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Outdoor Transmission of SARS-CoV-2 and Other Respiratory Viruses, a Systematic
Tommaso Celeste Bulfone, MS,a Mohsen Malekinejad, MD, DrPh,b George W. Rutherford,
MD, AM, b,c Nooshin Razani MD, MPHb,c,*
a. Joint Medical Program, University of California, Berkeley - University of California, San
Francisco. 2121 Berkeley Way, Room 5302 Berkeley, CA 94720-7360, USA.
b. Department of Epidemiology and Biostatistics, University of California, San Francisco.
550 16th St 2nd floor, San Francisco, CA 94158, USA
c. Department of Pediatrics, University of California, San Francisco
Corresponding author: Nooshin Razani, MD 550 16th St 2nd floor, San Francisco, CA
94158,, 415-722-1915
This systematic review found that while outdoor environments do seem at lower risk for
transmission of SARS-CoV-2 and other respiratory viruses than indoor environments, there are data
showing that infection transmission is possible outdoors, thus warranting further rigorous
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While risk of outdoor transmission of respiratory viral infections is hypothesized to be low,
there is limited data of SARS-CoV-2 transmission in outdoor compared to indoor settings.
We conducted a systematic review of peer-reviewed papers indexed in PubMed, EMBASE
and Web of Science and pre-prints in Europe PMC through August 12th, 2020 that described
cases of human transmission of SARS-CoV-2. Reports of other respiratory virus transmission
were included for reference.
Five identified studies found that a low proportion of reported global SARS-CoV-2 infections
have occurred outdoors (<10%) and the odds of indoor transmission was very high compared
to outdoors (18.7 times; 95% CI 6.0, 57.9). Five studies described influenza transmission
outdoors and two described adenovirus transmission outdoors. There was high heterogeneity
in study quality and individual definitions of outdoor settings which limited our ability to
draw conclusions about outdoor transmission risks. In general, factors such as duration and
frequency of personal contact, lack of personal protective equipment and occasional indoor
gathering during a largely outdoor experience were associated with outdoor reports of
Existing evidence supports the wide-held belief that the the risk of SARS-CoV-2
transmission is lower outdoors but there are significant gaps in our understanding of specific
Keywords: coronaviruses, SARS-CoV-2, COVID-19, transmission, outdoor
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Recommendations about methods to curb transmission of the severe acute respiratory
syndrome coronavirus type 2 (SARS-CoV-2) beyond wearing masks and maintaining social
distance have varied, especially regarding outdoor transmission.[1] This variability reflects a
general lack of information on how SARS-CoV-2 is transmitted outdoors.
Outdoor spaces generally allow for more physical distancing, which mitigates the risk of
virus transmission through larger respiratory droplets [2]. Outdoor spaces allow for airflow,
ventilation, and lack of recycled air, which all minimize the theoretical risk of aerosol
transmission through smaller respiratory droplets. While aerosol spread in community
settings is controversial, emerging data suggest that indoor recycled air can spread SARS-
CoV-2 with examples of spreading events in a restaurant in Guangzhou [3], at an indoor
choir practice in Skagit, Washington, USA [4], at a South Korean call center [5], at meat-
packing plants in the USA [6] and in a nursing home in the Netherlands [7]. In areas with low
ventilation, aerosolized droplets have the capacity to linger for longer before being inhaled or
falling to a surface, which could result in fomite transmission [8]. In enclosed environments,
low humidity, air conditioning, and low UV light may all contribute to longer survival of
viral particles [9]. Outdoor environments also generally have fewer high touch surfaces that
may harbor the virus. UV light, present outdoors from sunlight, results in a ten-fold decrease
in virus survival on surfaces [10]. Finally, indoor environments may increase host
susceptibility; the low indoor humidity has been associated with slower host ciliary clearance
and complications such as pneumonia, and lack of sunlight has been associated with lower
vitamin D levels [11]. For these reasons, the risk of virus transmission in outdoor locations
has been hypothesized to be lower than in indoor spaces.
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We sought to quantify the risk of SAR-CoV-2 transmission in outdoor settings. We
conducted a systematic review of the literature on transmission of SARS-CoV-2 to better
understand the risks of outdoor transmission. Where data was available, we estimated the risk
of outdoor compared to indoor transmission. Anticipating a paucity of data on SARS-CoV-2,
we chose a broad search strategy that included other human beta coronaviruses and
respiratory viruses.
Search strategy and selection criteria
Data for this review were identified by searches of PubMed, EMBASE, Web of Science, as
well as preprints available in Europe PMC [12]. Details of our search strategies and eligibility
criteria can be found in our protocol published on August 3rd, 2020 on PROSPERO (ID:
183826). The search was conducted on June 17th, 2020, and because of the rapidly expanding
data on SARS-CoV-2, the search was repeated to include most recent literature on August
12th, 2020.
Exposures and outcomes
The exposure of interest - outdoor gatherings - was defined as persons congregating outdoors
for work, social or recreational activities (Supplementary Material 1 for our full search
strategy). The outcome of interest included cases of transmission of SARS-CoV-2 or other
respiratory viruses identified by a case report, illness, or mortality. We also included
secondary outcomes of clusters or outbreaks of cases. Our search included any viral infection
that can be spread by respiratory droplets and, in addition to SARS-CoV-2, included the other
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two recognized human beta-human coronaviruses viruses (SARS-CoV-1 and Middle East
Respiratory Syndrome), human influenza viruses, adenoviruses, rhinoviruses, human
metapneumoviruses, and respiratory syncytial virus.
We included studies (experimental or observational with empirical data collection) that
described human-to-human transmission of respiratory viruses between humans in an outdoor
setting, any review of these studies, and any study (experimental or observational) that
compared respiratory viral transmission among humans in an outdoor versus indoor settings.
We excluded reviews of previously published data, studies of exclusively indoor outbreaks,
outdoor outbreaks within animal populations or between animals and humans, and outbreaks
where the site of transmission was not listed or was unclear. We also excluded studies limited
to built environments (homes, apartment buildings, military barracks), hospitals, or forms of
transportation (airplanes, trains, buses, cars, ships).
Data Selection and Extraction
After removing duplicate records, one author (TCB) reviewed all downloaded citations based
on their titles and pre-specified inclusion criteria. A second co-author (MM) reviewed a 5%
random sample of the excluded titles (rejected from initial search results) for quality control.
Two authors (TCB and NR) then independently screened the titles, abstracts and descriptor
terms and compared and discussed discrepancies until consensus was reached; a third author
(MM) served as an arbiter when needed. Two authors (TCB and NR) then independently
inspected the full texts of the remaining studies for relevance based on exposure, design and
outcome measures to select the included papers, and discussed discrepancies until consensus
was reached with a third author (MM) serving as arbiter. We used Endnote X9.3.2 (Clarivate
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Analytics, Philadelphia, Pennsylvania, USA) and Rayyan (Qatar Computing Research
Institute, Doha, Qatar) web-based software to manage search results [13].
Two authors (TCB and NR) extracted the following data from each paper into a pre-piloted
data extraction form in Excel spread sheets : complete citation, study location, study design,
details of participants (risk group or groups, sample size), exposure details (type of gathering,
characteristics of gathering place, number of people, duration, proportion of time spent
outdoors, amount if any of indoor transmission, how the non-exposure state (indoors) was
defined, outcomes (numerators and denominators associated with each outcome, definitions
and descriptions of outcomes provided in papers, details of how outcomes were assessed,
individual cases of infection and/or large spreading events, mortality), methodological details
(sample characteristics, how the information was gathered, how the outbreak was
investigated), and details related to bias assessment.
The combined searches yielded 10,912 unique citations, of which 12 studies met our
inclusion criteria. Nine studies were identified from the June 17th search, two from the
August 12th, and one from a targeted search. Out of the 12 that met our inclusion criteria, five
were pertaining to SARS-CoV-2 (Table 1 and 2), five reported on influenza or influenza-like
viruses (Table 3), and two reported on adenovirus transmission. Of note, 33 studies were
excluded because they did not specify the location of transmission (Supplementary Material
2). The PRISMA diagram is shown in Figure 1.
Five studies related to SARS-CoV-2 transmission found that less than 10 percent of reported
transmission occurred in outdoor settings, less than 5% of cases were related to outdoor
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occupations, and the odds of transmission or super spreading are much lower outdoors (Table
1) [1417].
Of 318 identified outbreaks involving three or more cases in China reported to local
Municipal Health Commissions from January 4 to February 11, 2020, Qian et al. found that
all occurred in indoor environments [14]. They reported a single transmission that occurred
outdoors (one case of outdoor transmission out of 7,324 total reported cases). This report,
however, might be affected by strict interventions prohibiting mass gatherings outdoors,
which may have contributed to the low number of cases contracted outdoors. Additionally,
relying on local health department reports may have led to underestimates of the total number
of transmissions, especially those which were asymptomatic [14].
Nishiura et al. [15] analyzed the transmission pattern of COVID-19 reported through
February 28, 2020 (11 clusters and sporadic cases) in Japan. They concluded that the odds of
a primary case transmitting COVID-19 in a closed environment were 18.7 times greater
compared to outdoor setting (defined as an open-air environment) (95% confidence interval
[CI]: 6.0, 57.9). The odds of a single case spreading to 3 or more individuals, which they
defined as a super spreader event, in closed environments compared to open air were as 32.6
(95% CI: 3.7, 289.5). This report, however, included no description of the context or location
of the outdoor transmission nor were any raw data provided. It is unclear whether this report
is relying on proportions, which again, may be subject to the fact that fewer people would
have been outdoors during winter months in Japan .
Leclerc et al. [16] reviewed 201 transmission clusters of COVID-19 world-wide that had
been reported up to March 30, 2020. The vast majority of these transmissions were associated
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with ―indoor‖ or ―indoor/outdoor‖ settings (197/201 clusters or 21/22 locations). The one
―outdoor‖ setting was at multiple construction sites in Singapore, where four outbreaks
Lan et al. [17] investigated 103 possible work-related cases of COVID-19 among a total of
690 local cases in six Asian countries or regions, including Hong Kong, Japan, Singapore,
Taiwan, Thailand, and Vietnam. In this paper, construction workers in Singapore constituted
only 5% of the total work-related transmissions. While this paper did not explicitly state
whether the location of work-related transmission was outdoor or indoors, it was included
based on Leclerc’s classification of the same construction workers as an ―outdoor‖ setting.
This does not rule out that that transmission may have occurred in indoor locations at
construction sites.
Szablewski et al. [18] report SARS-CoV-2 transmission at an overnight camp in Georgia,
USA, where attack rates increased with increasing length of time at the camp, and with co-
housing. Staff members, who stayed the longest at camp, had the highest attack rate (56%).
The outbreak was clustered by cabin assignments, which suggests a high likelihood of
transmission in indoor spaces during overnight cabin stays rather than during outdoor
activities during the day. The authors state that non-pharmaceutical interventions such as
cohorting and adults wearing masks during the day, were not protective, although no further
information is given about this claim.
While there is high heterogeneity in the studies describing outdoor transmission of SARS-
CoV-2, the studies we found highlight the conditions of outdoor exposure and transmission.
The location and context of SARS-CoV-2 transmissions reported in this review are
summarized in Table 4. Among these are examples of transmissions at a gathering in a park,
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but over multiple days with the same people, and at a camp, which lasted for several days and
had indoor housing components.
Five other studies included in Table 3 describe outdoor transmission of influenza or
influenza-like viruses. Summers et al. [19] conducted a historical analysis of a large outbreak
of the 1918 influenza virus on a military troop ship in July 1918. The outbreak involved over
1000 of the 1,217 crew members and caused 68 deaths. Analysis of factors that might have
contributed to mortality revealed a significant association between individuals who slept
indoors, in cabins with bunks (mortality of 146.1/1,000 population), versus individuals who
slept in hammocks in open-air areas (mortality of 34.1/1,000 population). This study is of
particular interest because the duration of exposure and distance between individuals was
held constant. This was one of the few studies which investigated potential confounders such
as age and social class mortality changed with age, but not with social class or rurality. Age
did not change the discrepancy in deaths seen outdoors compared to indoors.
Pestre et al. [20] conducted a retrospective analysis of a 2009 H1N1 influenza outbreak at a
summer camp in France. Investigations revealed that all febrile individuals had travelled
together in the same train wagon to reach camp, suggesting that the enclosed space facilitated
transmission. The three individuals out of 32 that had not travelled in the same train wagon as
all the other participants never developed symptoms, even though they were still present at
camp for two days with all other infected individuals - presumably mostly in outdoor spaces.
Finally, three manuscripts about respiratory illnesses at mass open-air gatherings emphasized
that while influenza outbreaks were uncommon, the duration of the event (multi-day over
single day) and communal housing were risk factors for outbreaks (Table 3). [2123] Rainey
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et al. concluded that all reported outbreaks in summer camps had social contact and
communal housing, none were reported without a shared housing component.[21] Of note, no
single-day mass gathering related outbreaks were detected in the 72 outbreaks they detail.
Figueroa et al. also did not identify any single day event-related outbreaks.[22] Botelho et al.
found four outbreaks of Influenza A (H1N1) and one of Influenza A and B; all events with an
outbreak were multi-day sport events while single-day events had none.[23]
Two articles discussed adenovirus outbreaks associated with lakes [24] and outdoor
swimming pools [25]. In both studies respiratory viral infection occurred in swimmers and in
others who did not swim, such as fellow camp attendees and family members, suggesting
human-to-human transmission prevalently occurring outdoors.
While the studies included in this review were highly heterogeneous, ranging in
methodology, definition of ―outdoor‖ transmission, and virus studied, several common
factors were identified. The studies with direct comparison of SARS-CoV-2 location of
transmission reported dramatically lower proportions occurring outdoors. The exact
determinants of outdoor transmission that can be gleaned from this review are limited, the
cases of outdoor transmission of SARS-CoV-2 we identified were affected by the duration of
exposure, frequency of exposure, density of gathering, whether maks were used, and were
confounded by the possibility of indoor transmission.
Historical evidence gleaned from influenza outbreaks further support the lower risk of
transmission outdoors. Summers et al. showed that influenza mortality on a ship was
significantly lower outdoors (sleeping in hammocks) compared to indoors (sleeping in
cabins). While mortality does not provide direct information about transmission, it serves as a
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useful proxy. Outcomes from several investigations of influenza outbreaks during mass
outdoor gatherings suggest that outdoor, single day events without communal sleeping
arrangements have lower risks of influenza transmission than multi-day events with indoor
components [2123].
These findings, as well as reports of influenza outbereaks and adenovirus outbreaks in
outdoor bodies of water, suggest that while outdoor transmission is less common than indoor,
it is not impossible. Case reports identified after our review was completed provide further
evidence that high density outdoor gatherings, particularly with low mask use, may lead to
higher transmission rates. Miron et. al noted that incidence of COVID-19 cases was
significantly higher in 14 out of 20 counties that had a large outdoor gathering 15 days
prior.[26] Dave et al. estimates that in the three weeks following the start of the Sturgis
motorcycle rally started on August 7th 2020, South Dakota, USA, an multi-day event with
500,000 participants, cases grew more in counties with weak mitigation policies than those
with strong mitigation policies (such as closure of restaurants and bars, or mask-wearing
mandates) as participants returned to their homes [27]. In contrast, although COVID-19 rates
increased in the three weeks following the mass protests in the United States [28], the uptick
in cases due to these events was less than expected because social distancing and masking
measures were more widespread [29]. The importance of protective measures is further
exemplified by the outdoor outbreak that occurred at the White House Rose Garden event on
September 26th 2020, where few of the 200 attendees were wearing masks or maintaining
social distancing measures.[30]
Of note, our search did not find any studies on the transmission of COVID-19 in settings of
outdoor agricultural work. In California prevalence of COVID-19 for agricultural workers is
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two to three times higher than the rate for workers in all other industries [31]. The experience
of agricultural workers suggests that crowded working or sleeping conditions may be a
substantive risk factor for transmission, but the contribution of work in outdoor spaces to
transmission risk has not been assessed. We found that outdoor, single day events without
communal sleeping arrangements have lower risks of transmission compared to multi-day,
mass outdoor gatherings in the spread of influenza [2123].
In order to better characterize the risks of outdoor SARS-CoV-2 exposure, future studies
should fill the research gaps we have identified in this review. First, many research studies
we identified did not report the location of transmission at all. This may be because
understanding relationships between cases is more important than the location of interaction,
or may be related to practical challenges in contact tracing outdoors. Second, it is difficult to
isolate an outdoor exposure to a virus. While outdoor gatherings could be largely safe, if they
are accompanied by time in indoor locations such as cabins or trains, it might be challenging
to identify exact location of transmission. Szablewski et al., which was included in our
review, while the summer camp may have been largely outdoors, it does not preclude from
exposure in the dining halls or cabins. As for construction sites, once a building is framed and
enclosed, it may be considered indoor work, which may in fact be the majority of the
work. Third, in many reports published early in the SARS-CoV-2 pandemic, the measured
outcome was "illness or death" due to viral infection, not SARS-CoV-2 infection itself, which
was rarely assessed. If asymptomatic infections are more likely to occur outdoors, this could
represent a systematic bias. Fourth, the definition of being ―outdoors‖ is ambiguous, and the
effect of exposure is likely modified by variable proximity to and contact with others. Fifth,
in order to test the hypothesis that the risk of infection is lower outdoors, future research
should collect data about time spent indoors versus outdoors. Given that 90% of time is spent
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indoors in high-and-middle income countries [32], then it would be expected that 90% of
transmission to occur indoors, all else being equal. Lastly, there are few data that examine
how respiratory droplets spread outdoors, such as how far they travel during running, biking,
or during windy conditions. A study examined these variables but was calculated with no
account of ventilation, sunlight, or humidity. [33]
Finally, most of the transmission events we identified in the literature did not report the
socioeconic status of those impacted. Spreading events often occur in settings where
marginalized and disempowered populations live or work such as lower-income, higher
density urban settings, work settings such as meat packing plants, or even prisons [34]. While
there are multiple reasons for the disproportionate impacts of COVID-19 in these
populations, we postulate that lack of opportunity to move high-risk activities outdoors may
be one of them. [35,36] While it was our intention to further explore this hypothesis by
analyzing sub-group socio-economic and ethnicity data in the studies included in this review,
the studies did not include these metrics.
Future studies could compare SARS-CoV-2 case rates at outdoor gatherings to known rates
for indoor gatherings. There are several examples of studies that estimate the risk of indoor
transmissison [3739] which have ranged from 10.3% (95% confidence interval [CI] 5.3%
19.0%) in a study of trains in China to 78% in a church in Arkansas [38]. Accurate estimation
of the risk of outdoor transmission will require determining person-time at risk for infection,
incidence rate ratios, and more nuanced information about the exposure environment; these
data are still lacking.
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Better understanding of how SARS-CoV-2 is transmitted outdoors is needed to inform sound
policies that reconcile shelter-in-place orders with the many health benefits associated with
time spent outdoors [40]. This is particularly relevant to outdoor parks and recreation
agencies, which seek clear guidance on how being outdoors has a low risk of transmission.
Other policy implications are to encourage moving essential activities outdoors, with
appropriate masking and social distancing measures, given that transmission can still occur
outdoors. The long term and potentially deleterious social and emotional effects of school
closures can be potentially mitigated if, for example, it is known that outdoor schooling is a
viable alternative. Finally, encouraging outdoor time may serve as a harm reduction model in
allowing people to congregate, and therefore better tolerate long-term shelter in place
This systematic review has several limitations. The few and heterogenous studies on outdoor
transmission of respiratory viruses had used various metrics, exposures and outcomes,
making it challenging to compare findings quantitatively. The low proportion of outdoor
COVID-19 cases may reflect the general decrease in outdoor activities since strict lockdowns
were enacted in the countries surveyed. Relying on reports of symptomatic infections may
under-represent asymptomatic cases that occur outdoors. If the viral inoculum affects the
severity of respiratory viral infection, an outdoor exposure may reduce the viral inoculum to
which the individual is exposed and therefore the subsequent clinical impact of the disease. If
this theory were true for SARS-CoV-2, it may increase the proportion of infections that are
asymptomatic.[41] The studies in this review did not contain much information about
potential confounders such as the age of infected individuals, activities in which they
participated, ethnicity, or social class. There was minimal information on mitigation efforts
such as masks and social distancing and how that may have impacted/influenced viral
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transmission. This review did not explicitly include gray literature (such as case reports from
health departments, lay newspaper sources) in its search strategy, as other comprehensive
reviews of transmissions have done.[16] Including preprints may have decreased our risk of
information bias.
While it has been acknowledged that spending time outside has general health
benefits, our review posits that there are also benefits in reducing transmission of SARS-
CoV-2 by reducing exposure time (substituting time indoors with time outdoors). These
results suggest that moving activities to outdoor settings may reduce infections and ultimately
save lives. However, it is important to note that infections are possible outdoors and the
advantage may be overtaken by relaxed mitigation efforts.
Tommaso Celeste Bulfone, MS, was supported by a Shoeneman grant at the UC Berkeley-
UCSF Joint Medical Program, Mohsen Malekinejad, MD DrPH and Nooshin Razani, MD
were supported by grants from the REI Foundation and the Long Foundation. Funders played
no role in the decision to conduct this study, the analysis, findings, or writing of this
manuscript. The authors do not have commercial or other associations that might pose a
conflict of interest.
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Table 1. Comparison of respiratory virus transmission outdoors compared to indoors ordered by virus
Estimate of effect
estimate of
Number of participants in
the study
Number of cases
2/7,324 cases
<1% of
7,324 cases, totaling 318
Number of cases
4/103 cases
99/103 cases
5% of work-
related cases
103 possible work-related
cases among a total of 690
local transmissions.
Odds of
(Raw data not
(Raw data not
Odds of
transmission in
18.7 (95% CI:
6.0, 57.9) times
greater than in
open air
110 cases: 27 primary cases
and 83 secondary cases
Number of
events and odds
of transmission*
1/7 super-
6/7 super-
spreading events
Odds ratio of
super spreading
in closed
32.6 (95%CI:
3.7, 289.5)
110 cases: 27 primary cases
and 83 secondary cases
Number of cases
95/10,926 cases
<1% of
10,926 cases, totaling 201
events of transmission
Number of cases
0/3 cases
24/29 cases
Out of 32 total
people in a
holiday camp, 29
traveled together
in a train wagon
32 people at a holiday camp
Mortality [19]
28/820 deaths
sleeping in
outside, 34.1
39/267, deaths
sleeping in
cabins inside,
Risk Ratio of
4.28, 95% CI
Total of 1,217 people on the
* superspreading defined as events where the number of secondary cases generated by a single primary case is greater than
the 95th percentile of the distribution (i.e. transmission to three or more persons)
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Table 2. Studies reporting outdoor SARS-CoV-2 transmission.
Location and
Outdoor exposure
Outdoor findings
Indoor findings
Qian et al.
cities in
Between 4
January and
11 February
7,324 cases, 318
analysis of all
public health
reports from local
Municipal Health
website to
location of
Location of
clusters and
Cluster was
defined as 3 or
more infections
that appear linked
to the same
infection venue.
An outbreak was
defined as a
cluster in which a
common index
patient is
Outbreaks were
organized by
relationship and
also by location.
Open air
One outdoor
involving two
cases in Shangqiu,
Henan: a 27-year-
old man had a
outdoors with an
individual who had
returned from
Of 318 identified
outbreaks that
involved 3 or more
cases, they all
occurred in indoor
Relied on
heterogenous case
reports of the local
health department,
which might have
missed cases
because of
allocation of
resources or
internal biases.
Additionally, the
data was collected
partly after lock-
down (started
January 23rd in
Wuhan), after
which most people
were indoors.
There was no
effort to access
exact locations of
Not peer-reviewed
at the time of
Nishiura et al.
prefectures in
Japan. Start
date of 28
110 cases (27
primary cases, 83
secondary cases).
events identified.
case investigation
using contact
tracing data.
Location and
number of
from primary to
secondary cases.
events defined as:
number of
Open air
Odds of
transmission in a
environment was
18.7 times greater
compared to an
environment (CI:
Out of seven
events, six of these
events (85.7%)
took place in
Small sample size
and no raw data
provided to
calculations of
odds. Limitations
were not discussed
in the manuscript.
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Table 2. Studies reporting outdoor SARS-CoV-2 transmission.
secondary cases
generated by a
single primary
case is greater
than the 95th
percentile of the
distribution (i.e.
transmission to
three or more
6.0, 57.9). The
odds ratio of
events in closed
environments was
as high as 32.6
(95% CI: 3.7,
event occurred
outdoors (not
Not peer-reviewed
at the time of
Leclerc et al.
locations, as
of March 30th
201 events of
Review of all
clusters (world-
wide) using
literature review
and open-source
Settings of
clusters for 201
22 types of settings
were determined.
Outdoor locations
were defined as
―outdoor‖, while
locations that were a
mixture were defined
as ―indoor/outdoor‖.
Indoor locations were
defined as ―indoor‖.
The transmissions
in the only
―outdoor‖ setting
occurred in four
outbreaks at
construction sites
in Singapore,
totaling 95 cases.
Updated results
- one transmission
occurred while
jogging in
Codogno, Italy
(non-peer re-
viewed source)
- Twenty cases in
an outdoor park in
Münster, Germany
reviewed source)
10/22 locations
defined as
indoor/outdoor, 11/
22 defined as
indoor. A total of
197 events
occurred in these
settings, totaling
10,831 cases.
Included reports
from some non-
peer reviewed
sources (eg. local
media outlets for
the jogging and
outdoor park
reports), which
might have been
influenced by
recall bias and
poor methodology.
While the study
conducted a
systematic review,
additional sources
were collected
using an open-
source strategy
which might have
been affected by
selection bias of
Lan et al.
Six Asian
690 locally
Number of cases
Workplace largely
A total of 103
The five
The exact
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Table 2. Studies reporting outdoor SARS-CoV-2 transmission.
Hong Kong,
Thailand, and
January 23,
2020 and
March 14,
transmitted cases
study, extracted
COVID-19 cases
reports. Only
transmitted (non-
imported) cases
were included.
period was
extended to 40
days from
primary case.
per occupation
country/area and
stratified into
early (first 10
days) and late (11-
40th day)
possible work-
related cases were
determined to be
outdoors among a
total of 690 local
transmissions. Of
workers that might
be prevalently
outdoors, 5% of
cases were
workers. Tour
guides (5% of
cases) might also
be considered to
have occurred
partly outdoors.
occupation groups
with the most cases
were healthcare
workers (22%),
drivers and
transport workers
(18%), services and
sales workers
(18%), cleaning
and domestic
workers (9%) and
public safety
workers (7%).
makeup of the
location of
transmission was
not described. This
is in part due to
the fact that the
source was not
always known,
and detailed
histories were also
not always known.
Also, none of the
reports arose from
systematic testing
of high-risk
occupations, rather
from individual
case reports,
which might have
been affected by
biased and
mechanisms from
different regions.
Szablewski et
camp in
USA. June
17-27 2020.
During June 1720
the overnight camp
held orientation for
138 trainees and
120 staff members;
staff members
remained for the
first camp session,
scheduled during
June 2127, and
were joined by 363
Positive test result
for SARS-CoV-2
(symptomatic and
Camp attendees were
cohorted by cabin and
engaged in a variety
of indoor and outdoor
activities, including
daily vigorous singing
and cheering.
On June 24 a staff
member tested
positive to SARS-
CoV-2. Test
results were later
available for 344
attendees; among
these, 260 (76%)
were positive. The
percentage of
transmission that
Median cabin
attack rate was
50% among 28
cabins that had one
or more cases (on
average, each cabin
housed 15 people).
Attack rate was
highest in the
larger cabin,
suggesting the
Attack rates are
likely an
because cases
might have been
missed among
persons not tested
or whose test
results were not
reported. Some
cases may have
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Table 2. Studies reporting outdoor SARS-CoV-2 transmission.
campers and three
senior staff
members on June
21. Children and
adults attended.
developed solely
outdoors was not
main location of
transmission was in
the cabins.
resulted from
occurring before
or after camp
attendance. Lastly,
exact details of
outdoor activities
versus indoor were
not described.
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Table 3. Studies reporting other outdoor respiratory virus transmission ordered by infection identified.
and Date
Outdoor findings
Indoor findings
et al.
States, 2009-
18 mass
gatherings in
8 states.
Data was
collected on mass
gathering related
disease outcomes.
50 state health
departments and
31 large local
departments were
contacted via
assessment. 43
(53%) of 81
Outbreak was
defined as one
or more cases
of an
disease. Mass
was defined as
a planned or
of 1,000 or
more persons
in either an
indoor or
outdoor venue
for a common
Mass gatherings
were defined as
indoors or
All reported
outbreaks occurred
at multi-day mass
gathering events.
For Influenza A
(H1N1) attack
rates at two
summer camps
were of 1.4% and
4.8% respectively.
Attack rate for a
religious event was
of 19.5%. At a
sporting event in
the spring, it was
of 3.3% - but only
included athletes.
Attack rate of
Inluenza A (H3) at
another summer
camp was of
At a professional
conference in
the winter,
which was likely
to be mostly
indoors, attack
rate was of
21.0%. Probable
factors that
affected attack
rates were
density and
rather than
gathering size
alone. Use of
surface cleaning)
might have been
an additional
Low response rate
(around 50%) by state
health departments,
while there was no
response from local
health departments.
There might be
responded bias, given
that departments which
experienced mass
gathering outbreaks
might have been more
willing to respond.
Furthermore, the details
of each mass gathering
and their
locations are not
Rainey et
States, 2005-
21 published
describing 72
Six medical,
behavioral and
social science
databases were
analyzed to
extract relevant
articles. NORS
were defined
as large events
more than
1,000 persons
in a specific
The authors did
not specify
outdoor vs indoor
location of mass
Close social
mixing and contact
in communal
were associated
with all other
identified. They
All reported
outbreaks in
summer camps
had social
contact and
housing, none
reported without
Search strategy might
have not captured
studies that did not use
the word ―outbreak‖,
and might have missed
any outbreak not
captured by
surveillance systems
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Table 3. Studies reporting other outdoor respiratory virus transmission ordered by infection identified.
reported to
was also analyzed
to estimate the
frequency of mass
disease outbreaks.
location for a
Definition of
outbreak was
deferred to the
Half of the
were related to
a zoonotic
source and
excluded. 38%
of the
occurred at a
variety of
concluded that
multiday mass
gatherings with
indoor residential
components can
a housing
(eg. smaller outbreaks,
of diseases with longer
incubation periods).
Not much detail was
shared on the
locations and activities
at the gatherings where
outbreaks occurred.
Nevers et
―open air
9 published
articles about
infections at
large, outdoor
festivals, or
Literature search
using ProMed and
database, with
using search
engines such as
google and yahoo
Outbreaks in
the setting of
Mass gatherings
defined as
outdoors‖, but
which may have
onsite housing
and food supply.
Four outbreaks of
Influenza A
(H1N1) and one of
Influenza A and B
were found.
Overall, the
incidence of
infections of
influenza per
100,000 attendees
ranged from 2 to
30. The
between sport
events, which seem
to have lower
No exclusively
indoor events
were included.
The infections related
to large open air
festivals may be under-
reported, given
difficulty in
ascertaining exact
location of transmission
and sporadic
surveillance systems.
The search strategy of
only using ProMed and
MEDLINE might have
limited the amounts of
results that might
otherwise be available
on other
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Table 3. Studies reporting other outdoor respiratory virus transmission ordered by infection identified.
incidence, and
large scale open air
festivals in terms
of infectious
diseases may also
be the consequence
of the relatively
short duration of
sports events
which frequently
last shorter than
one day.
Pestre et
2009 H1N1
camp in
August 2009
32 persons
in the holiday
camp. 29 of
them traveled
in the same
train wagon.
Infection of
Individuals who
did not travel in
the same train
The outbreak
involved 21
children and 3
adults who had all
travelled together
in the same wagon.
The three
individuals that did
not take the same
train wagon and
were immediately
thereafter in
contact with the 24
individuals at camp
did not experience
Out of 29
individuals who
took the same
train wagon, 21
children and 3
Conditions of outdoor
versus indoor activities
at camp were not
described. Given this,
the comparison
between indoor (train
wagon) and outdoor
(camp) exposure
assumes that a majority
of time at camp, as
compared to the train
wagon, was outdoors.
Measurement of cases
might have been
affected by timing of
testing and/or presence
of asymptomatic cases.
Limitations were not
et al.
troop ship in
1,217 persons
Sleeping in
hammocks as
opposed to cabins
with bunks
Out of 1,217
persons onboard,
over 1,000
suspected cases of
influenza, 68
deaths. Mortality
rate for persons
that slept in
Mortality rate
for persons that
slept in cabins
with bunks was
of 39/267 (146.1
population). The
Historical evidence
used in this paper is
subject to transcription
and/or recording errors,
lack of case definitions,
and approximate
estimates of case
numbers. While it is
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Table 3. Studies reporting other outdoor respiratory virus transmission ordered by infection identified.
Leone, July
outdoors was of
28/820 or of 34.1
hammocks was
(crude RR 4.28,
95% CI 2.69
6.81). Density
did not seem to
be a contributing
hinted that hammocks
were in higher
ventilated zones as
compared to cabins, the
exact location of
hammocks was not
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Table 4. Outdoor conditions where COVID-19 was transmitted
Description of transmission
Use of Non-Pharmaceutical
summer camp
Outbreak of 260 cases during an overnight camp
in Georgia.
Everyone was tested negative for COVID less
than or equal to 12 days prior to coming to camp.
While exact outdoor activities were not described,
the overnight component suggests that the attack
rate increased with length of time spent at the
camp. This was shown by staff members, who
were present at camp the longest, having the
highest attack rate (56%). Attack rate associated
with being adult, length of stay, and being in a
cabin together. Median attack rate in the cabins:
50%, overall attack rate 44%.
Yes. They state the NPI was not
effective. The non-pharmaceutical
interventions they tried was cohorting of
attendees by cabin (less than or equal to
26 persons), staggering of cohorts for use
of communal spaces, physical distancing
outside of cabin cohorts, and enhanced
cleaning and disinfection, especially of
shared equipment and spaces.
Cloth masks were required for staff
members. Evidently, these interventions
were not effective at preventing a
majority of cases.
in outdoor
setting [14]
One outdoor transmission involving two cases in
Shangqiu, Henan: a 27-year-old man had a
conversation outdoors with an individual who had
returned from Wuhan. No secondary or tertiary
cases from this transmission were reported
sites [16,17]
Four outbreaks at outdoor construction sites in
Singapore, involving a total of 95 cases [16]
Five cases of construction workers in Singapore
Details of exact location of transmission were not
described. Details of how ―indoors‖ versus
outdoors unknown. However, in Leclerc et al.
building sites were described as ―outdoor‖
outdoors [16]
One transmission while jogging in Codogno, Italy
(reported by local news media, cited in Leclerc et
al. open source database)
Outdoor park
Twenty cases in an outdoor park in Münster,
Germany (reported by local news media, cited in
Leclerc et al. open source database). The members
of the extended family, who had been living in
different houses in the Angelmodde district of
Munster, were suspected to have met often on a
playground in the Osthuesheide district. The
activites of the family were not described, but it
was described as a repeated exposure over days.
* Such as masks, physical distance, cohorting.
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Figure 1
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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]. ...
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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.
Limiting exposure to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus has been the major principle guiding public health measures. Masking, social distancing, as well as frequent hand hygiene have been the chief nonpharmaceutical interventions as preventive strategies for all age groups. Advancement in vaccine development and vaccination of large populations offer a glimmer of hope for containing and ending this pandemic. However, until immunization is widespread in the community, masking, social distancing, and frequent handwashing, as well as early detection and isolation of infected persons, should be continued to curb the spread of illness.
Objectives: To understand the impact of the COVID-19 pandemic on parental perceptions of health behaviors and food insecurity among children with overweight and obesity living in San Francisco and to assess the relationship between food insecurity and dietary intake during the pandemic. Methods: Parents of children ages 4-12 in San Francisco with BMI≥85th percentile measured at a clinic visit at an FQHC or academic practice from January 1st to March 15th, 2020 were eligible to participate. Parents completed a survey reporting on child health behaviors and household food insecurity prior to and since the start of the pandemic. Survey items were abstracted from validated surveys with adaptations. Regression models evaluated associations between food insecurity and dietary intake variables. Results: Most participants (n=145) were publicly insured (90%), Latino (77%), and spoke Spanish at home (70%). Parents perceived that child mean daily non-academic screen time was higher during the pandemic compared to before (3.8 hours vs 1.6 hours). Mean daily physical activity was reported to be lower compared to prior to the pandemic (1 hour vs 1.8 hours). On average, reported bedtime shifted 1.6 hours later. Food insecurity increased significantly, but was not associated with intake of fruits, vegetables, sugar-sweetened beverages, or foods with added sugar during the pandemic. Conclusion: Parents of children with overweight or obesity in San Francisco perceive increased child screen time, decreased physical activity and later bedtimes during the COVID-19 pandemic. Findings suggest a need for policies that support healthy lifestyle behaviors among low-income children during the pandemic.
Full-text available
Background COVID-19 is an infectious disease that has killed more than 555,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. Objectives We evaluated whether greenness was related to COVID-19 incidence and mortality in the US. Methods We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home orders. Results An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. Discussion Exposures to NDVI were associated with reduced county-level incidence of COVID-19 in the US as well as reduced county-level COVID-19 mortality rates in densely populated counties.
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Background: Train is a common mode of public transport across the globe; however, the risk of COVID-19 transmission among individual train passengers remains unclear. Methods: We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2,334 index patients and 72,093 close contacts who had co-travel times of 0–8 hours from 19 December 2019 through 6 March 2020 in China. We analysed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time. Results: The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI] 5.3% – 19.0%), with a mean of 0.32% (95%CI 0.29% – 0.37%). Passengers in seats on the same row as the index patient had an average attack rate of 1.5% (95%CI 1.3% – 1.8%), higher than that in other rows (0.14%, 95%CI 0.11% – 0.17%), with a relative risk (RR) of 11.2 (95%CI 8.6 –14.6). Travellers adjacent to the index patient had the highest attack rate (3.5%, 95%CI 2.9% – 4.3%) of COVID-19 infections (RR 18.0, 95%CI 13.9 – 23.4) among all seats. The attack rate decreased with increasing distance, but it increased with increasing co-travel time. The attack rate increased on average by 0.15% (p = 0.005) per hour of co-travel; for passengers at adjacent seats, this increase was 1.3% (p = 0.008), the highest among all seats considered. Conclusions: COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when travelling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.<br/
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Background : Concern about the health impact of novel coronavirus SARS-CoV-2 has resulted in widespread enforced reductions in people’s movement (“lockdowns”). However, there are increasing concerns about the severe economic and wider societal consequences of these measures. Some countries have begun to lift some of the rules on physical distancing in a stepwise manner, with differences in what these “exit strategies” entail and their timeframes. The aim of this work was to inform such exit strategies by exploring the types of indoor and outdoor settings where transmission of SARS-CoV-2 has been reported to occur and result in clusters of cases. Identifying potential settings that result in transmission clusters allows these to be kept under close surveillance and/or to remain closed as part of strategies that aim to avoid a resurgence in transmission following the lifting of lockdown measures. Methods : We performed a systematic review of available literature and media reports to find settings reported in peer reviewed articles and media with these characteristics. These sources are curated and made available in an editable online database. Results : We found many examples of SARS-CoV-2 clusters linked to a wide range of mostly indoor settings. Few reports came from schools, many from households, and an increasing number were reported in hospitals and elderly care settings across Europe. Conclusions: We identified possible places that are linked to clusters of COVID-19 cases and could be closely monitored and/or remain closed in the first instance following the progressive removal of lockdown restrictions. However, in part due to the limits in surveillance capacities in many settings, the gathering of information such as cluster sizes and attack rates is limited in several ways: inherent recall bias, biased media reporting and missing data.
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Objective There is limited evidence of work-related transmission in the emerging coronaviral pandemic. We aimed to identify high-risk occupations for early coronavirus disease 2019 (COVID-19) local transmission. Methods In this observational study, we extracted confirmed COVID-19 cases from governmental investigation reports in Hong Kong, Japan, Singapore, Taiwan, Thailand, and Vietnam. We followed each country/area for 40 days after its first locally transmitted case, and excluded all imported cases. We defined a possible work-related case as a worker with evidence of close contact with another confirmed case due to work, or an unknown contact history but likely to be infected in the working environment (e.g. an airport taxi driver). We calculated the case number for each occupation, and illustrated the temporal distribution of all possible work-related cases and healthcare worker (HCW) cases. The temporal distribution was further defined as early outbreak (the earliest 10 days of the following period) and late outbreak (11th to 40th days of the following period). Results We identified 103 possible work-related cases (14.9%) among a total of 690 local transmissions. The five occupation groups with the most cases were healthcare workers (HCWs) (22%), drivers and transport workers (18%), services and sales workers (18%), cleaning and domestic workers (9%) and public safety workers (7%). Possible work-related transmission played a substantial role in early outbreak (47.7% of early cases). Occupations at risk varied from early outbreak (predominantly services and sales workers, drivers, construction laborers, and religious professionals) to late outbreak (predominantly HCWs, drivers, cleaning and domestic workers, police officers, and religious professionals). Conclusions Work-related transmission is considerable in early COVID-19 outbreaks, and the elevated risk of infection was not limited to HCW. Implementing preventive/surveillance strategies for high-risk working populations is warranted.
Background: We aim to describe the basic demographics, clinical course and outcomes of critically ill patients with Covid-19 admitted to Avera McKennan Hospital and University Health Center Intensive Care Unit (ICU) between March 20 and May 4, 2020. Methods: In this single centered, retrospective, observational study, we enrolled 37 critically ill adults with COVID-19 pneumonia admitted to the (ICU) between March 20 and May 4, 2020. Demographic data, admitting symptoms, laboratory values, co-morbidities, treatments and clinical outcomes were collected. Data was compared between survivors and non-survivors. We aim to describe our data and report the 28-day mortality as of June 1, 2020. Results: Of 154 patients admitted with COVID-19 pneumonia during our study period, 37 (24 percent) were critically ill and required an ICU stay. The mean age was 58 years and 76 percent were men. Of these 37 patients, 28 (78 percent) had a chronic illness (diabetes in 43 percent, hypertension in 47 percent). In addition, 54 percent were associated with a local meat packing plant. Most common presenting symptoms were dyspnea (92 percent), cough (70 percent) and fever (68 percent). The mean PaO2/ FiO2 ratio was 143 (67-362). Significant lab findings include the following: 54 percent of patients had lymphocytopenia, the mean ferritin was 850 ng/mL (10-3528), the mean D-Dimer was 4.09 FEU ug/mL and the mean IL-6 was 96.5 pg/mL. At 28 days, 24 percent (nine) had died. Twenty-five (68 percent) patients required mechanical ventilation, with 10 (27 percent) of those patients requiring initiation of neuromuscular blocking agents for ventilator compliance. Of those four (40 percent) did not survive. In addition, 20 patients (54 percent) were proned. Pneumomediastinum or pneumothorax occurred in five of the 37 (14 percent). Renal replacement therapy was required in 6 of the 37 patients, 4 of whom (66 percent) died. Steroids were used in 70 percent of patients, tocilizumab in 59 percent, and hydroxychloroquine in 27 percent. All patients received antibiotics. Convalescent plasma became available for our 5th patient. A total of 29 (78 percent) received convalescent plasma, (86 percent of survivors and 56 percent non-survivors). Median ICU length of stay was 11 days for both survivors (1-49) and non-survivors (1-21). There were no differences in age, body mass index (BMI), or initial PaO2/FiO2 (P/F) among those two groups. Non-survivors (nine) included the two immune compromised patients in our cohort, two patients with pre-existing DNR/DNI status, and one death within two hours of admit. Compared with survivors, more of the non-survivors received vasopressors (78 percent vs 46 percent), dialysis (44 percent vs 7 percent) and hydroxychloroquine (44 percent vs 21 percent). The first 5 patients treated in the ICU did not survive. One month after the initial case was reported in South Dakota, our ICU experienced a six-week surge. At its highest, COVID-19-related census reached 63 percent of the ICU capacity (15/24). Conclusion: Mortality of critically ill patients with COVID-19 is high. Multi-organ, advanced and prolonged critical care resources are needed. Interpretation of our data is limited by a higher mortality of the earlier members of the cohort, a change in therapeutic practice over time and institution of social distancing.
Limited data are available about transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), among youths. During June 17-20, an overnight camp in Georgia (camp A) held orientation for 138 trainees and 120 staff members; staff members remained for the first camp session, scheduled during June 21-27, and were joined by 363 campers and three senior staff members on June 21. Camp A adhered to the measures in Georgia's Executive Order* that allowed overnight camps to operate beginning on May 31, including requiring all trainees, staff members, and campers to provide documentation of a negative viral SARS-CoV-2 test ≤12 days before arriving. Camp A adopted most† components of CDC's Suggestions for Youth and Summer Camps§ to minimize the risk for SARS-CoV-2 introduction and transmission. Measures not implemented were cloth masks for campers and opening windows and doors for increased ventilation in buildings. Cloth masks were required for staff members. Camp attendees were cohorted by cabin and engaged in a variety of indoor and outdoor activities, including daily vigorous singing and cheering. On June 23, a teenage staff member left camp A after developing chills the previous evening. The staff member was tested and reported a positive test result for SARS-CoV-2 the following day (June 24). Camp A officials began sending campers home on June 24 and closed the camp on June 27. On June 25, the Georgia Department of Public Health (DPH) was notified and initiated an investigation. DPH recommended that all attendees be tested and self-quarantine, and isolate if they had a positive test result.
This manuscript explores the question of the seasonality of severe acute respiratory syndrome coronavirus 2 by reviewing 4 lines of evidence related to viral viability, transmission, ecological patterns, and observed epidemiology of coronavirus disease 2019 in the Southern Hemispheres' summer and early fall.
Background As of June 8, 2020, the global reported number of COVID-19 cases had reached more than 7 million with over 400 000 deaths. The household transmissibility of the causative pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), remains unclear. We aimed to estimate the secondary attack rate of SARS-CoV-2 among household and non-household close contacts in Guangzhou, China, using a statistical transmission model. Methods In this retrospective cohort study, we used a comprehensive contact tracing dataset from the Guangzhou Center for Disease Control and Prevention to estimate the secondary attack rate of COVID-19 (defined as the probability that an infected individual will transmit the disease to a susceptible individual) among household and non-household contacts, using a statistical transmission model. We considered two alternative definitions of household contacts in the analysis: individuals who were either family members or close relatives, such as parents and parents-in-law, regardless of residential address, and individuals living at the same address regardless of relationship. We assessed the demographic determinants of transmissibility and the infectivity of COVID-19 cases during their incubation period. Findings Between Jan 7, 2020, and Feb 18, 2020, we traced 195 unrelated close contact groups (215 primary cases, 134 secondary or tertiary cases, and 1964 uninfected close contacts). By identifying households from these groups, assuming a mean incubation period of 5 days, a maximum infectious period of 13 days, and no case isolation, the estimated secondary attack rate among household contacts was 12·4% (95% CI 9·8–15·4) when household contacts were defined on the basis of close relatives and 17·1% (13·3–21·8) when household contacts were defined on the basis of residential address. Compared with the oldest age group (≥60 years), the risk of household infection was lower in the youngest age group (<20 years; odds ratio [OR] 0·23 [95% CI 0·11–0·46]) and among adults aged 20–59 years (OR 0·64 [95% CI 0·43–0·97]). Our results suggest greater infectivity during the incubation period than during the symptomatic period, although differences were not statistically significant (OR 0·61 [95% CI 0·27–1·38]). The estimated local reproductive number (R) based on observed contact frequencies of primary cases was 0·5 (95% CI 0·41–0·62) in Guangzhou. The projected local R, had there been no isolation of cases or quarantine of their contacts, was 0·6 (95% CI 0·49–0·74) when household was defined on the basis of close relatives. Interpretation SARS-CoV-2 is more transmissible in households than SARS-CoV and Middle East respiratory syndrome coronavirus. Older individuals (aged ≥60 years) are the most susceptible to household transmission of SARS-CoV-2. In addition to case finding and isolation, timely tracing and quarantine of close contacts should be implemented to prevent onward transmission during the viral incubation period. Funding US National Institutes of Health, Science and Technology Plan Project of Guangzhou, Project for Key Medicine Discipline Construction of Guangzhou Municipality, Key Research and Development Program of China.
On March 17, 2020, a member of a Skagit County, Washington, choir informed Skagit County Public Health (SCPH) that several members of the 122-member choir had become ill. Three persons, two from Skagit County and one from another area, had test results positive for SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). Another 25 persons had compatible symptoms. SCPH obtained the choir's member list and began an investigation on March 18. Among 61 persons who attended a March 10 choir practice at which one person was known to be symptomatic, 53 cases were identified, including 33 confirmed and 20 probable cases (secondary attack rates of 53.3% among confirmed cases and 86.7% among all cases). Three of the 53 persons who became ill were hospitalized (5.7%), and two died (3.7%). The 2.5-hour singing practice provided several opportunities for droplet and fomite transmission, including members sitting close to one another, sharing snacks, and stacking chairs at the end of the practice. The act of singing, itself, might have contributed to transmission through emission of aerosols, which is affected by loudness of vocalization (1). Certain persons, known as superemitters, who release more aerosol particles during speech than do their peers, might have contributed to this and previously reported COVID-19 superspreading events (2-5). These data demonstrate the high transmissibility of SARS-CoV-2 and the possibility of superemitters contributing to broad transmission in certain unique activities and circumstances. It is recommended that persons avoid face-to-face contact with others, not gather in groups, avoid crowded places, maintain physical distancing of at least 6 feet to reduce transmission, and wear cloth face coverings in public settings where other social distancing measures are difficult to maintain.