- and S-Elecsys anti-SARS-CoV-2 test together with a self-administered questionnaire. N-seropositivity at BL was 9.2% (1st wave). While we observed a low new seropositivity between BL and FU1 (0.9%), the combined 2nd and 3rd wave accounted for 6.1% new N-seropositives between FU1 and FU2 (ever seropositives at FU2: 15.4%). The SDR decreased from 5.4 (BL) to 1.1 (FU2) highlighting the success of massively increased testing in the population. The IFR based on a combination of serology and registration data resulted in 3.3% between November 2020 and April 2021 compared to 2.3% until June 2020. Although IFRs were consistently higher at FU2 compared to BL across age-groups, highest among individuals aged 70+ (18.3% versus 10.7%, respectively), observed differences were within statistical uncertainty bounds. While municipalities with senior care homes showed a higher IFR at BL (3.0% with senior care home vs. 0.7% w/o), this effect diminished at FU2 (3.4% vs. 2.9%). In April 2021 (FU2), vaccination rate in the elderly was high (>77.4%, age-group 80+).
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| 2. |
- Günther, Felix, et al.
(författare)
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Higher Infection Risk among Health Care Workers and Lower Risk among Smokers Persistent across SARS-CoV-2 Waves - Longitudinal Results from the Population-Based TiKoCo Seroprevalence Study
- 2022
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1661-7827 .- 1660-4601. ; 19:24
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Tidskriftsartikel (refereegranskat)abstract
- SARS-CoV-2 seroprevalence was reported as substantially increased in medical personnel and decreased in smokers after the first wave in spring 2020, including in our population-based Tirschenreuth Study (TiKoCo). However, it is unclear whether these associations were limited to the early pandemic and whether the decrease in smokers was due to reduced infection or antibody response. We evaluated the association of occupation and smoking with period-specific seropositivity: for the first wave until July 2020 (baseline, BL), the low infection period in summer (follow-up 1, FU1, November 2020), and the second/third wave (FU2, April 2021). We measured binding antibodies directed to SARS-CoV-2 nucleoprotein (N), viral spike protein (S), and neutralizing antibodies at BL, FU1, and FU2. Previous infection, vaccination, smoking, and occupation were assessed by questionnaires. The 4181 participants (3513/3374 at FU1/FU2) included 6.5% medical personnel and 20.4% current smokers. At all three timepoints, new seropositivity was higher in medical personnel with ORs = 1.99 (95%-CI = 1.36–2.93), 1.41 (0.29–6.80), and 3.17 (1.92–5.24) at BL, FU1, and FU2, respectively, and nearly halved among current smokers with ORs = 0.47 (95%-CI = 0.33–0.66), 0.40 (0.09–1.81), and 0.56 (0.33–0.94). Current smokers compared to never-smokers had similar antibody levels after infection or vaccination and reduced odds of a positive SARS-CoV-2 result among tested. Our data suggest that decreased seroprevalence among smokers results from fewer infections rather than reduced antibody response. The persistently higher infection risk of medical staff across infection waves, despite improved means of protection over time, underscores the burden for health care personnel.
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| 3. |
- Günther, Felix, et al.
(författare)
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Nowcasting the COVID-19 pandemic in Bavaria
- 2021
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Ingår i: Biometrical Journal. - : Wiley. - 0323-3847 .- 1521-4036. ; 63:3, s. 490-502
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Tidskriftsartikel (refereegranskat)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 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.
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| 4. |
- Küchenhoff, Helmut, et al.
(författare)
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Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points
- 2021
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Ingår i: Epidemiology and Infection. - 0950-2688 .- 1469-4409. ; 149, s. 1-7
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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