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Abstract

The world faces an unprecedented SARS-CoV2 pandemic where many critical factors still remain unknown. The case fatality rates (CFR) reported in the context of the SARS-CoV-2 pandemic substantially differ between countries. For SARS-CoV-2 infection with its broad clinical spectrum from asymptomatic to severe disease courses, the infection fatality rate (IFR) is the more reliable parameter to predict the consequences of the pandemic. Here we combined virus RT-PCR testing and assessment for SARS-CoV2 antibodies to determine the total number of individuals with SARS-CoV-2 infections in a given population. Methods: A sero-epidemiological GCP- and GEP-compliant study was performed in a small German town which was exposed to a super-spreading event (carnival festivities) followed by strict social distancing measures causing a transient wave of infections. Questionnaire-based information and biomaterials were collected from a random, household-based study population within a seven-day period, six weeks after the outbreak. The number of present and past infections was determined by integrating results from anti-SARS-CoV-2 IgG analyses in blood, PCR testing for viral RNA in pharyngeal swabs and reported previous positive PCR tests. Results: Of the 919 individuals with evaluable infection status (out of 1,007; 405 households) 15.5% (95% CI: [12.3%; 19.0%]) were infected. This is 5-fold higher than the number of officially reported cases for this community (3.1%). Infection was associated with characteristic symptoms such as loss of smell and taste. 22.2% of all infected individuals were asymptomatic. With the seven SARS-CoV-2-associated reported deaths the estimated IFR was 0.36% [0.29%; 0.45%]. Age and sex were not found to be associated with the infection rate. Participation in carnival festivities increased both the infection rate (21.3% vs. 9.5%, p<0.001) and the number of symptoms in the infected (estimated relative mean increase 1.6, p=0.007). The risk of a person being infected was not found to be associated with the number of study participants in the household this person lived in. The secondary infection risk for study participants living in the same household increased from 15.5% to 43.6%, to 35.5% and to 18.3% for households with two, three or four people respectively (p<0.001). Conclusions: While the number of infections in this high prevalence community is not representative for other parts of the world, the IFR calculated on the basis of the infection rate in this community can be utilized to estimate the percentage of infected based on the number of reported fatalities in other places with similar population characteristics. Whether the specific circumstances of a super-spreading event not only have an impact on the infection rate and number of symptoms but also on the IFR requires further investigation. The unexpectedly low secondary infection risk among persons living in the same household has important implications for measures installed to contain the SARS-CoV-2 virus pandemic.
Infection fatality rate of SARS-CoV-2 infection in a German
community with a super-spreading event
Hendrik Streeck1, Bianca Schulte1, Beate M. Kümmerer1, Enrico Richter1, Tobias Höller5,
Christine Fuhrmann5, Eva Bartok4, Ramona Dolscheid4, Moritz Berger3, Lukas Wessendorf1,
Monika Eschbach-Bludau1, Angelika Kellings5, Astrid Schwaiger6, Martin Coenen5, Per
Hoffmann7, Birgit Stoffel-Wagner4, Markus M. Nöthen7, Anna-Maria Eis-Hübinger1, Martin
Exner2, Ricarda Maria Schmithausen2, Matthias Schmid3 and Gunther Hartmann4
1 Institute of Virology, University Hospital, University of Bonn, Germany, and German Center
for Infection Research (DZIF), partner site Bonn-Cologne
2 Institute for Hygiene and Public Health, University Hospital, University of Bonn, Germany
3 Institute for Medical Biometry, Informatics and Epidemiology, University Hospital, University
of Bonn, Germany
4 Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital, University of
Bonn, Germany; German Center for Infection Research (DZIF), partner site Bonn-Cologne
5 Clinical Study Core Unit, Study Center Bonn (SZB), Institute of Clinical Chemistry and Clinical
Pharmacology, University Hospital, University of Bonn, Germany
6 Biobank Core Unit, University Hospital, University of Bonn, Germany
7 Institute of Human Genetics, University Hospital, University of Bonn, Germany
Co-corresponding authors:
Hendrik Streeck, Institute of Virology, University Hospital Bonn, Venusberg-Campus 1, 53127
Bonn, Germany
E-Mail: hendrik.streeck@ukbonn.de
Gunther Hartmann, Institute of Clinical Chemistry and Clinical Pharmacology, University
Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
E-Mail: gunther.hartmann@ukbonn.de
2
Abstract
The world faces an unprecedented SARS-CoV2 pandemic where many critical factors still
remain unknown. The case fatality rates (CFR) reported in the context of the SARS-CoV-2
pandemic substantially differ between countries. For SARS-CoV-2 infection with its broad
clinical spectrum from asymptomatic to severe disease courses, the infection fatality rate (IFR)
is the more reliable parameter to predict the consequences of the pandemic. Here we
combined virus RT-PCR testing and assessment for SARS-CoV2 antibodies to determine the
total number of individuals with SARS-CoV-2 infections in a given population.
Methods: A sero-epidemiological GCP- and GEP-compliant study was performed in a small
German town which was exposed to a super-spreading event (carnival festivities) followed by
strict social distancing measures causing a transient wave of infections. Questionnaire-based
information and biomaterials were collected from a random, household-based study population
within a seven-day period, six weeks after the outbreak. The number of present and past
infections was determined by integrating results from anti-SARS-CoV-2 IgG analyses in blood,
PCR testing for viral RNA in pharyngeal swabs and reported previous positive PCR tests.
Results: Of the 919 individuals with evaluable infection status (out of 1,007; 405 households)
15.5% (95% CI: [12.3%; 19.0%]) were infected. This is 5-fold higher than the number of
officially reported cases for this community (3.1%). Infection was associated with characteristic
symptoms such as loss of smell and taste. 22.2% of all infected individuals were asymptomatic.
With the seven SARS-CoV-2-associated reported deaths the estimated IFR was 0.36%
[0.29%; 0.45%]. Age and sex were not found to be associated with the infection rate.
Participation in carnival festivities increased both the infection rate (21.3% vs. 9.5%, p<0.001)
and the number of symptoms in the infected (estimated relative mean increase 1.6, p=0.007).
The risk of a person being infected was not found to be associated with the number of study
participants in the household this person lived in. The secondary infection risk for study
participants living in the same household increased from 15.5% to 43.6%, to 35.5% and to
18.3% for households with two, three or four people respectively (p<0.001). Conclusions:
While the number of infections in this high prevalence community is not representative for other
parts of the world, the IFR calculated on the basis of the infection rate in this community can
be utilized to estimate the percentage of infected based on the number of reported fatalities in
other places with similar population characteristics. Whether the specific circumstances of a
super-spreading event not only have an impact on the infection rate and number of symptoms
but also on the IFR requires further investigation. The unexpectedly low secondary infection
risk among persons living in the same household has important implications for measures
installed to contain the SARS-CoV-2 virus pandemic.
3
Introduction
Introduction
The novel SARS-CoV-2 coronavirus causing a respiratory disease termed COVID-191,2 is
affecting almost every country worldwide3. One of the reasons for its rapid spread is its ability
to transmit already during the asymptomatic phase of infection, reported to be responsible for
approximately 40% of SARS-CoV-2 transmission events4,5. As the COVID-19 pandemic
continues to grow in extent, severity, and socio-economic consequences, its fatality rate
remains unclear. SARS-CoV-2 infection presents with a broad spectrum of clinical courses
from asymptomatic to fatal, complicating the definition of a 'case'. About 80-91% of the
infections have been reported to show only mild to moderate symptoms including sore throat,
dry cough and fever6. These are currently often left undiagnosed. Together with different PCR
test capacities and different regulations for testing, the ratio of SARS-CoV-2-associated deaths
to overall reported cases (case fatality rate, CFR) inherently differs between countries 7. The
current estimate of the CFR in Germany by the World Health Organization (WHO) is between
2.2% and 3.4%3. The data basis, however, for calculating the CFR is weak, with the
consequence that epidemiological modeling is currently associated with a large degree of
uncertainty. However, epidemiological modeling is urgently needed to design the most
appropriate prevention and control strategies to counter the pandemic and to minimize
collateral damage to societies.
Unlike the CFR, the infection fatality rate (IFR) includes the whole spectrum of infected
individuals, from asymptomatic to severe. The IFR is recommended as a more reliable
parameter than the CFR for evidence-based assessment of the SARS-CoV-2 pandemic
(Center for Evidence-Based Medicine, CEBM in Oxford). The IFR includes infections based
on both PCR testing and virus-specific antibodies. Mild and moderate disease courses are
also included, which tend to not be captured and documented by PCR testing alone. Active
infections before seroconversion are included into IFR-calculation by PCR testing. In this
testing scheme, only those individuals may be missed who already became negative in the
PCR test but have not yet reached antibody levels above the threshold of the antibody
detection assay8.
Recently commercial assays became available with a specificity of up to 99% to allow for a
reliable serological analysis of SARS-CoV-2-specific antibodies9. Of note, lower specificities of
ELISA tests reported in the literature are partially due to the use of beta versions of the ELISA
and to different calculation algorithms (>0.3 log(Ig ratio)) defining positive values10.
Furthermore, even an assay with a validated specificity of 99% has limitations in its accuracy
to reliably identify infected individuals in populations with low seroprevalence (e.g. <1 %). We
chose the community of Gangelt, where due to a super-spreading event, the officially reported
cases were 3% (time of study period). In this community, carnival festivities around February
15th were followed by a massive outbreak of SARS-CoV-2 infections. Strict measures including
a suggested curfew were immediately taken to slow down further spreading of the infection.
Given its relatively closed community with little tourism and travel, this community was
identified as an ideal model to better understand SARS-CoV2 spreading, prevalence of
symptoms, as well as the infection fatality rate. The results presented here were obtained in
the context of the larger study program termed COVID-19-Case-Cluster Study. The parts of a
larger study program which are presented here were specifically designed to determine the
total number of infected and the IFR. In addition, the spectrum of symptoms, as well as the
associations with age, sex, household size, co-morbidities and participation in carnival
festivities, were examined.
4
Materials and methods
Study design
This study was conducted between March 31st, 2020 and April 6th, 2020 in Gangelt, a
community with 12,597 inhabitants (as of Jan 1st, 2020) located in the German county of
Heinsberg in North Rhine-Westphalia. For this cross-sectional epidemiological study, all
inhabitants of Gangelt were eligible. Enrollment was based on a sample of 600 persons
contained in the Heinsberg civil register ("Melderegister"), which is the public authority that
collects all names and addresses of the inhabitants of Gangelt. All study participants provided
written and informed consent before enrolment. For children under 18 years, written and
informed consent was provided by the persons with care and custody of the children following
aged-adapted participant information. In addition to the data provided by the study participants,
aggregated data on mortality and socio-demographic characteristics were collected. The latter
data were provided by the district administration of Heinsberg and the Statistics & IT Service
of the German federal state of North Rhine-Westphalia. The study was approved by the Ethics
Committee of the Medical Faculty of the University of Bonn (approval number 085/20) and has
been registered at the German Clinical Trials Register (https://www.drks.de, identification
number DRKS00021306). The study was conducted in accordance with good clinical (GCP)
and epidemiological practice (GEP) standards and the Declaration of Helsinki.
Sampling and procedures
Based on the sample size recommendations of the World Health Organisation (WHO) (see
below), the aim was to collect data from at least 300 households in Gangelt. To reach this
target, a sample of 600 persons aged older than 18 years was drawn from the civil register.
Sampling was done randomly under the side condition that all 600 persons had different
surnames, as it was assumed that different surnames were likely to indicate different
households. After sampling, the 600 selected persons were contacted by mail and were invited
to the study acquisition center, which was established at the site of a public school in Gangelt.
The letters sent to the 600 selected persons also included invitations for all persons living in
the respective households to participate in the study. Persons aged older than 80 years or
immobile were offered the opportunity to be visited at home. After having provided written and
informed consent, study participants completed a questionnaire querying information including
demographics, symptoms, underlying diseases, medication and participation in carnival
festivities (main carnival session "Kappensitzung" and others). Furthermore, study participants
were asked to provide blood specimens and pharyngeal swabs. Blood was centrifuged and
EDTA-plasma was stored until analysis (-80°C). Analyses were performed in batches at the
central laboratory of the University Hospital Bonn (UKB), which is accredited according to DIN
EN ISO 15189:2014. Anti-SARS-CoV-2 IgA and Anti-SARS-CoV-2 IgG were determined with
enzyme-linked immunosorbent assays (ELISA) on the EUROIMMUN Analyzer I platform (most
recent CE version for IgG ELISA as of April 2020, specificity 99.1%, sensitivity 90.9%, data
sheet as of April 7, 2020, validated in cooperation with the Institute of Virology of the Charité
in Berlin, and the Erasmus MC in Rotterdam, Euroimmun, Lübeck, Germany). The data sheet
(April 7, 2020) reports cross-reactivities with anti-SARS-CoV-1-IgG-antibodies, but not with
MERS-CoV-, HCoV-229E-, HCoV-NL63-, HCoV-HKU1- or HCoV-OC43-IgG antibodies. In our
study, infected included positives (ratio of 1.1 or higher, 91% positive in neutralization assay)
and equivocal positives (ratio 0.8 to 1.1, 56% positive in neutralization assays). Assays were
performed in line with the guidelines of the German Medical Association (RiliBÄK) with
stipulated internal and external quality controls. Pharyngeal swabs were stored in UTM Viral
5
Stabilization Media at 4 °C at the study acquisition center for up to four hours. The cold chain
remained uninterrupted during transport. At the Institute of Virology of the UKB swab samples
were homogenized by short vortexing, and 300 µl of the media containing sample were
transferred to a sterile 1.5 ml microcentrifuge tube and stored at 4 °C. Viral RNA was extracted
on the chemagic™ Prime™ instrument platform (Perkin Elmer) using the chemagic Viral 300
assay according to manufacturer’s instructions. The RNA was used as template for three real
time RT-PCR reactions (SuperScript™III One-Step RT-PCR System with Platinum™ TaqDNA
Polymerase, Thermo Fisher) to amplify sequences of the SARS-CoV-2 E gene (primers
E_Sarbeco_F1 and R, and probe E_Sarbeco_P111), the RdRP gene (primers
RdRP_SARSr_F, and R, and probe RdRP_SARSr-P211), and an internal control for RNA
extraction, reverse transcription, and amplification (innuDETECT Internal Control RNA Assay,
Analytik Jena #845-ID-0007100). Samples were considered positive for SARS-CoV-2 if
amplification occurred in both virus-specific reactions. All PCR protocols and materials were
used according to clinical diagnostics standards and guidelines of the Virology Diagnostics
Department of the UKB. Neutralization assays were performed using a SARS-CoV-2 strain
isolated in Bonn from a throat swab of a patient from Heinsberg. Plasma samples from study
participants were inactivated at 56°C for 30 min. In a first round, neutralizing activity was
analyzed by a microneutralization test using 100 TCID50 similar as described12. Serial 2-fold
dilutions (starting dilution 1:2, 50 µl per well) of plasma were performed and mixed with equal
volumes of virus solution. All dilutions were made in DMEM (Gibco) supplemented with 3%
fetal bovine serum (FBS, Gibco) and each plasma dilution was run in triplicate. After incubation
for 1 h at 37°C, 2x104 Vero E6 cells were added to each well and the plates were incubated at
37°C for 2 days in 5% CO2 before evaluating the cytopathic effect (CPE) via microscopy. In
each experiment, plasma from a SARS-CoV-2 IgG negative person was included and back
titration of the virus dilution was performed. Titers were calculated according to the Spearman-
Kaerber formula13 and are presented as the reciprocals of the highest plasma dilution
protecting 50% of the wells. To further assess the neutralizing activity of plasma samples
exhibiting neutralizing antibody titers below 2.8 in the microneutralization test, a plaque
reduction neutralization test was performed. To this end, heat inactivated plasma samples
were serially two-fold diluted starting with 1: 2 up to 1:1,024. 120 µl of each plasma dilution
was mixed with 100 plaque forming units (PFU) of SARS-CoV-2 in 120 µl OptiPROTMSFM
(Gibco) cell culture medium. After incubation of 1 h at 37°C, 200 µl of each mixture were added
to wells of a 24 well plate seated the day before with 1.5x105 Vero E6 cells/well. After
incubation for 1 h at 37°C, the inoculum was removed and cells were overlayed with a 1:1
mixture of 1.5% carboxymethylcellulose (Sigma) in 2xMEM (Biochrom) with 4% FBS (Gibco).
After incubation at 37°C for three days in 5% CO2, the overlay was removed and the 24 well
plates were fixed using a 6% formaldehyde solution and stained with 1% crystal violet in 20%
ethanol.
Data management and quality control
Planning and conduct of the study were supported by the Clinical Study Core Unit
(Studienzentrale) of the Study Centre Bonn (SZB). Support included protocol and informed
consent development following specifications of the World Health Organization with regards to
pandemic events14, data management, submission to the ethics committee, clinical trial
monitoring and quality control. Study data were collected and managed using REDCap
electronic data capture tools hosted at Institute for Medical Biometry, Informatics and
Epidemiology13, 14. REDCap (Research Electronic Data Capture) is a secure, web-based
software platform designed to support data capture for research studies, providing 1) an
intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and
6
export procedures; 3) automated export procedures for seamless data downloads to common
statistical packages; and 4) procedures for data integration and interoperability with external
sources. Questionnaire data were recorded on site using paper case report forms and were
entered into the electronic study database using double data entry by trained study personnel.
Comparisons between entries were made by the data management unit of the SZB; non-
matches were corrected, and duplicated entries were deleted, after assessing the original
paper case report forms. Additionally, plausibility checks of demographic data were performed.
Study personnel were trained with respect to informed consent and study procedures prior to
inclusion of first study participant. The study team was supported on site in Gangelt by a quality
control manager who refined workflow processes and monitored critical processes such as
obtaining informed consent. Furthermore, regulatory advice could be given whenever asked
for or needed. Data entry personnel was trained for double data entry prior to data entry and
only then granted database access authorization. Contact with the responsible data managers
could be established when needed. Diagnostic data were imported into the trial database
automatically via validated interfaces. Following the completion of the study, critical data was
monitored by an experienced clinical trial monitor which included (but was not limited to) a
check of availability of source data (completed questionnaires), random source data
verification of diagnostic data and a check of signatures of all informed consent forms obtained.
Statistical analysis
In the absence of any pilot data on SARS-CoV-2 infection rates in Gangelt, sample size
calculations were based on the WHO population-based age-stratified seroepidemiological
investigation protocol for COVID-19 virus infection14. According to the recommendations stated
in the protocol, a size of 200 samples is sufficient to estimate SARS-CoV-2-prevalence rates
<10% with an expected margin of error (defined by the expected width of the 95% confidence
interval associated with the seroprevalence point estimate obtained using binomial likelihood)
smaller than 10%. In order to rule out larger margins of error due to dependencies of persons
living in the same household and to be able to analyze seroprevalence (i.e., infection rates)
also in subgroups defined by participant age, it was planned to recruit 1,000 participants living
in at least 300 households. Statistical analysis was carried out by two independently working
statisticians (MS, MB) using version 3.6.1 of the R Language for Statistical Computing (R Core
Team 2019: R: A Language and Environment for Statistical Computing. R Foundation for
Statistical Computing, Vienna, Austria) and version 9.4 of the SAS System for Windows
(copyright © 2002-2012 by SAS Institute Inc., Cary, NC, USA). Participants with a missing anti-
SARS-CoV-2 IgG/A or PCR test result were excluded from analysis, as they were not
evaluable for infection status (Fig. 1B). Participants that did not report a previous positive PCR
test result were documented as PCRrep negative. Missing and unknown values in the co-
morbidity and symptom variables were not imputed, as listwise deletion reduced sample sizes
by less than 5%. Age groups were formed according to the classification system of the Robert
Koch Institute (RKI), which is the German federal government agency and research institute
responsible for infectious disease control and prevention.
Descriptive analyses included the calculation of means (plus standard deviations, sds) and
medians (plus minimum and maximum values) for continuous variables, and numbers (n, with
percentages) for categorical variables. Associations between continuous variables were
analyzed using the Pearson correlation coefficient (r).
Generalized estimation equations (GEE)15 with exchangeable correlation structure within
household clusters were used to adjust point estimates and confidence intervals (CIs) for
possible dependencies between participants living in the same household. By definition, GEE
models employ quasi-likelihood methods to obtain point estimates and CIs. Adjustments for
7
possible sex and age effects were made by including these variables as additional covariables
in the GEE models. One person of diverse sex (Table 1) was excluded from the models
including sex as covariable. For binary outcomes (e.g. infection status), GEE models with a
logistic link function were applied. Results of logistic GEE models are presented in terms of
either back-transformed mean estimates (GEE models with a single covariable) or odds ratios
(ORs, GEE models with 1 covariables). For count data (e.g. number of symptoms), Poisson
GEE models with a logarithmic link function were used. Results of Poisson GEE models are
presented in terms of either back-transformed mean estimates (GEE models with a single
covariable) or estimated relative mean increases/decreases (GEE models with 1
covariables). For all GEE models, the estimated correlation between participants living in the
same household cluster (rho) is reported. On a household-level basis (with households
assumed to be independent sampling units), quasi-Poisson models with offset values defined
by the logarithmized household cluster size were applied. Wald tests were used to test
covariables for statistical significance.
All CIs presented in this work were computed using the 95% level. CIs are Wald CIs and were
not adjusted for multiple comparisons unless otherwise stated. All statistical hypothesis tests
were two-sided; p-values < 0.05 were considered significant. The Bonferroni-Holm procedure
was applied to adjust p-values for multiple comparisons as indicated.
Infection rates obtained from IgG and IgA measurements were additionally corrected for
possible misclassification bias using the matrix method16, with sensitivity and specificity values
obtained from the ELISA manufacturer’s (Euroimmun, Lübeck, Germany) validation data sheet
(version: April 7, 2020). No adjustments were made for age and sex, as these variables were
not found to be associated with infection status (Fig. 6A). To account for possible clustering
effects due to participants living in the same household, confidence intervals for the corrected
infection rate estimates were computed using a cluster bootstrap procedure with 10,000
bootstrap samples17. With this procedure, household clusters were sampled with replacement.
Within sampled clusters, no additional resampling of household members was carried out. The
distributions of the bootstrapped corrected infection rate estimates were symmetrical and close
to normality (as indicated by normal quantile-quantile plots), and the percentile method was
applied to calculate CI limits. Note: Throughout the paper, the term rate refers to the number
of persons experiencing an event divided by the number of the reference population, in line
with the definition of the IFR18. We adopted this definition due to its widespread use in the
context of COVID-19 research, keeping in mind that “rates” are usually defined in terms of
person-time (e.g. Rothman et al19).
Results
Study design and study population
The major objective of this study was to determine the total number of individuals infected by
SARS-CoV-2 in the given defined population. This number together with the reported SARS-
CoV-2-associated fatalities in that same population allows the calculation of the infection
fatality rate (IFR, according to Centre for Evidence-Based Medicine, CEBM, Oxford University,
to be distinguished from case fatality rate, CFR). In the German community Gangelt (12,597
inhabitants), a super-spreading event (carnival festivities incl. "Kappensitzung" on February
15, 2020), was followed by numerous measures starting February 28 (shut-down) to limit the
further spread of infections (Fig. 1A). This local infection hotspot was closely monitored by
health authorities, and a high PCR test rate revealed an increase in officially reported cases,
8
with a maximum around March 13 when 85 individuals tested PCR positive for SARS-CoV-2
in a 4-day period. Numbers declined afterwards down to 48 PCR positive cases officially
reported during the 7-day period of the present study (March 30th - April 6th), not counting the
33 new PCR positives detected in this study. The total number of officially reported PCR
positives on April 6th was 388, also excluding the 33 PCR positives of this study. By the end of
the 7-day study period, a total of 7 SARS-CoV-2-positive individuals had died in the community
of Gangelt since the super-spreading event (average age 80.8 years, sd ± 3.5 years). In
January, February and March 2020, a total of 48 people died in Gangelt, which was 3 people
more than in the same period the year before. At the start date of data and material acquisition
of the study, 340 PCR positives were reported in the community which is 2.7% of the
population.
Our study design addressed the recommendations for COVID-19 studies by the WHO14. For
the study, 600 adult persons with different surnames in Gangelt were randomly selected, and
all household members were asked to participate in the study. Data and materials were
collected over a 7-day period (March 30 to April 6) six weeks after super-spreading event. Of
the 1,007 individuals participating in the study, 987 individuals were seen in the local study
acquisition center in a community school, and 20 individuals were visited in their homes due
to age or limited mobility. Complete information from both pharyngeal swabs and blood
samples was available for 919 study participants living in 405 households (Fig. 1B). The
demographic characteristics of the study participants, including age, sex and the number of
participants living in the same household, are summarized in Table 1. The comparison of age
groups in the study population to the community Gangelt, to the state North Rhine-Westphalia,
(NRW) and to Germany is illustrated in supplementary figure 1. Characteristics of the 88
study participants who were not evaluable for infection status, mainly children due to lack of
biomaterials, are provided in the supplementary table.
Number of SARS-CoV-2-infected and infection fatality rate (IFR)
The analysis of IgA and IgG levels measured in plasma samples of all study participants by
ELISA (Euroimmun) showed a positive correlation (r=0.778, CI 95%: [0.751-0.802]: Fig. 2A).
While 18.50% of all study participants were found to be IgA positive, 13.60% were IgG positive
(Fig. 2B). Correction for sensitivity and specificity of the ELISA (specificity 99.1%, sensitivity
90.9%) revealed a much lower corrected value of 10.63% [7.48%; 13.88%] for IgA and a
slightly higher value of 14.11% [11.15%; 17.27%] for IgG (Fig. 2B). The higher specificity of
the IgG ELISA (99.1%, validation reported April 7, 2020 by company based on 1,656 samples)
compared to IgA ELISA (91.2%) was confirmed by our own independent analysis of control
samples (specificity 98.3%: 1 positive in 68 samples of healthy control individuals, 1 positive
in 32 samples of patients with cardiovascular disease, 0 positive in 9 samples of 7 patients
with PCR-confirmed infection with endemic coronaviruses). To illustrate the difference
between a specificity of 99% and 98%, the correction for specificity of 98% is added in light
gray (Fig. 2B,C). Based on these data, a "seropositive" study participant was defined as being
positive for IgG (mean of values corrected for sensitivity and specificity of all study participants;
Fig. 2B). The neutralization activity of IgG-positive plasma samples was analyzed using a
microneutralization assay combined with a plaque reduction neutralization test. Results are
shown in Suppl. Fig. 2.
To determine the total number of infected individuals, all study participants were tested for the
presence of virus in pharyngeal swabs by SARS-CoV-2 PCR in addition to serology. Of the
919 participants of the study, 33 tested positive (PCRnew: 3.59%). Furthermore, based on the
information collected from the questionnaire, 22 study participants reported that they had had
9
a SARS-CoV-2 positive PCR test in the past (PCRrep: 2.39%). The combination of serology
(non-corrected IgG values) and past and present PCR testing yielded a total number of 138
study participants (15.02%) that had been previously or were at that time point infected by
SARS-CoV-2 as illustrated in Fig. 2C. The inclusion of IgG values corrected for sensitivity and
specificity in the calculation resulted in an estimated 15.53% [12.31%; 18.96%] cumulative
SARS-CoV-2-infected of all study participants.
To determine the infection fatality rate (IFR), the estimated infection rate of 15.53% in the study
population was applied to the total population in the community (12,597) yielding an estimated
number of 1,956 [1,551; 2,389] infected people. With 7 SARS-CoV-2-associated deaths, as
reported to the authors by the local administration, the estimated IFR was 7/1,956 = 0.00358
[0.00293; 0.00451] (0.358% [0.293%; 0.451%]) (Fig. 3A) at the end of the acquisition period.
While the percentage of previously reported cases as collected from the questionnaire in the
study population was 2.39% (PCRrep+), the percentage of officially reported cases in the
community of Gangelt at the end of the study period (April 6) was 3.08% (388/12,597). This
indicates that previously SARS-CoV-2 diagnosed individuals were somewhat
underrepresented in our study, possibly due to previously diagnosed people not opting to
participate in the study given their known infection status, or for other reasons, such as
quarantine, not feeling well or hospitalization. Thus, applying the corresponding correction
factor (3.08% / 2.39% = 1.29) to the infection rate of 15.53% of our study population, the
resulting corrected infection rate was 19.98% [15.84%; 24.40%] (Fig. 3B). Accordingly, the
corrected higher infection rate reduced the IFR to an estimated 0.278% [0.228%; 0.351%] (Fig.
3C).
Infection rate, symptoms and intensity of disease
A number of symptoms have been reported to be associated with SARS-CoV-2 infection1. In
the questionnaire, study participants were asked to indicate whether they experienced any of
the described symptoms since the beginning of the pandemic February 15th. Noting that
symptoms may vary in both frequency (Table 2) and intensity, and that causal relationships
cannot be established by a cross-sectional study, the following symptoms were found to be
significantly associated with SARS-CoV-2 infection (based on IgG+, PCRrep+, PCRnew+, ranked
by odds ratios with 95% CIs, adjusted for sex and age, Bonferroni-Holm corrected p-values
indicated): loss of smell (OR: 19.06 [8.72; 41.68]; p<0.001), loss of taste (OR: 17.01 [8.49;
34.10]; p<0.001), fever (OR: 4.94 [2.87; 8.50]; p<0.001), sweats and chills (OR: 3.74 [2.31;
6.07]; p<0.001), fatigue (OR: 2.99 [1.97; 4.56]; p<0.001), cough (OR: 2.81 [1.92; 4.11];
p<0.001), muscle and joint ache (OR: 2.42 [1.46; 4.00]; p=0.005), chest tightness (OR: 2.32
[1.31; 4.11]; p=0.019), head ache (OR: 2.28 [1.46; 3.56]; p=0.003), sore throat (OR: 1.92 [1.25;
2.96]; p=0.017), and nasal congestion (OR: 1.91 [1.28; 2.85]; p=0.010). Not significant were
shortness of breath, other respiratory symptoms, stomach pain, nausea and vomiting (Table
2). The number of symptoms reported by an individual participant served as an indicator for
the intensity of the disease and was 2.18-fold higher (adjusted for sex and age, 95% CI: [1.78;
2.66]) in SARS-CoV-2-infected (IgG+, PCRrep+, PCRnew+) compared to participants without
infection (Fig. 4A, p<0.001). 22.22% of infected (IgG+, PCRrep+, PCRnew+) reported no
symptoms at all (Fig. 4B); for the other infected participants symptom numbers varied between
0 and 11 (Fig. 4B). IgG levels of infected study participants were not significantly associated
with the number of symptoms (Fig. 4C).
Association between household size and rate of infection
SARS-CoV-2 is thought to be highly contagious. As a consequence, people living in the same
household are expected to be at a much higher risk of infection. The average number of people
10
in household clusters examined in this study was 2.27 (sd = 1.11, range 1-6) compared to
Gangelt (2.44 as of 2011), the state NRW (2.02, as of Dec 2018) and Germany (1.99, as of
Dec 2018). Household clusters with 5 or more people were excluded from the analysis below
because of insufficient numbers (15 clusters). First, we analyzed whether the fact that an
individual person was part of a one-, two-, three- or four-person household cluster changed
the probability of this person being infected. We found that the infection risk was not associated
with the number of people in a household cluster (Fig. 5A). Second, we analyzed the infection
risk of a person in a household in which at least one other person was infected (Fig. 5B). Under
the theoretical assumption that there was no increased infection risk for a second, a third or a
fourth person in a household cluster in which one person was infected, the average risk in this
household cluster was calculated to be 0.578 (two-person household cluster; (1 + 0.1553) / 2),
0.4369 (three-person household cluster; (1 + 2 x 0.1553) / 3) or 0.3665 (four-person household
cluster; (1 + 3 x 0.1553) / 4) (Fig. 5B, lower gray curve). The estimated infection risk as
calculated from the data was significantly above the theoretical risk without enhanced
transmission (Fig. 5B, black curve, dotted lines indicate CI 95%). A significant association
between household cluster size and the per-person infection risk was found (Fig. 5B, p<0.001).
In a two-person household cluster, the estimated risk for the second infection increased from
15.53% to 43.59% [25.26%; 64.60%]; in a three-person household cluster the estimated risk
for the second and third persons increased from 15.53% to 35.71% [19.57%; 55.60%] each,
and in a four-person household cluster the estimated risk for the second, third and fourth
persons increased from 15.53% to 18.33% [9.67%; 28.74%] each. For household clusters with
at least one infected child (< 18 years), the estimated per-person risk for the other person to
be infected in three-person household clusters increased from 15.53% to 66.67% [21.83%,
100.00%] and in four-person household clusters from 15.53% to 33.33% [9.02%; 71.60%].
Associations between sex, age, co-morbidities and super-spreading event with the rate of
infection, the number of symptoms and IgA/IgG
Sex and age were not associated with the rate of infection (Fig. 6A). Neither IgA nor IgG of
infected study participants showed significant associations with age or sex (suppl. Fig. 3). It
is well-established that severe disease courses and fatal outcomes of SARS-CoV-2 infection
are associated with the extent of underlying diseases, especially lung diseases with reduced
respiratory reserves and cardiovascular diseases. We therefore analyzed the associations
between co-morbidities on both the infection rate and the number of symptoms. In the
questionnaire, study participants were asked to report whether they had pre-existing diseases
or disease states, including lung diseases, cardiovascular diseases, neurological diseases and
stroke, cancer and diabetes. Neither increased rate of infection (Fig. 6B) nor a higher number
of symptoms were found in infected individuals (suppl. Fig. 4B). For infected study participants
the self-reported use of medications queried in the questionnaire (not in figure) (ibuprofen,
ACE inhibitors or AT1 agonists) all had no significant associations with the infection rate or
number of symptoms. Underlying morbidities of infected study participants were not associated
with Ig levels (suppl. Fig. 5).
Associations between celebrating carnival and rate of infection and number of symptoms
The impact of super-spreading events on the dynamics of the SARS-CoV-2 pandemic is well-
established20,21. The carnival festivities in Gangelt are mostly visited by local people staying in
the area after the event, therefore providing a unique setting to study the mechanisms of super-
spreading more closely than in events where people are traveling and thus disappearing from
a local study population. We analyzed whether celebrating carnival (main carnival session
"Kappensitzung" or other carnival festivities) was associated with the rate of infection and the
11
intensity of the infection, based on the number of symptoms. Study participants were asked to
indicate whether they had participated in carnival events. There was a significant positive
association between celebrating carnival and infection (OR = 2.56 [1.67; 3.93], p < 0.001, Fig.
6C). Furthermore, there was a significant positive association between celebrating carnival
and the number of symptoms in infected study participants (estimated relative mean increase:
1.63 [1.15; 2.33], p=0.007, Fig. 6D). While the percentage of asymptomatic infected
participants was 36% without celebrating carnival, only 16% who had celebrated carnival were
asymptomatic (Fig. 6E).
Discussion
One key parameter to assessing the potential impact that SARS-CoV-2 infection poses on
societies is the fatality rate. However, the fatality rate of 'cases' (case fatality rate, CFR) widely
varies between countries. 'Cases' do not cover the whole spectrum of SARS-CoV-2 infections
reaching from asymptomatic to lethal. Therefore, we set out to determine the infection fatality
rate (IFR) based on the total number of SARS-CoV-2-infected individuals. We chose the
German community Gangelt which had been exposed to a super-spreading event. A random
population sample revealed that an estimated 15.53% of the population in this community is
or was infected with the virus, which is 5-fold higher than the officially reported number of PCR-
positives. Based on the estimated percentage of infected people in this population, the IFR
was estimated to be 0.36% [0.29%; 0.45%]. Infection was highly associated with known
characteristic symptoms of SARS-CoV-2 infection such as loss of smell and taste. The risk of
being infected was not found to be associated with the number of participants living in the
same household, and the estimated risk to be infected in a household cluster with one person
already infected (secondary infection risk) was distinctly below 100%. The frequency of
infection did not significantly differ between age groups from children to the elderly and was
not found to be associated with sex. Co-morbidities such as underlying lung disease or
cardiovascular disease did not show associations with the risk of infection. Notably, this does
not contradict the well-established fact that co-morbidities such as lung disease predispose for
severe disease outcomes22,23. The use of ACE-inhibiting drugs or ibuprofen did not show an
association, as previously speculated24.
In our study, infection is defined as either PCR positive, anti-SARS-CoV2+ IgG seropositive or
both, thus including present and past infections. Since SARS-CoV-2 only arrived in February,
seropositives are expected to cover all infections except the very recent. This may become
different as the pandemic continues, since a decrease in antibody titers over time needs to be
considered in the calculation of the IFR. Furthermore, in our study, the number of reported
PCR positives (2.39%) was lower than in the overall population (3.08%) of this high-prevalence
community. This indicates that infected individuals may be underrepresented in our study
population. Although this is plausible (no response to study request due to illness, hospital,
ICU, already known infection status, etc.) and would lead to a correction by factor 1.29, we
chose to use the uncorrected lower percentage to conservatively estimate the total number of
infected and the resulting IFR in the population.
To determine the IFR, the collection of materials and information including the reported cases
and deaths was closed at the end of the study acquisition period (April 6th), and the IFR was
calculated based on those data. However, some of the individuals still may have been acutely
infected at the end of the study acquisition period (April 6th) and thus may have succumbed to
the infection later on. In fact, in the 2-week follow-up period (until April 20th) one additional
12
COVID-19 associated death was registered. The inclusion of this additional death would bring
up the IFR from 0.36% to an estimated 0.41% [0.33%; 0.52%].
Although the IFR is much less variable than the infection rate in different parts of the country,
the IFR may still be affected by certain circumstances. The community in which this study was
performed experienced a super-spreading event. The IFR was unlikely affected by an
overwhelmed health care system because sufficient numbers of ICU beds and ventilators were
available at all times. However, it is possible that the super-spreading event itself caused more
severe cases. In our study, we found a highly significant increase in both infection rate and
number of symptoms when people attended carnival festivities, as compared to people who
did not celebrate carnival. This association with carnival was at the same level when adjusted
for the age of the participants. At this point, the reason for the association with celebrating
carnival remains speculative. Thus far, we could not identify confounding factors that would
explain the observed difference. However, it is well established that the rate of particle
emission and superemission during human speech increases with voice loudness25. Because
of loud voices and singing in close proximity are common in carnival events, it is reasonable
to speculate that a higher viral load at the time of infection caused the higher intensity of
symptoms and thus more severe clinical courses of the infection. Notably, results from
experimental human influenza infection studies have demonstrated that the symptom score
depends on the viral dose administered26,27. Similar observations have been made for MERS28
and SARS29. Little is known about the infection dynamics of SARS-CoV-2. Future studies
designed to specifically analyze the infection chains after super-spreading events may provide
further insight. If substantiated, the IFR under strict hygiene measures might be lower than the
IFR in the context of a super-spreading event in this study, with important consequences for
the strategy against the pandemic. In this context, it is interesting to note that in our study, 22%
of infected individuals were asymptomatic, confirming previous reports of about 20%
asymptomatic carriers that contribute to the spread of infection30-32. Notably, asymptomatic
infected individuals in our study present with substantial antibody titers. Furthermore, since the
mean symptom number of non-infected in our study was 1.6 (of 15 symptoms), it would be
also appropriate to count infected study participants reporting up to 1 symptom as individuals
with no symptoms above the baseline level of uninfected study participants, thereby increasing
the percentage of asymptomatic infected individuals to 30.1%.
Given the high contagiousness of SARS-CoV-2, one would expect high rates of transmission.
However, in our study we found a relatively moderate increase of the secondary infection risk
which depended on the household cluster size (increase from 15.5% baseline risk by 28% for
two people, 20% for three people, 3% for four people). This finding is consistent with recent
observations of secondary infection risk of 16.3% in Chinese33 and 7.56% in South Korea34.
The reason for the comparably low secondary infection risk despite the high rate of
transmission is currently unknown, but it is seen with other respiratory infections such as
influenza (H1N1) 14.5%35 or SARS 14.9%36. Secondary household members may have
acquired a level of immunity (e.g. T cell immunity) that is not detected as positive by our ELISA,
but still could protect those household members from a manifest infection26,37.
To date, knowledge about SARS-CoV-2 immunity is rather scarce. Whether the Ig levels
detected in infected individuals in our study are protective and how long such a protection lasts
is not currently known. Virus neutralization control assays as performed in our study add
information, but do not provide evidence for the presence of an effective immunity. As other
tests, virus neutralization assays in general can be false positive, as cross-reactivity between
betacoronaviruses is well-known38,39. Likewise a lack of virus neutralization does also not
exclude a past infection as there is ample evidence that not all antibody responses neutralize
but still may provide some degree of protective immunity40,41. Therefore, at this point our study
13
uses IgG values as indicator whether an individual was infected but not as evidence for existing
immunity. However, one may assume that a certain degree of protection might exist even if
the IgG levels are below the threshold of the ELISA. Such individuals are not counted as
infected in our study, yet this hidden number of infected could possibly represent an important
component towards immunity in a population. The analysis of anti-SARS-CoV-2 IgM might
help to further close this window in the future.
Since i) a high degree of PCR testing was performed in this community by the health authorities
during the outbreak of SARS-CoV-2 infection, and ii) the outbreak was largely over, this
community was chosen as an ideal site to estimate the real number of infected individuals. It
is important to note that the infection rate in Gangelt is not representative for other regions in
Germany or other countries. However, with the limitations discussed above, the IFR calculated
here remains a useful metric for other regions with higher or lower infection rates. If in a
theoretical model the here calculated IFR is applied to Germany with currently approximately
6,575 SARS-CoV-2 associated deaths (May 2nd, 2020, RKI), the estimated number of infected
in Germany would be higher than 1.8 Mio (i.e. 2.2% of the German population). It will be very
important to determine the true average IFR for Germany. However, because of the currently
low infection rate of approximately 2% (estimated based on IFR), an ELISA with 99% specificity
will not provide reliable data. Therefore, under the current non-superspreading conditions, it is
more reasonable to determine the IFR in high prevalence hotspots such as Heinsberg county.
The data of the study reported here will serve as baseline for follow up studies on the delta of
infections and deaths to identify the corresponding IFR under those changed conditions.
Conflict of interest statement: None of the authors have conflict of interests to declare
(including financial, commercial, political or personal). The idea, the plan, the concept,
protocol, the conduct, the data analysis and the writing of the manuscript of this study was
independent of any third parties or the government of North Rhine-Westphalia.
Funding: The government of the German Federal State of North Rhine-Westphalia
unconditionally provided 65,000 Euro to support the study. No other financial support by any
third parties was received or used for the study.
Acknowledgement: HS, EB, MMN and GH are members of the Excellence Cluster
ImmunoSensation EXC 2151 - 390873048. We would like to thank the inhabitants of Gangelt
for their participation. We would also like to thank the local government of Kreis Heinsberg for
their support to conduct the study. We thank Sammy Bedoui and Art Krieg for critical reading
of the manuscript. Furthermore we would like to thank the following people, who helped with
the study:
Gero Wilbring, Janett Wieseler, Marek Korencak, Ryan Nattrass, Jernej Pusnik, Maximilian
Becker, Ann-Sophie Boucher, Marc Alexander de Boer, Rebekka Dix, Sara Dohmen, Kim
Friele, Benedikt Gansen, Jannik Geier, Marie Gronemeyer, Sarah Hundertmark, Nora Jansen,
Michael Jost, Louisa Khorsandian, Simon Krzycki, Ekaterina Kuskova Judith Langen, Silvia
Letmathe, Ann-Kathrin Lippe, Jonathan Meinke, Freya Merker, Annika Modemann, Janine
Petras, Sophie Marie Porath, Anna Quast, Laurine Reese, Isabel Maria Rehbach, Jonas
Richter, Thea Rödig, Eva Schmitz, Tobias Schremmer, Louisa Sommer, Jennifer Speda ,Yuhe
Tang, Oliver Thanscheidt, Franz Thiele, Johanna Thiele, Julia Tholen, Sophia Tietjen, Moritz
Transier, Maike van der Hoek, Tillmann Verbeek, Sophia Verspohl, Kira Vordermark, Julian
14
Wirtz ,Marina Wirtz, Lisa Zimmer, Philip Koenemann, Adi Yaser, Lisa Anna, Katharina
Bartenschlager, Lisa Baum, Roxana Böhmer Romero, Diana de Braganca, Isabelle Engels,
Moritz Färber, Carina Fernandez Gonzalez, Lucia Maria Goßner, Victoria Handschuch,
Franziska Georgia Liermann, Steffan Meißner, Laura Racenski, Patrick, Denis Raguse,
Larissa Reiß, Maximilian Rölle, Franziska Scheele, Chiara Schwippert, Arlene Christin
Schwippert, Antonia Seifert, Joshua David Stockhausen, Sofia Waldorf, Leonie Weinhold,
Nicolai Trimpop, Julia Reinhardt, Vera Gast, Michelle Yong, Eva Engels, Jonathan Meinke,
Susanne Schmidt, Janine Schulte, Saskia Schmitz, Kübra Bayrak, Regina Frizler, Katarzyna
Andryka, Sofía Soler, Thomas Zillinger, Marcel Renn, Patrick Müller, Dillon Corvino, Zeinab
Abdullah, Katrin Paeschke, Hiroki Kato, Daniel Hinze, Martina Schmidt, Arcangelo Ricchiuto,
Sonja Gross, Uta Wolber, Marion Zerlett, Esther Sib, Benjamin Marx, Souhaib Aldabbagh.
We thank Stefan Holdenrieder, Alexander Semaan, Bernd Pötzsch and Georg Nickening for
providing control samples.
15
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17
Figure legends
Fig. 1. A: Timeline of the SARS-CoV-2 superspreading event. On February 15th, 2020, a
carnival celebration became a SARS-CoV2 superspreading event in the German community
of Gangelt. The resulting increase in SARS-CoV-2-infected people was instantly countered by
a complete shutdown (schools, restaurants, stores, etc.). As a result, the number of reported
cases (PCRrep) reached its peak around March 13 (85 reported in a four-day period) and
declined thereafter, with 48 new cases reported in the seven-day study period (March 30 to
April 6). Thus, at the starting point of the study (March 30), the main wave of new infections
had already passed. The number of PCR-positive cases found in the study population (PCRnew)
was 33 (four of those reported PCR positive in the past). This situation in the community of
Gangelt was ideal to assess the cumulative real number of SARS-CoV-2-infected individuals
(area within dotted line: PCRrep, PCRnew and anti-SARS-CoV-2 IgG/A). B: Enrolment and flow
of participants through the study.
Fig. 2. IgA and IgG levels and number of SARS-CoV2 infected in the study population. IgA
and IgG were quantified (Euroimmun ELISA) in single plasma samples obtained from the study
participants at one time point during the seven-day acquisition period of the study. A: IgG
plotted against IgA in plasma of 919 study participants (log-scale, r = 0.778). The gray line
indicates equality of log(IgA) and log(IgG). B: Estimated percentage of IgA reactives (> 0.8;
black circle: 10.63% [7.48%; 13.88%]; gray circle: raw sample proportion 170/919, 18.50%)
and IgG reactives (>0.8; black circle: 14.11% [11.15% - 17.27%]; gray circle: raw sample
proportion, 125/919, 13.60%). Estimates were corrected for household clustering (cluster
bootstrap) and for sensitivity and specificity (matrix method) of the IgA (sensitivity 100%;
specificity 91.2%) and IgG (sensitivity 90.9%; specificity 99.1%) ELISA (validation sheet
version of April 7, 2020). C: The absolute numbers of IgG reactives (rectangle with black
border), PCRnew positives (rectangle with dashed border, left side) and PCRrep positives
(rectangle with dashed border, right side) as well as the respective overlaps of values are
depicted (percentages in brackets). The number of infected (total grey area) is defined as study
participants positive for at least one of either IgG, PCRnew or PCRrep (138/919, 15.02%; raw
percentages not corrected for sensitivity and specificity of the ELISA).
Fig. 3. Estimation of the SARS-CoV2 infection rate and the IFR. A: The number of SARS-CoV-
2-positive reported cases in the study population is known from the questionnaire (r = 22). The
observed number of infected in the study population is known from the data available (at least
one of either IgG+, PCRnew+ or PCRrep+, i = 138). The ratio of i / n (study participants, 919) =
0.1502 is a raw estimate of the number of infected in the whole population of Gangelt (i =
0.1502 x 12,597 1,892). A raw estimate of the IFR in Gangelt is therefore given by the number
of SARS-CoV-2-associated deaths (f = 7) / (i = 1,892) = 0.370%. B: The infection rate
estimated from the IgG and PCR data in the study population, corrected for both
sensitivity/specificity of the ELISA (matrix method) and household clustering (cluster
bootstrap), is 15.53% [12.31%; 18.96%] (left bar, dark gray). An additional correction was
made for the underrepresentation of reported PCR positive (PCRrep+) in the study population
(22/919 = 0.0239) as compared to the real proportion of PCRrep+ in Gangelt (388/12,597 =
0.0308), increasing the infection rate by the factor 0.0308/0.0239 = 1.2866 to 19.98% [15.84%;
24.40%] (third bar from left, dark gray). The bars in light gray depict the values corrected for a
theoretical specificity of the ELISA of 98% (light gray) instead of the 99% provided on the data
sheet of the company. C: Infection fatality rate calculated based on the estimated infection
rates and the number of SARS-CoV-2-associated deaths (7 by the end of the acquisition
18
period, mean age 81.1 ± 3.3 years, age range 78 years to 85 years). Similar to the infection
rates in B, the estimated IFR of 0.36% [0.29%; 0.45%] (left bar) may be an estimate at the
upper limit of the real IFR in Gangelt. IFR estimates were obtained by dividing the number of
SARS-CoV-2-associated deaths (7) by the point estimates and 95% CI limits of the infection
rates in B.
Fig. 4. Number of symptoms and Ig in SARS-CoV-2-infected study participants. Clinical
symptoms reportedly associated with SARS-CoV-2-infection were analyzed (questionnaire
data). A: Estimated mean number of symptoms in non-infected study participants (1.61 [1.42;
1.81]) and SARS-CoV-2-infected study participants (3.58 [3.01; 4.27], Poisson GEE model,
estimated relative mean increase in infected = 2.23 [1.82; 2.73], p<0.001, rho = 0.248 [0.164;
0.332]; Poisson GEE model adjusted for age and sex: estimated relative mean increase in
infected = 2.18 [1.78; 2.66], p<0.001, rho = 0.250 [0.167; 0.333]). Results are based on the
876 study participants without missing values in any of the symptom items (range of the
observed numbers of symptoms: 0 to 12, mean = 1.92, sd = 2.59, median = 1). Bars refer to
the empirical mean values. B: Raw percentages of symptoms in the 126 SARS-CoV2-infected
study participants without missing values for any of the symptoms. Of the SARS-CoV-2
infected, 22.22% reported that they did not have any (most left bar on x-axis: 0) of the 15
symptoms. Numbers above bars indicate the total number of individuals in the respective
group. C: IgA and IgG levels and intensity of symptoms. The boxplots depict the log(IgA) (light
gray) and log(IgG) (dark gray) levels in the 126 infected study participants. In a quasi-Poisson
model, no significant association between the number of symptoms (response variable) and
log(IgA) (covariable) was found. Similar results were obtained from a quasi-Poisson model
with the number of symptoms as response variable and log(IgG) as covariable. Note: Quasi-
Poisson models were used instead of Poisson GEE models because the number of
households was large relative to the number of analyzed study participants (see
Supplementary figure 4 A).
Fig. 5: Association between household cluster size and the per-person infection risk. Due to
the random selection of households, study participants were clustered within households. A:
Estimated per-person infection risk by household cluster size (black dots; 95% CIs: gray lines).
Estimates and CI limits were determined by fitting a quasi-Poisson model with the number of
infected persons as response variable and household cluster size as a factor covariable.
Additionally, log(household cluster size) was included as offset. No association between
household cluster size and the per-person infection risk was found (p = 0.936). B: Per-person
infection risk in household clusters in which at least one person was found infected (black
curve based on 86 household clusters, with 213 persons, gray curve, based on 7 household
clusters with 25 persons in which at least one infected child younger than 18 years was
infected. The gray line below the black curve show the expected per-person infection risk under
the assumption that there is no enhanced risk of a secondary infection in household clusters
(e.g. two people in household cluster: one is infected, i.e. per-person infection risk = 1; per-
person infection risk for the second person if assumed to be independent of the first person’s
risk is estimated to be 0.1553 (cf. Figure 3B); expected per-person infection risk in household
cluster is therefore (1 + 0.1553) / 2 = 0.578). Estimates and CI limits were determined by fitting
a quasi-Poisson model with the number of infected persons as a response variable and
household cluster size as a factor covariable (excluding 13 household clusters of size 1 each).
Additionally, log(household cluster size) was included as an offset. A significant association
between household cluster size and the per-person infection risk was found (p < 0.001).
Estimates for the upper gray curve (children, CIs depicted by gray vertical lines) are based on
19
an analogously defined model (p = 0.196). Note: 15 household clusters with more than 4
members were omitted from analysis due to small numbers. The average percentage of
infected persons in these household clusters was 17.33% (0% in 9, 16.66% in 1, 20% in 2,
40% in 1, 80% in 1, 83.33% in 1 household cluster).
Fig. 6. Associations of sex and age, comorbidities and super-spreading event with infection
rate and symptoms. A: Estimated rates of infected in the study participants (filled circles, with
95% CIs) for male participants (dark gray) and female participants (light gray) stratified by age
groups. Estimates were obtained by fitting logistic GEE models with the infection status as
response variable and age as covariable (rho = 0.256 [0.104; 0.407] and rho = 0.244 [0.154;
0.334], respectively, in the male and female subgroups). Bars refer to the raw percentages. In
a logistic GEE model with both sex and age as covariables, neither sex (OR = 1.28 [0.95; 1.73]
for females, p = 0.101) nor age (OR = 1.03 [0.94; 1.14] per 10 years, p = 0.539) were found to
be associated with infection status. Numbers above bars indicate the total number of
individuals in the respective group. B: For each of the co-morbidities, the infection rate (%)
was determined by fitting a logistic GEE model with infection status as response variable to
the data of all study participants (light gray: co-morbidity present (+), dark gray: co-morbidity
not present (-)). Point estimates obtained from the GEE models are represented by filled circles
(with 95% CIs). The bars represent the raw percentages of infected in each of the subgroups
(calculated from the participant numbers shown above the bars). No associations between the
infection status and any of the co-morbidities were found (Bonferroni-Holm corrected p-values
from GEE models at top of figure). Associations remained statistically insignificant in GEE
models that included sex and age as additional covariables. Raw proportions are indicated
above bars. C: The association with the life style factor 'celebrating carnival' was analyzed
(questionnaire “have you celebrated carnival?” yes/no). Celebrating carnival was not limited to
attending the main carnival event (Kappensitzung in Gangelt). Estimated infection rates (%;
with 95% CIs) of participants not celebrating carnival (light gray) and participants celebrating
carnival (dark gray). Point estimates (filled circles) and CIs were obtained by fitting a logistic
GEE model with infection status as response variable and carnival (yes/no) as a factor
covariable. The bars represent the raw percentage values. There was a significant positive
association between celebrating carnival and infection status (OR = 2.56 [1.67; 3.93], p <
0.001, rho = 0.351 [0.162; 0.540]). Similar results were obtained when adding sex and age as
covariables to the GEE model (OR = 3.08 [1.92; 4.95], p < 0.001, rho = 0.340 [0.126; 0.554]).
Analyses were based on the 915 participants that had complete data in both the carnival and
the infection variables. D: Estimated mean number of symptoms in infected participants not
celebrating carnival (light gray) and in infected participants celebrating carnival (dark gray).
Point estimates (filled circles) and CIs were obtained by fitting a quasi-Poisson model with the
number of symptoms as response variable and carnival (yes/no) as a factor covariable. The
quasi-Poisson model was used instead of a Poisson GEE model because the number of
households was large relative to the number of analyzed study participants (see
Supplementary figure 4 A). There was a significant positive association between celebrating
carnival and the number of symptoms (estimated relative mean increase = 1.63 [1.15; 2.33], p
= 0.007). Similar results were obtained when adding sex and age as covariables to the model
(estimated relative mean increase = 1.62 [1.12; 2.34], p = 0.011). Analyses were based on the
124 infected participants that had complete data in both the carnival and infection variables.
E: Raw percentages of infected participants celebrating carnival, grouped by their numbers of
symptoms. Numbers above bars indicate the total number of individuals in the respective
group.
20
21
Supplementary figures
Supplementary figure 1: Comparison of the distribution of age groups of the study
participants to those in the community of Gangelt, the state NRW and Germany. Data were
obtained from the Landesdatenbank NRW (reporting date: December 31, 2017) and
statista.com (reporting date: December 31, 2018).
Supplementary figure 2: Comparison of IgG levels to neutralization activity. A: NT titers were
determined by a microneutralization assay using 100 TCID50. Titers indicate the reciprocal
value of the plasma dilutions that protect 50% of the wells at incubation with 100 TCID50.
Samples able to suppress the cytopathic effect (CPE) in at least all three wells of the 1:2
dilution (NT titer ≥ 2.8) are depicted above the dashed line. Samples for which the CPE was
suppressed in one or two wells of the 1:2 dilution are shown directly below the dashed line.
Samples showing a CPE in all wells with either equal or reduced severity compared to the
negative control were depicted at the level of the x-axis. B: Samples below the dashed line in
A were re-evaluated using a plaque reduction neutralization test (PRNT). The neutralizing titers
were calculated as the reciprocal of serum dilutions resulting in neutralization of 50% input
virus (NT50). Samples without neutralizing activity in the NT50 assay were depicted at the level
of the x-axis. Dotted line: upper borderline for ELISA IgG ratio.
Supplementary figure 3: IgA and IgG levels in study participants of different sex and age.
The boxplots depict the log(IgA) (A) and log(IgG) (B) levels of the infected study participants
grouped by sex and age. No significant associations were found (i) between log(IgA), log(IgG),
and sex in Gaussian models with log(IgA) and log(IgG) as response variables and sex as
covariable, and (ii) between log(IgA), log(IgG), sex, age in Gaussian models with log(IgA) and
log(IgG) as response variables and both sex and age as covariables. Note: Gaussian models
were used instead of GEE models because the number of household clusters was large
relative to the number of analyzed study participants, see Supplementary figure 4 A.
Supplementary figure 4: Associations of sex and age and co-morbidities with disease
intensity. A: Estimated mean numbers of symptoms (filled circles, with 95% CIs) in 55 infected
male participants (living in 52 household clusters, dark gray) and 71 infected female
participants (living in 66 household clusters, light gray) stratified by age groups. In view of the
large number of household clusters relative to the number of persons (52/55, 66/71), estimates
were obtained by fitting non-GEE quasi-Poisson models with the number of symptoms as
response variable and age as covariable in the male and female subgroups. Bars refer to the
empirical mean values. In a quasi-Poisson model with both sex and age as covariables, neither
sex (estimated relative mean increase = 1.26 [0.93; 1.71] for females, p = 0.142) nor age
(estimated relative mean increase = 0.97 [0.90; 1.04] per 10 years, p = 0.348) were found to
be associated with the number of symptoms. Results are based on the 126 infected study
participants without missing values in any of the symptom items. Numbers above bars indicate
the total number of individuals in the respective group. Note: There were no children aged less
than 5 years. Numbers above CIs indicate the upper CI limits. B: For each of the co-
morbidities, the mean number of symptoms in the infected participants was determined by
fitting a non-GEE quasi-Poisson model with the number of symptoms as response variable to
the data of the infected study participants (light gray: co-morbidity present (+), dark gray: co-
morbidity not present (-)). Quasi-Poisson models were used instead of GEE models for the
same reason as in A. Results are based on the 126 infected study participants without missing
values in any of the symptom items. Point estimates obtained from the models are represented
22
by filled circles (with 95% CIs). The bars represent the empirical means of the number of
symptoms in each of the subgroups. Raw proportions numbers in each of the subgroups are
shown above the bars. Numbers above error bars indicate the upper end of the error bar. No
significant associations between the presence of any of the co-morbidities and the number of
symptoms were found (Bonferroni-Holm corrected). Analogous results were found when sex
and age were added as covariables to the quasi-Poisson models.
Supplementary figure 5: Associations between IgG levels, co-morbidities, and celebrating
carnival in the infected study participants. The boxplots depict the log(IgG) levels of the
infected study participants in subgroups defined by the presence of co-morbidities and
celebrating carnival. In Gaussian models with log(IgG) as response variable (used instead of
GEE models for the same reason as in Supplementary figure 4 A), no associations between
log(IgG), the co-morbidities, and celebrating carnival were found.
0
25
50
75
100
Anti-SARS-CoV-2 Ig (%)
● ●
● ●
● ●
● ●
● ●
● ●
● ●
● ●
● ●
● ●
● ●
●●
321 0 1 2
321 0 1 2
log(antiCoV2 IgA)
log(antiCoV2 IgG)
r = 0.778
log(anti-SARS-CoV-2 IgG ratio)
IgG
IgA
5
log(anti-SARS-CoV-2 IgA ratio)
r=0.778
20
Infection rate (%)
Infection fatality rate (%)
Number of Symptoms
level
321 0 1 2 3
0123456789101112
log(IgA)
log(IgG)
5
Number of symptoms
p<0.001
4
3
2
1
20
15
10
5
0!
0
1
2
3
4
5
6
7
8
9
10
11
Number of symptoms
SARS-CoV-2-infected (%)
6
8
7
3
1
log(Ig ratio)
Number of symptoms
123456
0.0 0.2 0.4 0.6 0.8 1.0
number of people in household
percent infected persons
123456
0.0 0.1 0.2 0.3 0.4 0.5
number of people in household
percent infected persons
Number of people in household cluster
Infection risk
All household clusters
123456
0.0 0.2 0.4 0.6 0.8 1.0
number of people in household
percent infected persons
Number of people in household cluster
Per-person infection risk
At least one !
child infected
At least one !
person infected
Household clusters with at least one infected
+
-
+
+
-
+
-
+
-
Infection rate (%)
30
20
10
0
p<0.001
No
carnival
Number of symptoms of infected
40
30
20
10
0
0
1
2
3
4
5
6
7
8
9
10
11
No carnival
Carnival
Carnival
Number of symptoms in infectd
0
5
4
3
1
Carnival
60-79
80+
35-59
15-34
5-14
30
20
10
0
Male
Female
20
30
10
0
Infection rate (%)
Infection rate (%)
years
-
0246810 12
1
4
16
64
256
IgG ratio
NT titer
(microneutralization)
0246
1
4
16
64
IgG ratio
NT titer
(PRNT)
Age
log(IgA) level
321 0 1 2 3
(0,4] (4,14] (14,34] (34,59] (59,79] (79,Inf]
male
female
Age
log(IgG) level
321 0 1 2 3
(0,4] (4,14] (14,34] (34,59] (59,79] (79,Inf]
male
female
IgA
IgG
log(IgG ratio)
Age
log(IgA ratio)
Age
60-79
80+
35-59
15-34
5-14
0-4
0
Number of symptoms in infected
+
-
+
-
+
-
+
-
+
-
Age
60-79
80+
35-59
15-34
5-14
0
Male
Female
2
14
14
20
5
2
16
28
21
4
Number of symptoms in infected
log(IgG) level
321 0 1 2 3
60-79
80+
35-59
15-34
5-14
0-4
Carnival
+
-
+
-
+
-
+
-
+
-
+
-
disease
log(IgG ratio)
Table 1. Characteristics and co-morbidities of the 919 study participants in 405 households evaluable for infection status.
Characteristic Value
Size of household clusters no. (%)
1 pers 98 (24.20)
2 pers 184 (45.43)
3 pers 59 (14.57)
4 pers 49 (12.10)
5 pers or more 15 (3.70)
Age
Median (range) 53 yr (1 yr 90 yr)
Distribution no. (%)
<5 yr 6 (0.65)
5-14 yr 55 (5.98)
15-34 yr 176 (19.15)
35-59 yr 344 (37.43)
60-79 yr 266 (28.94)
>79 yr 72 (7.83)
Sex no. (%)
Male 451 (49.08)
Female 467 (50.82)
Diverse 1 (0.11)
IgG no. (%)
High 106 (11.53)
Intermediate 19 (2.07)
Normal 794 (86.40)
PCRnewno. (%)
Positive 33 (3.59)
Negative 886 (96.41)
PCRreportedno. (%)
Yes 117 (12.79)
No 796 (86.99)
Not known 2 (0.22)
Missings: 4
PCRreported positive no (%)
Yes 22 (18.97)
No 93 (80.17)
Not known 1 (0.86)
Missings: 1
Lung disease no (%)
Yes 107 (1.67)
No 798 (87.02)
Not known 12 (1.31)
Missings: 2
Cardiovascular disease no (%)
Yes 128 (13.93)
No 779 (84.77)
Not known 12 (1.31)
Missings: 0
Neurological disease - no (%)
Yes 41 (4.46)
No 875 (95.21)
Not known 3 (0.33)
Missings: 0
Cancer no(%)
Yes 69 (7.52)
No 842 (92.82)
Not known 6 (0.65)
Missings: 2
Diabetes no (%)
Yes 79 (8.62)
No 832 (90.73)
Not known 6 (0.65)
Missings: 2
Carnival no (%)
Yes 417 (45.52)
No 498 (54.37)
Not known 1 (0.11)
Missings: 3
Table 2. Associations between symptoms and infection rate in the 919 study participants. Missing and unknown values in
the symptom variables were listwise deleted.
CoV-2 infectedno.
Symptomno. yes no odds ratio* p-value* odds ratio** p-value**
Loss of taste yes 37 15 17.44 <0.001 17.01 <0.001
no 100 764
Loss of smell yes 31 11 19.54 <0.001 19.06 <0.001
no 106 767
Fever yes 33 47 4.63 <0.001 4.94 <0.001
no 105 732
Head ache yes 49 148 2.28 0.002 2.28 0.003
no 88 631
Cough yes 71 205 2.77 <0.001 2.81 <0.001
no 67 575
Nose congestion yes 59 205 1.90 0.013 1.91 0.010
no 75 576
Sore throat yes 42 144 1.96 0.014 1.92 0.017
no 93 634
Shortness of breath yes 12 34 2.08 0.127 1.98 0.191
no 126 741
Other respiratory symptoms yes 14 55 1.51 0.556 1.49 0.640
no 121 716
Fatigue yes 59 148 3.05 <0.001 2.99 <0.001
no 78 633
Sweats and chills yes 40 76 3.74 <0.001 3.74 <0.001
no 97 700
Muscle and joint ache yes 34 90 2.46 0.004 2.42 0.005
no 103 688
Stomach pain yes 6 45 0.64 0.623 0.62 0.640
no 131 735
Nausea and vomiting yes 9 35 1.46 0.623 1.29 0.640
no 128 744
Chest tightness yes 19 47 2.38 0.014 2.32 0.019
no 118 732
*corrected for clustering **corrected for clustering,
age and sex
Supplementary Table 1. Characteristics of the 88 study participants not evaluable for infection status.
Characteristic Value
Participants (no., %) living in household clusters of size
2 11 (12.50)
3 29 (32.95)
4 28 (31.82)
5 20 (22.73)
Age
Median (range) 9 yr (1 yr 84 yr)
Distribution no. (%)
<5 yr 28 (31.82)
5-14 yr 28 (31.82)
15-34 yr 12 (13.64)
35-59 yr 10 (11.36)
60-79 yr 9 (10.23)
>79 yr 1 (1.14)
Sex no. (%)
Male 36 (41.38)
Female 50 (57.47)
Diverse 1 (1.15)
Missings: 1
PCRreportedno. (%)
Yes 13 (14.78)
No 75 (85.22)
PCRreported positive no (%)
Yes 0 (0.00)
No 13 (100.00)
Carnival no (%)
Yes 46 (52.27)
No 42 (47.73)
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At a regional and local level, the COVID-19 pandemic has not spread out uniformly and some German municipalities have been particularly affected. The seroepidemiological data from these areas helps estimate the proportion of the population that has been infected with SARS-CoV-2 (seroprevalence), as well as the number of undetected infections and asymptomatic cases. In four municipalities which were especially affected, 2,000 participants will be tested for an active SARS-CoV-2 infection (oropharyngeal swab) or a past infection (blood specimen IgG antibody test). Participants will also be asked to fill out a short written questionnaire at study centres and complete a follow-up questionnaire either online or by telephone, including information on issues such as possible exposure, susceptability, symptoms and medical history. The CORONA-MONITORING lokal study will allow to determine the proportion of the population with SARS-CoV-2 antibodies in four particularly affected locations. This study will increase the accuracy of estimates regarding the scope of the epidemic, help determine risk and protective factors for an infection and therefore also identify especially exposed groups and, as such, it will be crucial towards planning of prevention measures.
... Zu den eher niedrigen Berechnungen zählte die im deutschsprachigen Raum viel beachtete Heinsberg-Studie, die in einer repräsentativen Stichprobe ungefähr 900 Personen untersuchte. Diese Studie schätzte die Infektionssterblichkeit auf 0,36 Prozent [190]. ...
... Gleichwohl lassen sich aus Modellierungsstudien Hinweise entnehmen, dass die betroffenen Menschen durchaus noch mehrere Lebensjahre vor sich gehabt hätten, von ungefähr 10 Jahren ist dort die Rede [203]. [190] oder aus der Santa Clara County-Studie [191]. Ein weiterer Zusammenhang besteht in den teils relativ ähnlichen Übertragungen sowie ähnlicher Symptome, die sowohl von Virusgrippen als auch von Covid-19-Erkrankungen hervorgerufen werden [204]. ...
... For instance, the estimated SARS-CoV-2 seropositivity was between 22 and 33% in Guilan Province in Iran in April 2020 [16]. Another study conducted in Gangelt, Germany, in April 2020 showed that 15.5% of the total study participants were seropositive, 22.2% of whom have been asymptomatic [19]. In a population-based study in Geneva, Switzerland, in April-May 2020 targeting randomly selected participants and their household members, 4.8% of the participants were found to have anti-SARS-CoV-2 IgG [20]. ...
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According to the Lebanese Ministry of Public Health, more than 1,053,000 cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been confirmed in Lebanon so far. The actual number of cases is likely to be higher. We conducted a serological study from October 2020 to April 2021 to estimate the prevalence of SARS-CoV-2 neutralizing antibodies and identify associated factors. Serum samples as well as demographic, health, and behavioral data were collected from 2,783 subjects. Sera were tested by microneutralization assay. Neutralizing antibodies were detected in 58.9% of the study population. The positivity rate increased over the study period. It was highest among the group who remained at work during the COVID-19 pandemic and in peri-urban areas with limited adherence to preventive measures. Sex and age were associated with positivity. Reported previous COVID-19, exposure to a COVID-19 patient in the family, and attending gatherings were associated with increased prevalence. Not taking any precautionary measures against COVID-19 was a risk factor, whereas precautionary measures such as working from home and washing hands were protective. The high neutralizing antibody seroprevalence rates detected in this study emphasize the high transmission rate of SARS-CoV-2 infection in the community. Adherence to preventive measures and non-pharmaceutical interventions imposed by the government is recommended.
... Our results confirm severe COVID-19 outbreaks among community dwelling special forces police officers with a high infection rate of 12.3% in Ecuador, suggesting that a massive COVID-19 transmission could be happening among other police officer groups or the military. Moreover, we found 2 individuals among the 20 SARS-CoV-2 positive ones, which means 10% of the infected population with viral loads above 10 8 copies/ul that would represent community dwelling COVID-19 super spreaders (32)(33)(34). ...
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Background At the beginning of the COVID-19 pandemic, health workers and first-responders, such as police officers, were in charge of trying to contain a disease that was unknown at that time. The lack of information and the tremendous need to contain new outbreaks put police officers at higher risk.MethodologyA cross-sectional study was conducted to describe SARS-CoV-2 infection rates among Police Special Forces Officers in Quito, Ecuador. In this study, 163 community-dwelling police officers from elite divisions voluntarily participated in our SARS-CoV-2 detection program using reverse transcription quantitative real-time PCR (RT-qPCR).ResultsA total of 20 out of 163 police officers tested positive for SARS-CoV-2, yielding an infection rate of 12.3%. Within this cohort, 10% (2/20) of SARS-CoV-2 positive individuals were potentially super spreaders with viral loads over 108 copies/ul. About 85% of the SARS-CoV-2 positive individuals were asymptomatic and 15% reported mild symptoms related to COVID-19.Conclusions We found a high SARS-CoV-2 infection rate within the special forces police officers that, beyond a high health risk for themselves, their families, and coworkers. Our results point out the need for permanent SARS-CoV-2 testing among asymptomatic essential workers and first-responders to avoid local outbreaks and to prevent work-place absenteeism among police special units.
... Poseben problem je pregledovanje otrok, oseb s psihičnimi motnjami in oseb z močnim žrelnim refleksom zaradi slabšega sodelovanja pri pregledu, kihanja, kašljanja, siljenja na bruhanje in s tem še večje možnosti nastanka aerosola. Prav tako sta foniater in medicinska sestra ob tem izpostavljena aerosolu pri kirurških posegih v splošni anesteziji, še večja nevarnost pa obstaja pri posegih v lokalni anesteziji v ambulanti (23,24,(28)(29)(30). Ne smemo pozabiti, da v populaciji kar precejšen del okuženih oseb nima simptomov in znakov okužbe (24). ...
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Motnje glasu, govora in požiranja niso redke, najbolj pa prizadenejo profesionalne uporabnike glasu, otroke in odrasle s prirojenimi anomalijami ali nevrološkimi motnjami ter bolnike po zdravljenju raka glave in vratu. Foniater je otorinolaringolog, ki obravnava te motnje. Osnovne preiskave foniatra so endoskopske preiskave grla ter fleksibilna endoskopska analiza požiranja. Kirurški posegi se izvajajo v grlu v splošni ali lokalni anesteziji. V času pandemije covida-19 se je zaradi teh visoko tveganih posegov z nastajanjem aerosola delo foniatra prilagodilo situaciji. Iz preglednih člankov in po stališčih Združenja evropskih foniatrov (angl. Union of the European Phoniatricians, UEP) so povzeta priporočila o načinu in potrebnosti izvajanja diagnostičnih in terapevtskih postopkov. Bistvenega pomena je dosledna uporaba osebne zaščitne opreme, upoštevanje čiščenja in zračenja prostorov ter s tem povezana razpoložljivost zadostnega časa za obravnavo posameznega bolnika. Za vsakega bolnika je potrebna individualna odločitev, ali se preiskava izpelje, ali pa jo je možno vsaj delno nadomestiti z drugo preiskavo, ki ne predstavlja take nevarnosti za prisotne zdravstvene delavce.
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The ongoing SARS-CoV-2 pandemic has triggered several months of global turmoil, and governments across the world are now actively attempting to develop strategies to combat both the virus and its societal impact. Since SARS-CoV-2 is a novel pathogen, basic research is essential, and manifold international efforts are now underway in order to elucidate the molecular mechanisms of both the SARS-CoV-2 infection process and the resulting disease, COVID-19. In this article we discuss how the field of Human Genetics can contribute to this endeavor, and summarize available findings from human genetic COVID-19 research. Finally, we briefly outline how through the use of existing national research infrastructure, German scientists can facilitate progress in this novel and fast-moving field.
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In Spring 2020 the World Health Organization (WHO) has published guidelines for population surveys which can deliver baseline data for health policy decisions in pandemic situations. The realization of the guidelines is by no means trivial. In this essay we describe the challenges of a statistical assessment of the Corona Pandemic. In the first part we treat the extent of hidden cases in the registration of Corona infections, the measurement of infection histories outside the clinical sector, the measurement of risk factors and finally the measurement of temporal and regional changes of the intensity of the pandemic. We discuss several possibilities but also limitations of surveys statistics to cope with the manifold challenges by a proper survey concept and design of a sample. A central issue is the difficult link of medical tests with representative populations surveys where confidentiality rules in the case of individual feedback of test results pose a special challenge. In the second part we report how one of the large German panel surveys, the SOcio-Economic Panel (SOEP), is used for a Covid-19 survey which is in line with theWHO guidelines. This survey was started in September 2020 in the framework of a cooperation of the Robert-Koch-Institut (RKI) with the SOEP as the “RKI-SOEP Stichprobe”. First results on the response rate of the survey which is prolongated in October 2021 are reported. It turned out that 5 percent of the previously cooperative interviewees refuse the further participation in the SOEP because of the invitation to the participate in the medical testing. On the basis of all available survey information (IgG antibody test, PCR test and questionnaire) a first estimate reveals that until November 2020 only two percent of the German adults in private households have been infected with SARS-CoV-2. This number of infections is about twice as high as the officially reported infection cases.
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Effective strategy to mitigate the ongoing pandemic of 2019 novel coronavirus (COVID-19) require a comprehensive understanding of humoral responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the emerging virus causing COVID-19. The dynamic profile of viral replication and shedding along with viral antigen specific antibody responses among COVID-19 patients started to be reported but there is no consensus on their patterns. Here, we conducted a serial investigation on 21 individuals infected with SARS-CoV-2 in two medical centers from Jiangsu Province, including 11 non-severe COVID-19 patients, and 5 severe COVID-19 patients and 5 asymptomatic carriers based on nucleic acid test and clinical symptoms. The longitudinal swab samples and sera were collected from these people for viral RNA testing and antibody responses, respectively. Our data revealed different pattern of seroconversion among these groups. All 11 non-severe COVID-19 patients and 5 severe COVID-19 patients were seroconverted during hospitalization or follow-up period, suggesting that serological testing is a complementary assay to nucleic acid test for those symptomatic COVID-19 patients. Of note, immediate antibody responses were identified among severe cases, compared to non-severe cases. On the other hand, only one were seroconverted for asymptomatic carriers. The SARS-CoV-2 specific antibody responses were well-maintained during the observation period. Such information is of immediate relevance and would assist COVID-19 clinical diagnosis, prognosis and vaccine design.
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On 5 February 2020, in Yokohama, Japan, a cruise ship hosting 3,711 people underwent a 2-week quarantine after a former passenger was found with COVID-19 post-disembarking. As at 20 February, 634 persons on board tested positive for the causative virus. We conducted statistical modelling to derive the delay-adjusted asymptomatic proportion of infections, along with the infections' timeline. The estimated asymptomatic proportion was 17.9% (95% credible interval (CrI): 15.5-20.2%). Most infections occurred before the quarantine start.