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Abstract and Figures

Much has changed since the outbreak of the novel corona virus SARS-CoV-2, respectively COVID-19. The German railway Deutsche Bahn Fernverkehr AG has decided to conduct a study involving its operational staff to help understand the ways of the virus' transmission, to monitor the employees' incidence of infections, and, consequently, to ensure safe operations for customers and employees throughout. We provide an overview of how long-distance train transportation evolved during Germany's lockdown and relaxation phase from March until the end of August and, furthermore, present first results from the ongoing study. The antibody seroprevalence of the tested employees is less than 2%; the seroprevalence of train attendants with frequent customer contact is even lower, which is very promising and contrary to expectations.
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DeutscheBahn 
WorkingPaperwww.deutschebahn.com
September20201©DeutscheBahnAG
UpdateonSARSCoV2InfectionRisksinLong
distanceTrains
ChristianGravert1,PhilippNagl2,FabianBall2,TobiasKörner2
1Deutsche Bahn AG, Berlin; 2DB Fernverkehr AG, Frankfurt am Main;
E-mail: philipp.nagl@deutschebahn.com
September 2020
Abstract
Much has changed since the outbreak of the novel corona virus SARS-CoV-2, respectively
COVID-19. The German railway Deutsche Bahn Fernverkehr AG has decided to conduct a
study involving its operational staff to help understand the ways of the virus’ transmission, to
monitor the employees’ incidence of infections, and, consequently, to ensure safe operations
for customers and employees throughout. We provide an overview of how long-distance train
transportation evolved during Germany’s lockdown and relaxation phase from March until
the end of August and, furthermore, present first results from the ongoing study. The antibody
seroprevalence of the tested employees is less than 2%; the seroprevalence of train attendants
with frequent customer contact is even lower, which is very promising and contrary to
expectations.
Keywords: SARS-CoV-2, COVID-19, antibody prevalence, long-distance trains, infection risks, public transport
1.Introduction
Deutsche Bahn (DB) Fernverkehr AG, Germany’s largest
provider of long-distance train services within Germany and
across borders of neighbouring countries (between 700 and
800 trains per day), was severely hit by the outbreak of SARS-
CoV-2, as all other (public) transportation service providers as
well. The ongoing global pandemic (as of September 2020)
has a huge impact on everyday life, since social distancing is
paramount for the containment of the SARS-CoV-2 virus and
the caused “COrona VIrus Disease 2019” (COVID-19).
We begin this article by thoroughly portraying the situation in
Germany since the beginning of 2020 until the end of August
with regard to long-distance train service. In the following
sections, we first wrap up the current situation at DB
Fernverkehr concerning installed prevention measures for
both customers and employees (section 2), provide an
overview of recent literature regarding the occurrence of
SARS-CoV-2 infections in trains (section 3), and then give
detailed insights and first results of an ongoing
epidemiological study that monitors the prevalence of the
virus in operational staff of DB Fernverkehr. Finally, a brief
outlook concludes the article.
When the WHO declared the novel coronavirus a pandemic
on March 11, there were about 2000 cases discovered in
Germany. A week later the German government announced
strict measures (with the beginning of calendar week (CW)
13), which involved the obligation of a rigorous reduction of
contacts and the closing of restaurants or other subordinate
service providers and shops (basically everything except
stores that sell everyday commodities). Due to the federal
system in Germany there were slight differences between the
actual regulations in each federate state. These restrictions are
commonly referred to as “lockdown”, although they were not
as strict as in other countries (e.g. it was not generally
forbidden to leave home for other than absolutely necessary
occasions).
Figure 1 depicts the mean occupancy of the trains of DB
Fernverkehr in the first halves of the years 2019 and 2020. It
impressively shows a drastic plunge beginning already in CW
9 from a level above the one of 2019 to a value below 10% in
CW 13 and the following weeks. Within this period most
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
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border-crossing trains had to cut off routes due to increasingly
closed borders and the overall capacity was reduced because
of declining demand. Reduction was implemented in two
ways: shorter trains were used and/or the frequency of
connections was decreased. However, long-distance train
service was mainly sustained and thus the number of
connections never dropped below the proportion of about 70%
compared to pre-pandemic times. The massive shock in
demand can be explained by the abrupt stop of touristic
journeys (hotels and similar businesses had to close) and the
cancellation of any conferences, congresses, and meetings,
which made business journeys superfluous.
Figure1MeanoccupancyoflongdistancetrainsofDBFernverkehr
inthefirsthalvesoftheyears2019and2020
Mid-April (CW 16) the public health authorities and regional
governments started to recommend the covering of mouth and
nose (e.g. by self-made “community masks” or medical
masks) until they became mandatory in Germany inside shops
and in local public transport at the end of April. Exact dates
differed between federate states. Deutsche Bahn argued in
favour of wearing masks early on and made it compulsory for
on-board staff with passenger contact when the regulation for
shops and local public transport was introduced.
About the same time, the relaxation phase of restrictions
began, as the infection rate of the virus had dropped below a
critical level. This allowed a reopening of shops and many
services could be provided again (e.g. hairdressing), in
condition that strict hygiene measures were followed. The
mean occupancy of the long-distance trains reflects this
revival of public life by a constant increase since CW 17. With
the beginning of CW 20, the served capacity started to recover
as well and climbed up to about 95% of the pre-pandemic offer
until CW 28. On-board catering and business or frequent
traveller lounges at train stations also resumed their service.
Demand during summer was mainly driven by touristic travel
on a national level, which was quickened by partially
diverging pandemic developments, especially in “classic”
German holiday countries (e.g. France, Spain, Italy, Turkey).
As a result, long-distance train connections to touristic
destinations were expanded. Regulations to cover mouth and
nose on board of long-distance trains were mostly harmonised
after initial heterogeneities and today (as of September 2020)
appropriate masks are mandatory in most European countries.
In July 2020 the situation in Germany remained mostly stable
with relatively low infection rates. At the beginning of August
infection rates increased and mandatory tests for returning
travellers from regions at risk were set up. This resulted in a
large increase of performed SARS-CoV-2 tests, while the ratio
of positive tests per fixed number of performed tests only
slightly increased. Larger outbreaks predominantly happened
at private gatherings or at home, as we will point out in section
3. Therefore, keeping social distance and additionally
covering mouth and nose, when keeping distance is not
possible, are without alternative, although discussed
critically1,2.
Throughout the past and upcoming pandemic phase, health
and safety for passengers and employees were – and remain to
be – paramount. Besides the generally relevant measures and
recommendations, this involved, e.g., an increase in surface
cleaning and disinfection cycles or the adjustment of shift
schedules to minimise contacts3. Furthermore, it led to the
decision to conduct a large epidemiological study among
operational staff, of which first results are presented in section
4. The additional activities to create and maintain a safe
environment for passengers and staff are based on the latest
commonly accepted scientific insights and are subject to being
constantly updated and improved.
Alongside the factual dangers of COVID-19 on health,
worldwide restrictions and measures have caused an
unprecedented economic crisis, which especially hit the
tourism industry hard4. Therefore, the incitement for DB
Fernverkehr to contribute to the research regarding the spread
of the virus is twofold and involves an economic component
as well. Infection risks in public transportation are an ongoing
topic of discussion, not just since the emergence of the SARS-
CoV-2 pandemic5. A – possibly disproportionate – fear of
using public transport for this reason may lead to an increased
use of motorised private transport, which hinders the
necessary shift to a more climate friendly way of
transportation6.
The research described in this publication aims to contribute
to the discussion of the risk of a SARS-CoV-2 infection by
delivering objective results, but it also helps to monitor the
effectivity of the installed occupational health measures for
staff in trains of DB Fernverkehr and to reduce the possible
discomfort of passengers.
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Meanoccupancy
Calendarweek
2019 2020
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
3
2.CurrentsituationatDBFernverkehr
We briefly want to recap the installed measures at DB
Fernverkehr that help to reduce the risk of infection on trains
and at the workplace during the COVID-19 pandemic to a
minimum7.
Administrative staff is strongly recommended to work from
home whenever this is possible. However, coming to the
office buildings is possible as well, as the number and
arrangement of workplaces was adapted to allow keeping the
necessary distance. A standardised test procedure was
established to quickly react to discovered acute SARS-CoV-2
infections within the staff. DB can perform PCR diagnostics
on a large scale in its own laboratory, the occupational health
service performs the necessary swabs from nose and throat at
the workplace. On trains, suspect cases are consequently
separated from other passengers, the suspicion is immediately
cleared out in collaboration with the authorities at the next
train stop.
Since air conditioning is crucial (especially the provision with
fresh air) to remove virus laden emissions8, the maintenance
of HVAC systems of trains was intensified. Regular
announcements of the train attendants and signage remind
passengers to correctly wear their mouth and nose covering.
Individuals who deny covering their face appropriately are
kindly adverted. As an absolute last resort, passengers can be
excluded from using the train, as they are a possible hazard for
safe operations. There are also fines for refusing mouth and
nose coverings, to be imposed by police.
The dynamic pricing system for pre-booked tickets was
adopted to allow occupancy rate control with a limited
capacity per train and, additionally, the capacity utilisation is
presented to customers during the booking process. An
expected occupancy of more than 50% is specifically
highlighted and owners of commuter tickets or tickets not tied
to a certain train are advised (not enforced) to use another train
if possible.
Before ending this section, let us present updated numbers9 of
reported COVID-19 positive cases at DB Fernverkehr in Table
1. Board service staff/train attendants have a slightly higher
prevalence compared to all other employees, but the
difference is not statistically significant. The difference to the
general population (0.26% vs. 0.36%) is, however,
significantly lower (p = 0.021). This means employees of DB
Fernverkehr are significantly less often affected by COVID-
19, given the assumption that test coverage is identical, and all
discovered cases were reported.
ahttps://docs.google.com/spreadsheets/d/16K1OQkLD4BjgB
dO8ePj6ytfRpPMlJ6aXFg3PrIQBbQprovidedbytheaerosol
researchgroupofCUBoulder
Number of
persons
Age 20-66
COVID-19
positive cases
Age 20-66
Ratio
DB FV 19,400 50 0.26%
DB FV Board Service 8,000 25 0.31%
German Population 51,747,759** 184,592* 0.36%
Table1EvaluationofthereportingsystemDBAGforconfirmed
COVID19cases(status10.09.2020).DBFV=DBFernverkehr.
*Queriedathttps://survstat.rki.de/Content/Query/Create.aspx
(status10.09.2020)**Queriedathttps://wwwgenesis.destatis.de/
(statistic124110005,referencedate31.12.2019)
We list the infection environment of DB Fernverkehr
employees with a special focus on train attendants in Table 2.
While employers in Germany usually do not get any
information about sickness of employees except their absence,
in case of infectious diseases and especially with COVID19
often the way of infection becomes known through the
inquiries of the health authorities. Most of the infections with
given information happened in a private environment and not
at work. The slightly higher number of infections in the
professional environment for employees who are no train
attendants can be explained by a minor outbreak in a
maintenance facility, which we describe in detail in another
publication10.
Infection
Environment No. of cases
train attendant
service
Ratio No. of cases DB
FV w/o train
attendant service
Ratio
Unknown 13 52% 7 28%
Private
Environment 11 44% 12 48%
Professional
Environment 1 4% 6 24%
Sum 25 100% 25 100%
Table2EvaluationoftheinfectionenvironmentofemployeesofDB
FernverkehrAG,status10.09.2020
3.Literature
There is only scarce literature on the occurrence of infections
in trains and we already gave a comprehensive overview in the
publication on first implications of COVID-19 at Deutsche
Bahn11. Basically, two ways to approach the topic can be
found in the literature: either bottom-up, involving theoretical
model-based considerations to approximate the risk of
infection/fatality, or top-down, by observing actual infections
and trying to identify their source of transmission.
Most modelling approaches involve the Wells-Riley equation,
on which we elaborated in the above article. Here, we just
want to point out an interactive spreadsheet that can be used
to calculate the risk of an aerosol transmission of SARS-CoV-
2 in different environmental settingsa. The creators report an
http://cires1.colorado.edu/jimenez/(bothaccessed1Sep
2020)
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
4
infection probability in a subway setting, which involves
results from current research for the variable input parameters,
that is very small (far less than 1%).
Hunt assesses the individual risk of infection for an average
passenger train journey by considering the probability of a
close contact to an infected person, the number of person
contacts during the whole journey (including the time in
station buildings), and the impact of mitigating factors (e.g.
wearing masks)12. The provided results are based on input
values from the UK and the infection risk is reported as below
0.01% per average journey (exact values depend on the type
of carriage and if masks are worn).
Both described concepts have a valid theoretical background
but depend on several input parameters. The parameter values
are approximated based on current literature, however as
always, parametrized models capture the reality only to a
certain extent. Therefore, it is vital to compare the theoretical
results to observed transmission routes, which can often be
uniquely assigned to an infection cluster. Transmission in
public transport is only a minor factor – if at all – in the
following publications. However, note that possible infection
transmissions in public transport facilities is very hard to trace.
Particularly in Germany it is not mandatory to have a reserved
seat in long-distance trains of DB Fernverkehr. Data-
protection legislation does not allow to analyse existing
reservation systems or credit card data for other purposes than
their original purpose.
Germany’s Robert Koch-Institute, “the government’s central
scientific institution in the field of biomedicine”b, analysed
55,141 out of 202,225 officially reported COVID-19 cases
(until 11 August 2020) with respect to their infection
environment13. Each case was part of an outbreak, which they
define as at least two related and laboratory-confirmed cases.
Nearly 8,000 individual outbreaks were identified and
allocated to their appropriate cluster-category. Most
categories are further divided into subcategories; e.g. “bus”,
“airplane”, “train” are all individual subcategories of the
transportation cluster. Only 18 outbreaks with 90 cases are
part of the transportation cluster, none of them is assigned to
the train subcategory. The largest clusters are residences, mass
accommodations for seasonal workers, and medical treatment
institutions with more than half of the reported cases.
The situation is similar in France, where the “Santé publique
France” (the French public health agency) includes a cluster
assignment of outbreaks in their weekly report14. The
monitoring system of clusters contains cases since May 2020,
if at least three reported cases within a period of seven days
belong together. From the 1,097 reported outbreaks (until 24
August 2020), only 15 have a transportation context
bhttps://www.rki.de/EN/Content/Institute/institute_node.h
tml(accessed1Sep2020)
(subcategories do not exist). As in Germany, most of the
outbreaks are family-related, work-related, or related to
healthcare institutions.
Austria also continuously updates the assignment of new cases
to clustersc. Until 17 August 2020, 11,236 cases were assigned
to 1,435 outbreaks. There is no separate cluster for outbreaks
in public transport, but the authors state that no accumulation
of outbreaks in a public transport context could be found.
Again, most outbreaks are household related.
An investigation of case clusters in the first quarter 2020 in
Japan also identifies (health-)care facilities to be most
affected15. Only one of 61 examined clusters was transport-
related and describes an incident in an airplane.
A systematic review of 65 studies on SARS-CoV-2 infections
identified six transportation related cluster transmissions (out
of 108)16. One of these studies is related to a train journey and
we will discuss it in detail below. The others are linked to a
taxi ride (1), to air travel (3), and to a cruise ship (1).
A nearly five-hour flight from Tel Aviv (Israel) to Frankfurt
(Germany) with seven index cases on board (all from the same
travel group) was investigated by Hoehl et al.17. The index
cases were tested on arrival and four of them were already
symptomatic, two others developed symptoms afterwards, and
one remained asymptomatic. Due to the flight date (mid-
March 2020), wearing masks was neither mandatory nor is
unsolicited wearing of masks reported. One passenger who sat
nearby the index cases received a positive PCR result four
days after the flight but did not develop any symptoms. An
antibody test some weeks after the flight had a positive result
as well. Another passenger, who reported to have had
symptoms after the flight and self-quarantined for 14 days,
was seropositive nine weeks after the flight. From these
results, the authors conclude that for these two cases the
transmission is likely to have happened on board of the
airplane.
A very interesting investigation is given by Hu et al., who try
to find a correlation between travellers of high-speed trains in
China with a reported SARS-CoV-2 infection and other, co-
travelling passengers with close contact18. They identified
2,334 index patients during the time from mid-December 2019
until the beginning of March 2020 and over 72,000 close
contacts. The selected time span includes the Lunar New Year
festivities, which go along with massive travel movements.
The data of infected persons who have used public transport
(including the necessary details) are publicly available in
China and were used by the authors. A person is counted as an
index case if the train usage was within the period of 14 days
before symptom onset and a close contact is defined as a
person who sat in the area of ±3 rows of the index case for a
chttps://www.ages.at/service/service
presse/pressemeldungen/epidemiologischeabklaerungam
beispielcovid19/(accessed1Sep2020)
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
5
certain time. From all the close contacts, 234 passengers have
been subsequently reported as COVID-19 (secondary) case.
The authors computed the co-travel-time and a spatial index
to quantify the duration of possible exposition to an infected
and the spatial distance. As every close contact with a
subsequent COVID-19 infection is assumed to became
infected on board the train, Hu et al. report an attack rate of
0.32%. They also use co-travel-time and spatial distance as
independent variables of regression models. Especially for the
co-travel-time they present a positive correlation with the
attack rate and a larger spatial distance is correlated
negatively.
However, the authors also admit some deficiencies of their
analysis. Firstly, the relatively long temporal overlap of train
journey, symptom onset, and secondary case report can lead
to an overestimation of actual transmission events. Secondly,
familial or other close relations between the passengers who
sat nearby are included into the analysis. These must be
assumed to be strong confounders for virus transmission, as
they imply other occasions around or beyond the train journey
where a transmission could have occurred. This issue is
underlined by the fact that the authors divide the close contacts
into two groups, namely into those who used the same travel
relation as the index case (i.e. same departure and destination)
and into those who had no identical travel relation. The first
group is likely to contain many co-traveling passengers who
have a personal relation to the index case, which is reflected
in a much higher attack rate of 0.92% compared to 0.06% for
the second group. Apart from these reported limitations, the
used regression models (shown in Figures 3 and 4 of the
article) are questionable, as they (i) are based on only few data
points (six or eight) and (ii) not every data point is included in
every model (likely to increase significance and/or adjusted
R2).
Despite these limitations, a direct comparison to the situation
in Germany (or possibly other European countries) is
additionally hindered by a different passenger density inside
the train carriages (five seats per row in China in the second
class with slightly wider carriages, only four in Germany).
Also, it is not reported if face masks were worn in China at the
investigated point of time.
In a summary article about experiences from China
concerning preventive measures in public transportation, the
authors mention in their conclusion that “there are no […]
cluster transmissions of COVID-19 caused by public transport
facilities in China”19. The quoted preventive measures are, in
short, personal hygiene, keeping social distance whenever
possible, enhancing ventilation, and wearing masks.
4.Intermediateresultsoftheongoingstudywith
employeesatDBFernverkehr
This section summarises the key content of the status report of
the first test phase of the study20 For further information not
addressed here we refer to the status report (which is in
German).
Studygoals
The goals of the study are multifaceted: Keeping up high-
speed and long-distance train services in Germany during the
entire pandemic needs the full commitment of the operational
staff as key workers. Thus, as for every responsible employer,
it is of crucial interest for DB Fernverkehr to maintain the
safety of their employees at all the time. In addition, the
responsibility is the same towards the customers, who expect
a safe journey as well.
For an objective and evidence-based approach, the main goal
is to measure the prevalence and incidence of acute SARS-
CoV-2 infections and of the seropositivity as a marker of past
infections of the operational staff, namely train attendants,
train drivers, and maintenance workers. These numbers allow
to derive the virus exposition rate as the quotient of positively
tested acute/seropositive persons and all tested persons.
Due to the multi-round approach of the study, it is also
possible to exploratively deduct the estimated number of
unreported cases, the time span of traceability of antibodies
after a verified infection, the examination of (epidemiological)
factors that possibly promote or inhibit the risk of an infection,
and to monitor the effectivity of non-pharmaceutical
intervention measures in trains.
A representative random sample per group allows – to a
certain extent – the extrapolation of the results to all
employees of DB Fernverkehr who belong to one of these
three groups. Furthermore, especially the exposition rate of the
group of train attendants, who have significantly more
(customer) contacts than the other groups, could act as an
indicator for the occurrence of infections on board of long-
distance trains in Germany. If the numbers are low, this would
indicate a low risk for passengers to become infected by train
attendants and possibly also a low risk to become infected by
other passengers.
Lastly, the results also contribute to the rather scarce
landscape of scientific publications of infection risks in trains.
In order to have a scientifically and especially statistically
flawless study, we carry it out in close cooperation with the
Charité Research Organisation (CRO). CRO is specialized in
the conduct of early clinical studies in both patients and
healthy volunteers. The study was reviewed by the ethics
committee of the Berlin Medical Association. Medical checks
and sampling were performed by PIMA Health Group.
Studydesign
The study is designed as a direct measurement of acute and
past infections of SARS-CoV-2 within three groups of
operational staff of DB Fernverkehr AG (train attendants, train
drivers, maintenance workers) over a period of roughly eight
months. The temporal scope incorporates a total of three
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
6
medical rounds where, firstly, participants complete a
questionnaire of demographical, epidemiological, and medical
questions. Secondly, they are tested for an acute infection via
a nose/throat swab following a PCR test and a small amount
of venous blood is drawn for testing of IgG antibodies against
SARS-CoV-2. The interval between the rounds is three to four
months; the first one took place at the end of June/beginning
of July 2020. The second round will follow end of October,
the third round is planned for February.
The reason why these three groups were chosen are, on the
one hand, their criticality for the operation of train services
while, on the other hand, they are expected to have different
individual infection risks concerning their working
environment:
Train attendants (TA) have frequent contacts to both
colleagues and customers on train and on platforms
during stops.
Train drivers (TD) only have few direct contacts to
other persons during their professional work.
Maintenance workers (MW) are responsible for the
attendance of the trains in facilities where close
physical distance to colleagues cannot always be
avoided. Additionally, their work involves physical
effort, but they have also only few contacts to
unknown persons during their professional work.
To get results as representative as possible, a random sample
for each of the three groups at four different locations across
Germany was drawn. The locations are Berlin,
Frankfurt/Main, Hamburg, and Munich, and in every city
exists a large site of operation and a maintenance facility (in
Hamburg even two). In total over all four locations, about a
third (TA), a quarter (TD), and half (MW) of all employees of
the respective group are covered by this selection.
Train
attendants Train drivers Maintenance
workers
No of persons ~2,600 ~750 ~2,000
Gender
distribution (m
/
f) 52.3% / 47.7% 98.8% / 1.2% 97.0% / 3.0%
Median / Mean age 43 / 43.0 51 / 47.6 44 / 42.9
Table3Keynumbersofthethreeemployeegroups(attheselected
locations)thatareinscopeofthestudy(asofMay2020)
Driving trains and performing maintenance work are
relatively technical professions, which could explain the
skewed gender distribution (Table 3). The median (mean) age
is 45 (43.6) years.
Overall, close to 1,100 participants, about 600 TA and each
250 TD and MW, respectively, were included in the study.
The sample was drawn per group and location at random to
capture the specific demographic structure. The group sizes
per location are nearly distributed evenly except for MW,
where there is an imbalance between Frankfurt and Hamburg
due to the different sizes of the maintenance facilities. Thus,
the MW per-location sample size was fitted accordingly.
Administrationofthefirsttestround
Employees were invited by letter mail and were asked to
voluntarily participate in the study. As a first step to
participate, prospects needed to register online and make an
appointment in one of the proposed time slots at their location.
As the sample was drawn per group and location, free time
slots could be filled up in advance to effectively use the test
capacities and to allow as much interested employees as
possible to take part in the study.
The tests took place between 29 June and 3 July 2020.
Planning and on-site execution was especially focused on the
prevention of unnecessary queue times to maintain health and
safety for all those involved. The second step to participate
was a medical consultation and the informed consent of the
invited person. After that, the questionnaire had to be filled
out and the two medical tests were performed by trained
personnel. The gathered personal data were promptly
pseudonymised by the medical service provider to prevent the
identification of individuals. Especially DB Fernverkehr as
the employer has no possibility to acquire any individual
health information of their employees related to this study. In
case of acute SARS-CoV-2 infections, for which exists an
obligation to immediately notify the authorities by the German
law on infection protection (IfSG, Infektionsschutzgesetz), the
notification would be performed by the chief medical officer
of Deutsche Bahn, who must obey medical confidentiality.
Resultsafterthefirsttestround
A total of 1,073 invited employees became active participants
of the study. Only three of them needed to be excluded from
the data analysis due to missing questionnaires. Of these 1,070
persons, 625 are train attendants, 242 are train drivers, and 203
are maintenance workers. The demographic distribution per
group is shown in Table 4. The mean age over all groups is
44.6 years.
Train
attendants Train drivers Maintenance
workers
No of persons 625 242 203
Gender
distribution (m
/
f) 44.6% / 55.4% 98.8% / 1.2% 97.5% / 2.5%
Mean age 43.3 47.1 45.8
Table4Keynumbersofthethreeparticipantgroupsofthefirst
roundofthestudy
A comparison of Table 3 and Table 4 shows that there are only
marginal differences between the population and the actual
participants: participating female TA seem to be slightly
overrepresented and the mean age of participating MW is a bit
higher than expected. Besides statistical variance, this could
be a result of self-selection bias in virtue of the voluntariness
of participation. However, as the differences are neglectable,
the sample of participants is assumed to be representative for
the population.
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
7
1,068 PCR tests yielded a valid result and only one had a
positive outcome, all others were negative. As a consequence
in the one positive case, all direct work place contacts of this
asymptomatically infected maintenance worker were tested as
well. These separate tests were all negative and are not
included in the study.
A blood sample could be drawn from 1,067 persons. From
these, one had no laboratory result and four had a borderline
result of IgG antibodies. Two participants with borderline
result were tested again and had a negative result. From the
remaining 1,064 results 1,044 were negative and 20 were
positive for SARS-CoV-2 IgG. One person, who was tested
positive, was not included in the data analysis due to a missing
questionnaire.
The questionnaire contained questions concerning pre-
existing conditions (e.g. of the cardiovascular system or
diabetes), smoking behaviour, influenza vaccination status,
contact behaviour on job and during spare time, behaviour of
wearing a mouth and nose covering at work, and acute or past
(since March 2020) symptoms that are assumed or attested to
be linked to COVID-19 (e.g. coughing, sore throat, limited
olfactory and/or gustatory senses21,22). We only elaborate on
those factors that stick out in connection with the acute or
antibody test results and omit the details – especially on
medical preconditions – where possible, to not disclose
medical or personal circumstances of whole groups of
employees of DB Fernverkehr. This includes details linked to
the attributes of smoking behaviour and pre-existing
conditions, as no statistically significant differences could be
found.
Overall, TA had the largest proportion of participants who
reported cold symptoms, particularly a sore throat, sniffing,
coughing, or limb pain sometime in the last four months. The
members of all three groups had a mean number of workdays
of over 14 days in the past four weeks before their visit in the
test centre. Only few (23 in total) employees did not work
within the mentioned time span (e.g. due to recreational
holiday), so almost all participants had been exposed to
contacts at work.
Over 90% of the TA are wearing a mouth and nose covering
during their working hours. The proportion for TD and MW is
much smaller, however, this had to be expected as a result of
the different regulations and working conditions of the three
groups. It is mandatory for TA to wear a covering on train (if
they have passenger contact) and on station platforms, TD and
MW only must wear coverings if social distance cannot be
kept (or on station platforms as well). Expectation is also met
for the proportion of the number of colleagues a participant
has longer lasting contacts to per week (more than 15min): TA
have the most, TD the least contacts; the difference between
TA and TD is statistically significant.
20 out of 1,064 performed valid SARS-CoV-2 antibody tests
were positive. This corresponds to a seroprevalence of 1.9%.
The one MW who had a positive PCR result was tested
positive for antibodies as well and is thus included.
Interestingly, the distribution across the three groups (Table 5)
shows the least seroprevalence for TA, which is certainly
contrary to expectations. However, the differences between
the groups are not statistically significant (p = 0.22).
Sero-
positivity Train
attendants Train
drivers Maintenance
workers
Negative 615 (98.7%) 234 (97.5%) 195 (97.0%) 1,044 (98.1%)
Positive 8 (1.3%) 6 (2.5%) 6 (3.0%) 20 (1.9%)
Table5Overviewoftheseroprevalenceacrosstheemployee
groups
We now provide details on conditions or circumstances that
may have an impact on the seroprevalence. Please note, that
due to the overall small absolute numbers of seropositive
persons, these results are often statistically insignificant or
should at least be taken with caution in terms of generalisation.
Concerning pre-existing conditions, only diabetes seemed to
have a reinforcing effect on SARS-CoV-2 antibody
seropositivity: 6.3% of the diabetics were tested positive and
only 1.7% of the other participants had antibodies. The
difference is significant (p = 0.065); the proportion of
diabetics among the participants is about 3%, which was
expectable23.
Another statistically significant impact on the seroprevalence
(p = 0.027) has the number of persons per household.
Participants who live on their own (about 23%) were all
seronegative, whereas the seroprevalence for participants who
live together with one other person (about 41%) is the highest
with 3.27%. For those living together with more than one
person (about 36%), the seroprevalence is 1.59%. Participants
in households of five or more persons have a seroprevalence
of zero, however, only very few (n = 22) live in such large
households. Living together with children has no significant
effect on the seroprevalence. The number of vocational or
private contacts could not be identified as significant drivers
of positive test results as well.
A very interesting and significant (p = 0.061) coincidence
could be identified for participants and their indication of
being vaccinated against influenza. About 22% of the tested
employees indicated a vaccination against influenza last
season. Only one positive antibody test fell into the group of
the vaccinated persons (prevalence 0.4%), all other 19 positive
test results relate to those who indicated to not have had
received an influenza vaccination for the past season
2019/2020 (prevalence 2.3%). Note that the influenza
vaccination rate in Germany is not recommended for persons
under 60 without individual and professional risks, so a
vaccination rate of 22% percent in the workforce is probably
above average for same age people in Germany24.
Seropositivity is linked to a significantly more often
occurrence of symptoms like fever (p = 0.006), coughing (p =
0.09), loss of olfactory and gustatory senses (p 0.001), limb
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
8
pain (p = 0.039), and coryza (p = 0.011). Especially the
limitation of olfactory and gustatory senses was indicated by
six of 19 seropositive persons (for one person theses
indications were missing). 14 of the 19 (73.7%) employees
reported to have had at least one of the listed symptoms above
(including diarrhoea, sore throat, and chest pain, which
yielded insignificant differences in their occurrence). The
remaining five participants, who did not report any symptoms,
had either an asymptomatic infection or their test result might
have been false-positive (see next section).
A last interesting finding, which has to be monitored over the
next two rounds, is that only four of six participants, who
indicated to have had a previous positive PCR test result for
SARS-CoV-2, were seropositive in the first round of our
study.
Discussionandlimitationsoftheresults
Before we discuss the described results from above, let us
shortly mention a technical limitation regarding the used
antibody test-kit (manufactured by EUROIMMUN AG,
Lübeck/Germany). The specificity of the used ELISA-(IgG)
test for SARS-CoV-2 antibodies is 99.6%. Thus about 4
(0.004×1,064) positive test results are expected to be false-
positive. Due to this fact and not knowing for sure, if positive
antibodies ensure immunity, it is not advisable for persons,
who have been informed of their positive antibody result, to
stop following the general health and safety rules of the
SARS-CoV-2 pandemic (keeping distance, reducing social
contacts, covering of mouth and nose on trains and at other
workplaces if keeping distance is not possible).
We now want to look at the results in relation to other
published results. Most of these publications report
seroprevalences between 1% and 3% with an exception of
those linked to populations that had a much higher possibility
of infections due to the overall circumstances (e.g. nursing
staff and doctors in hospitals, people in highly affected areas).
Also, the passed time since the beginning of the pandemic in
contrast to when the tests were conducted plays an important
role.
Three studies/systematic tests took place in hospitals in
Germany:
Charité clinic in Berlin, mid-April 2020, staff with
and without patient contact (~7,500p): around 2%
seroprevalence25
Fulda hospital, mid-April 2020, staff with patient
contact (~1,500p): around 1% seroprevalence 26
dhttps://www.rki.de/DE/Content/Gesundheitsmonitoring/St
udien/cmlstudie/Factsheet_Kupferzell.html(accessed31
Aug2020)
ehttps://www.rki.de/DE/Content/Gesundheitsmonitoring/St
udien/cmlstudie/Factsheet_Bad_Feilnbach.html(accessed
31Aug2020)
Hospital in Hessisch Oldendorf, end of April 2020,
77% of the staff (~400p): 2.7% seroprevalence 27
These three investigations have in common that they took
place relatively early in the pandemic phase (about a month
after strict measures in Germany began) and that the probands,
on the one hand, probably have had a higher exposition rate
due to contact with patients, but, on the other hand, also very
high hygiene standards were omnipresent due to the healthcare
environment.
More important – in terms of comparability to the study at DB
Fernverkehr – are studies subject to the general population.
Nonetheless, some of these studies are carried out in highly
affected regions, which results in a higher seroprevalence than
seen by us:
City of Gangelt in the county of Heinsberg,
beginning of April 2020, representative sample of the
population (~600p): 15.5% seroprevalence28
City of Kupferzell, end of May to beginning of June
2020, representative sample of the population
(~2,200p): 7.7% seroprevalenced
City of Bad Feilnach, end of June to beginning of
July 2020, representative sample of the population
(~2,150p): 6.0% seroprevalencee
The latter two are part of the “CORONA-MONITORING
lokal” study29 that has the aim to investigate locations in
Germany with a proportionally higher reported incidence of
SARS-CoV-2.
A cross-sectional epidemiologic study is carried out in
Munich, where about 3,000 randomly selected households
shall be monitored over a period of 12 months30. During that
time, blood sampling is planned every six weeks. As the study
is still in progress, no official numbers are published, yet.
However, the authors assume “an infection rate within the
lower one-digit percent range” (translated from German) f.
A comparably low seroprevalence of 1.3% was reported as
intermediate result for an ongoing study of blood donors in
Germany31. The result is based on nearly 11,700 tested
samples, which were drawn between the beginning of April
and the end of June 2020. The representativity of the results is
debatable, as not everyone is eligible to become a blood donor
(besides the voluntariness). Especially risk groups (e.g.
diabetics), who are assumed to be at risk concerning COVID-
19 as well, are excluded from donating blood, which could
explain the lower seroprevalence in this study.
In particular a study involving civil service personnel from
Bremen can be used as a major reference to our results32. The
fhttp://www.klinikum.unimuenchen.de/Abteilungfuer
InfektionsundTropenmedizin/de/COVID
19/KoCo19/Aktuelles/index.html(accessed31Aug2020)
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
9
seroprevalence of about 280 “police officers, firefighters,
rescue personnel, health workers and administrative staff who
worked despite the lockdown and may have been in contact
with COVID-19 infected individuals”33 is reported as 2.1%.
The tests took place during April and the first half of May
2020. The participants of this and our study have in common
that they worked during the lockdown phase similar to normal
times. The differences are possibly a different experience in
special hygiene measures and a different exposure to infected
persons.
An overview article of SARS-CoV-2 seroprevalence studies
with a focus on Germany, but also including international
publications, is given by Poethko-Müller et al.34. However,
they present only few results, as many cited studies were still
in progress at the date of submission (early July 2020). Results
from a nationwide Spanish study are available as well35, but
they cannot be taken as a reference for Germany due to the
overall much higher incidence. Spain is one of the mostly
affected countries in Europe with nearly 30,000 deaths by the
beginning of July 2020. The authors report a seroprevalence
of 5.0%, based on a sample size of over 60,000 persons tested
between the end of April and beginning of May 2020.
Two studies, both in preprint stage, support the discovered
possible dependence of influenza vaccination and
seroprevalence. Fink et al.36 investigate over 90,000 COVID-
19 patients in Brazil and how their influenza vaccination status
influences the disease process. They report lower probabilities
for the need of intensive care, invasive ventilation, and fatality
if patients were vaccinated. Furthermore, they find possible
evidence for a reduced probability of transmission, as the
vaccination rate of patients with age 65 or older is about 42-
44%, the influenza vaccination rate in the general population
of the same age group in 2015/16 was 73%. This may indicate
an overrepresentation of non-vaccinated COVID-19 patients.
However, because of the temporal gap and possible
unobserved confounders, these results must be taken
cautiously.
Patients from the USA are considered by Zanettini et al.37 in
terms of fatality and influenza vaccination and under
consideration of diverse confounding variables. They, as well,
discover a reduced fatality rate that negatively correlates with
the vaccination rate. As promising as these results are, further
research needs to be done.
Our study has several strengths, but some limitations as well.
It is the first study that quantifies infection risks with SARS-
CoV-2 in long-distance trains during the worldwide COVID-
19 pandemic. This adds to the now and then upcoming
discussion (especially in the media), whether there is a higher
infection risk in trains or not. The (intermediate) results of the
ongoing study presented here are of course not generalisable
for every pathogen but must be seen in the current context:
transmission occurs mainly via droplets, aerosols38, or contact
infection and covering of mouth and nose is mandatory during
the whole journey.
It has been shown that the seroprevalence of the tested
operational staff is not higher compared to other, yet scarcely
available study results. Even the inter-group comparison,
despite statistical insignificance, indicates no higher infection
rate of on-board personnel. Furthermore, the used sampling
technique assures representativity for all employees in the
three different groups at the four locations, and we have no
reason to doubt the results are also valid for staff beyond the
chosen locations. Care must be taken in the comparison of our
results to other published studies, as most of them (us
included) have a focus on a special group of people that is not
necessarily representative for the general population. A
broadly conceived and, therefore, representative study for the
German population, which could act as a baseline for the
occurrence of infection to compare to, is missing at the point
of writing this article.
Another limitation is the abrupt plunge of demand of train
journeys during the phase of strictest measures in Germany,
which is recovering, but still below the pre-pandemic level
(Figure 1). This results in a smaller average utilisation of long-
distance trains and thus also in less customer contacts on
trains. However, due to the longitudinal setting of the study,
the effect of an increase in the passenger numbers will be
captured by the study in the second and third test round.
Transferring our results to, e.g., regional trains, metro
systems, or busses should also be done carefully, as the
environments generally differ (shorter journeys, often higher
degree of utilisation/larger crowd level, possibly different air
conditioning).
The last limitation we must point out is related to the infection
risks of passengers. There is of course a difference in the
behaviour of passengers and train attendants on board of
trains, which may restrict the risk approximation for the
former by the results for the latter. Passengers normally stay
seated for most of the time during their journey, except for
their way to the toilet, to the on-board restaurant, or to shortly
walk around on longer journeys. Train attendants pass through
the different cars for ticket inspection, act as contact person
for passengers, and are responsible to secure passengers
during boarding. Therefore, passengers have a shorter mean
travel time and mostly stay at the same location inside the
train, whereas train attendants have a longer time on board,
constantly move around, and have more, but shorter contacts
to other persons. Particularly short contacts are not considered
as elevated risks for infections.
5.Outlook
The global COVID-19 pandemic is a massive challenge on
many levels. Normality – in terms of an end of social
distancing necessity and intensified hygiene measures – is not
in sight, despite progress regarding vaccines is being made39.
SARSCoV2InfectionRisksinLongdistanceTrains Gravertetal
10
Another research project regarding the dispersal of droplets
and aerosols in train carriages is taking place at Deutsche Bahn
together with DLR (German Aerospace Center). The main
contributions will be measurements of different settings that
are performed in an actual train carriage under laboratory
conditions. As this project is in progress, no further results are
available to date. Results for coach busses and airplanes from
other publications exist, but only rely on the outcome of
simulations40,41.
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... ). Also, so-called superspreading events, with an explosive growth of infections, clearly seem to be tied to specific patterns of spatial crowding and mobility: besides the mentioned nursing homes and warships also large conferences [64], cold slaughter houses [65], and hot parties [66,67] (with poor air conditioning) -but not public trains [68], the exchange of bank notes [69], or outdoor events [70]. Once more, a small fraction of (in this case spatial 17 ) interaction patterns appears to produce almost all spreading [71]. ...
... And there is indeed new evidence from mobility surveys to support the notion that it is actually information about the spreading process (rather than centrally imposed mitigation orders, say), which arguably has the strongest impact on pertinent behavioral change in an epidemic -see, e.g., Fig. 23.4 26 and Refs. [40,68,71,129]. In this respect, the infected population may be viewed as an agent-based algorithm, employing swarm-intelligence in order to collectively outsmart the spreading pathogen, in a self-organized way [40,130]. This distinctive feature of epidemiological dynamics has various profound practical, social, and political implications, further elaborated in Sec. ...
Preprint
Full-text available
Epidemic disease spreading is conventionally often modelled and analyzed by means of rate and diffusion equations, following the paradigms of well-controlled chemical reactions and diffusive dynamics in a test tube. Yet, serious worries that this suggestive and appealing similarity might be a false friend were already voiced by the pioneers of mathematical epidemiology. A century later, we can draw on cross-fertilizations from network and game theory and the emerging field of eco-evolutionary dynamics to substantiate them. Epidemiological spreading is thereby revealed as a fundamentally heterogeneous and erratic process that shares certain properties with more unwieldy phenomena, such as earthquakes, hurricanes, traffic jams, and stock crashes. They are all characterised by high tail risks that materialize very rarely but fatally. That they arise from bursts of unlikely chains of localized random "superspreader" events, by which micro-scale fluctuations and uncertainties may get heftily magnified, makes their accurate prediction and control intrinsically and notoriously hard. That epidemic disease spreading is moreover closely intertwined with equally heterogeneous genetic drift and information feedback adds new challenges -- and chances.
... German Railway's long-distance division tested 625 train conductors, 242 train drivers, and 203 maintenance workers. The antibody seroprevalence of the tested employees was less than 2% [41]. Among the 103 cases surveyed in Hong Kong, Japan, Singapore, Taiwan, Thailand, and Vietnam, the five occupational groups with the most cases sin dall'inizio della pandemia si sono spesso focalizzate sui trasporti. ...
... Su 377 focolai COVID-19 in Cina, ad eccezione dello Hubei dove si ricorda essere sita Wuhan, il 79% risultava avvenire in ambiti familiari, mentre i trasporti con solo 6 cluster rappresentavano appena il 2% del totale [40]. La divisione "lunghe percorrenze" delle ferrovie tedesche su 625 addetti di bordo, 242 macchinisti e 203 addetti alla manutenzione testati riscontrava una sieroprevalenza degli anticorpi inferiore al 2% [41]. Su 103 casi censiti ad Hong Kong, Singapore, Taiwan, in Giappone, Tailandia e Vietnam, sono stati indentificati cinque gruppi occupazionali prevalenti, con gli addetti ai trasporti (18%) secondi agli operatori sanitari (22%), ed insieme agli addetti alle vendite e del terziario (18%), seguiti dal personale addetto alle pulizie domestiche (9%) e agli addetti di pubblica sicurezza (7%) [42]. ...
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The COVID-19 containment measures are parts of the key strategies is to reduce human mobility to slow down the spread, thus at a crossroad with transport policy and affecting travel behaviors. In this paper, based on information collected with expert questionnaire and supported by a literature review, we carried out an analysis on how transport policy is affected through various COVID-19 containment measures. Our analysis shows that the COVID-19 measures on public transport are virtually all restrictive, calling for additional behavioral changes, typically wearing face masks and enforced on a precautionary basis, since there seems no epidemiological consensus that public transport is a high-risk superspreading environment at the current status of knowledge. Moreover, from the result of the expert questionnaire, it is clear that a large variety of measures has been enforced, in response to the specific problem of containment, thus demonstrating that there was neither preparedness nor common vision among the public transport operators. On the contrary, for private motorized transport, COVID-19 measures are often oriented on ease of existing restrictions and regulations. This is not in line with the principle of sustainable transport policies to encourage the use of active modes and public transport and to discourage the use of private motorized modes. It shows the vulnerability of current transport policy and policy-making. © 2021, CIFI Collegio Ingegneri Ferroviari Italiani. All rights reserved.
... German Railway's long-distance division tested 625 train conductors, 242 train drivers, and 203 maintenance workers. The antibody seroprevalence of the tested employees was less than 2% [41]. Among the 103 cases surveyed in Hong Kong, Japan, Singapore, Taiwan, Thailand, and Vietnam, the five occupational groups with the most cases sin dall'inizio della pandemia si sono spesso focalizzate sui trasporti. ...
... Su 377 focolai COVID-19 in Cina, ad eccezione dello Hubei dove si ricorda essere sita Wuhan, il 79% risultava avvenire in ambiti familiari, mentre i trasporti con solo 6 cluster rappresentavano appena il 2% del totale [40]. La divisione "lunghe percorrenze" delle ferrovie tedesche su 625 addetti di bordo, 242 macchinisti e 203 addetti alla manutenzione testati riscontrava una sieroprevalenza degli anticorpi inferiore al 2% [41]. Su 103 casi censiti ad Hong Kong, Singapore, Taiwan, in Giappone, Tailandia e Vietnam, sono stati indentificati cinque gruppi occupazionali prevalenti, con gli addetti ai trasporti (18%) secondi agli operatori sanitari (22%), ed insieme agli addetti alle vendite e del terziario (18%), seguiti dal personale addetto alle pulizie domestiche (9%) e agli addetti di pubblica sicurezza (7%) [42]. ...
Article
Full-text available
The COVID-19 containment measures are parts of the key strategies is to reduce human mobility to slow down the spread, thus at a crossroad with transport policy and affecting travel behaviors. In this paper, based on information collected with expert questionnaire and supported by a literature review, we carried out an analysis on how transport policy is affected through various COVID-19 containment measures. Our analysis shows that the COVID-19 measures on public transport are virtually all restrictive, calling for additional behavioral changes, typically wearing face masks and enforced on a precautionary basis, since there seems no epidemiological consensus that public transport is a high-risk superspreading environment at the current status of knowledge. Moreover, from the result of the expert questionnaire, it is clear that a large variety of measures has been enforced, in response to the specific problem of containment, thus demonstrating that there was neither preparedness nor common vision among the public transport operators. On the contrary, for private motorized transport, COVID-19 measures are often oriented on ease of existing restrictions and regulations. This is not in line with the principle of sustainable transport policies to encourage the use of active modes and public transport and to discourage the use of private motorized modes. It shows the vulnerability of current transport policy and policy-making.
... This interim report is a follow-up to the publications [1] and [2], in which we describe our insights regarding the Covid-19 pandemic from DB Long-distance's (DB Fernverkehr) perspective. In the earlier publications, we first provided an extensive literature review [1] on infection risks in the public transport sector (with a strong focus on railway). ...
... The study has a longitudinal design with three test series one week in summer 2020, in fall 2020, winter 2021). The results from the summer 2020 test series have already been published [2]. Here we report the results of the second test series from fall 2020. ...
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This interim report is a follow-up to the publications [1] and [2], in which we describe our insights regarding the Covid-19 pandemic from DB Long-distance’s (DB Fernverkehr) perspective. In the earlier publications, we first provided an extensive literature review [1] on infection risks in the public transport sector (with a strong focus on railway). The paper also discussed the impact of the air conditioning system in train carriages with respect to airborne virus transmission via aerosols. A short introduction to the underlying theoretical model was presented as well. The work on this theoretical risk modelling prompted the idea at DB Fernverkehr to gather actual clinical evidence on the dissemination of SARS-CoV-2 within the company. An epidemiological study was therefore started in summer 2020 with the aim to empirically determine whether employees on board of trains have an increased infection risk as compared to the general population. Furthermore, the results would allow assessing the efficiency of the hygiene measures on board of long-distance trains. The study has a longitudinal design with three test series one week in summer 2020, in fall 2020, winter 2021). The results from the summer 2020 test series have already been published [2]. Here we report the results of the second test series from fall 2020. It is a translation of the German version of the interim report [3]. The study is sponsored by DB Long-distance and scientifically carried out by Charité Research Organisation GmbH (CRO). The following persons were involved in this publication (in alphabetical order): Ball, F. (DB Long-distance), Gravert, C. (DB AG; Christian.Gravert@deutschebahn.com), Kim, H.J. (CRO), Neubert, J. (CRO), Schultz-Heienbrok, R. (CRO; corresponding author, Robert.Schultz-Heienbrok@charite-research.org), Uhle, M. (CRO), Wagner, F. (CRO) [1] C. Gravert, P. Nagl, H.-P. Lang, F. Ball, A. Schöllmann, and S. Jeschke, “Preliminary Implications of COVID-19 on Long- Distance Traffic of Deutsche Bahn,” Deutsche Bahn AG, Jun. 2020. [Online]. Available:https://www.researchgate.net/publication/342353367_Preliminary_Implications_of_COVID-19_on_Long-_Distance_Traffic_of_Deutsche_Bahn. [2] C. Gravert, P. Nagl, F. Ball, and T. Körner, “Update on SARS-CoV-2 Infection Risks in Long- distance Trains,” Deutsche Bahn AG, Oct. 2020. doi: 10.13140/RG.2.2.14207.64165. [3] Charité Research Organisation GmbH, “Epidemiologischer Zwischenbericht nach zweiter Testreihe,” Charité Research Organisation GmbH, Berlin, Jan. 2021. [Online]. Available: https://www.drks.de/drks_web/AttachmentDownstreamServlet?ID=922839fe-0319-4cba-aea0-b37f77683a2a&LOCALE=de&FILENAME=210107_Kurzfassung-Studienbericht-DB_Zwischenbericht_Version5%200.pdf.
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Background: The German government has made it mandatory to wear respiratory masks covering mouth and nose (MNC) as an effective strategy to fight SARS-CoV-2 infections. In many countries, this directive has been extended on shopping malls or public transportation. The aim of this paper is to critically analyze the statutory regulation to wear protective masks during the COVID-19 crisis from a medical standpoint. Methods: We performed an extensive query of the most recent publications addressing the prevention of viral infections including the use of face masks in the community as a method to prevent the spread of the infection. We addressed the issues of practicability, professional use, and acceptability based on the community and the environment where the user resided. Results: Upon our critical review of the available literature, we found only weak evidence for wearing a face mask as an efficient hygienic tool to prevent the spread of a viral infection. However, the use of MNC seems to be linked to relevant protection during close contact scenarios by limiting pathogen-containing aerosol and liquid droplet dissemination. Importantly, we found evidence for significant respiratory compromise in patients with severe obstructive pulmonary disease, secondary to the development of hypercapnia. This could also happen in patients with lung infections, with or without SARS-CoV-2. Conclusion: Epidemiologists currently emphasize that wearing MNC will effectively interrupt airborne infections in the community. The government and the politicians have followed these recommendations and used them to both advise and, in some cases, mandate the general population to wear MNC in public locations. Overall, the results seem to suggest that there are some clinically relevant scenarios where the use of MNC necessitates more defined recommendations. Our critical evaluation of the literature both highlights the protective effects of certain types of face masks in defined risk groups, and emphasizes their potential risks.
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