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SARS-CoV-2 transmission dynamics should inform policy


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It is generally agreed that striking a balance between resuming economic and social activities and keeping the effective reproductive number (R0) below 1 using non-pharmaceutical interventions is an important goal until and even after effective vaccines become available. Therefore, the need remains to understand how the virus is transmitted in order to identify high-risk environments and activities that disproportionately contribute to its spread so that effective preventative measures could be put in place. Contact tracing and household studies in particular provide robust evidence about the parameters of transmission. In this viewpoint, we discuss the available evidence from large-scale, well-conducted contact tracing studies from across the world and argue that SARS-CoV-2 transmission dynamics should inform policy decisions about mitigation strategies for targeted interventions according to the needs of the society by directing attention to the settings, activities and socioeconomic factors associated with the highest risks of transmission.
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Clinical Infectious Diseases
Received 11 June 2020; editorial decision 15 September 2020; published online 23 Septembe r
Correspondence: M. Cevik, Division of Infection and Global Health Research, School of
Medicine, University of St Andrews, Fife, KY16 9TF UK (
Clinical Infectious Diseases® 2020;XX(XX):1–6
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society
of America. All rights reserved. For permissions, e-mail:
DOI: 10.1093/cid/ciaa1442
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-
CoV-2) Transmission Dynamics Should InformPolicy
Muge Cevik,1 JuliaL. Marcus,2 Caroline Buckee,3 and TaraC. Smith4
1Division of Infection and Global Health Research, School of Medicine, University of St Andrews, St Andrews, United Kingdom, 2Department of Population Medicine, Harvard Medical School and
Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA, 3Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA, and
4College of Public Health, Kent State University, Kent, Ohio, USA
It is generally agreed that striking a balance between resuming economic and social activities and keeping the eective reproductive
number (R0) below 1 using nonpharmaceutical interventions is an important goal until and even aer eective vaccines become
available. erefore, the need remains to understand how the virus is transmitted in order to identify high-risk environments and ac-
tivities that disproportionately contribute to its spread so that eective preventative measures could be put in place. Contact tracing
and household studies, in particular, provide robust evidence about the parameters of transmission. In this Viewpoint, we discuss
the available evidence from large-scale, well-conducted contact-tracing studies from across the world and argue that severe acute res-
piratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics should inform policy decisions about mitigation strategies
for targeted interventions according to the needs of the society by directing attention to the settings, activities, and socioeconomic
factors associated with the highest risks of transmission.
Keywords. COVID-19; coronavirus; SARS-CoV-2; novel coronavirus; transmission.
Since coronavirus disease 2019 (COVID-19) was first described
in December 2019, we have witnessed widespread implemen-
tation of local and national restrictions in many areas of the
world and social, health, and economic devastation due to di-
rect and indirect impact of the pandemic. It is generally agreed
that striking a balance between resuming economic and social
activities and keeping the effective reproductive number (R0)
below 1 using nonpharmaceutical interventions is an important
goal until and even after effective vaccines become available.
Achieving this balance requires an understanding of how the
virus is spread. There is also a need to identify the structural
factors that contribute to transmission, a particular concern
considering the already stark health disparities driven by socio-
economic and racial/ethnic inequities in our societies.
An understanding of severe acute respiratory syndrome co-
ronavirus 2 (SARS-CoV-2) transmission dynamics can inform
policy decisions by directing attention to the settings and ac-
tivities that confer the highest risk of transmission and un-
derstanding of the intersection between poverty, household
crowding, and COVID-19. is understanding will allow pol-
icymakers and public health practitioners to shape the best
strategy and preventative measures and inform the public about
transmission risk. Epidemiological investigations including
contact-tracing studies and outbreak investigations conducted
so far across the world already provide crucial information
about the probability of infection in close contacts and various
environments. We argue that health authorities should use the
large-scale, well-conducted contact-tracing studies and obser-
vations from across the world to date in their risk assessment
and mitigation strategies. is article summarizes current
knowledge about transmission dynamics and discusses recom-
mendations that could prevent infections by focusing on factors
associated with risk of transmission.
Emerging data suggest that risk of transmission depends on
several factors, including contact pattern, host-related infect-
ivity/susceptibility pattern, environment, and socioeconomic
factors (Figure 1). We will discuss the emerging evidence re-
lating to each of these aspects of transmission.
Contact-tracing studies provide early evidence that sustained
close contact drives the majority of infections and clusters. For
instance, living with the case, family/friend gatherings, dining,
or traveling on public transport were found to have a higher risk
for transmission than market shopping or brief (<10 minutes)
community encounters [13]. While people are more likely to
recall and disclose close and household contacts, and it is easier
for tracers to identify the source, household studies provide im-
portant information about the contact patterns and activities
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associated with higher attack rates. Close contacts with the
highest risk of transmission are typically friends, household
members, and extended family, with a secondary attack rate that
ranges from 4% to 35% [1, 48]. In the same household, higher
attack rates are observed among spouses compared with the rest
of the household [8]. A systematic review including 5 studies
based on relationship demonstrated that household SAR (sec-
ondary attack rate) to spouses (43.4%; 95% confidence interval
[CI], 27.1–59.6%) was significantly higher than to other rela-
tionships (18.3%; 95% CI, 10.4–26.2%) [8]. Similar results were
observed in the USS Theodora Roosevelt outbreak in which
those sharing the same sleeping space had a higher risk of being
infected [9]. In addition, the attack rate has shown to be higher
when the index case is isolated in the same room with the rest
of the household or when the household members have daily
close contact with the index case [10, 11]. Transmission is sig-
nificantly reduced when the index case is isolated away from the
family, or preventative measures such as social distancing, hand
hygiene, disinfection, and use of face masks at home are applied
[10, 11]. In a study of an outbreak in the largest meat-processing
plant in Germany, while the universal point of potential con-
tact among all cases was the workplace, positive rates were
statistically significant for a single shared apartment, shared
bedroom, and associated carpool [12]. These findings suggest
that sleeping in the same room or sharing the same sleeping
space and increased contact frequency constitute a high risk of
Large clusters have been observed in family, friend, and work-
colleague gatherings including weddings and birthday parties [13,
14]. Other examples include gatherings in pubs, church services,
and close business meetings [1417]. ese ndings suggest that
group activities pose a higher risk of transmission. In non–house-
hold contact-tracing studies, dining together or engaging in group
activities such as board games have been found to be a high risk
for transmission as well [18]. In the same household, frequent
daily contact with the index case and dining in close proximity
have been associated with increased attack rates [10, 11].
Large, long-term-care facilities such as nursing homes and
homeless shelters have seen increased rates of infection, in part
because of patterns of contact among sta and residents. In
nursing home outbreak investigations from the Netherlands,
Boston, and London, multiple viral genomes were identied,
suggesting multiple introductions to the facility leading to in-
fections among residents [1921]. In an investigation of 17
nursing homes that implemented voluntary sta connement
with residents, including 794 sta members and 1250 residents
Proximity to index cas
Time of contact
Duraon of exposure
Contact frequency
Long term care facilies
Severity of illness
Host defence factors
Job insecurity
Prolonged working hours
Household crowding
Figure 1. Factors influencing transmission dynamics. Transmission depends on several factors, including contact pattern (duration of contact, gathering, proximity,
activity), environment (outdoor, indoor, ventilation), host-related infectivity/susceptibility pattern (ie, viral load in relation to disease course, severity of illness, age), and so-
cioeconomic factors (ie, crowded housing, job insecurity, poverty). Virus infectivity and differences between other viruses and host immune factors are not discussed in this
review. (This figure was created by the authors based on available literature about SARS-CoV-2 transmission dynamics.) Abbreviation: SARS-CoV-2, severe acute respiratory
syndrome coronavirus 2.
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in France, sta conning themselves to a single facility for a
weeklong period was associated with decreased outbreaks in
these facilities [22].
ese ndings emphasize that contact patterns, including the
duration of contact, contact frequency, proximity to index case,
and types of activities, inuence transmission risk, highlighting
the need for tailored prevention strategies for dierent settings.
Contact tracing and outbreak investigations suggest that many
people with SARS-CoV-2 either do not contribute to onward
transmission or have minimal potential to do so [6, 17], and
a large number of secondary cases are often caused by a small
number of infected patients. While this may also be due to con-
tact pattern and environmental factors, host factors strongly in-
fluence this variation; individual variation in infectiousness is
an expected feature of superspreadingevents.
Timing of the contact with an index case is key in transmis-
sion dynamics as it relates to the infectiousness of the index case.
In a systematic review of studies published up to 6 June 2020,
we found that viral load peaks early in the disease course, with
the highest viral loads observed from symptom onset to day 5,
indicating a high level of infectiousness during this period [23]
(Figure2). Supporting these ndings, transmission events are
estimated to occur in a short window, likely a few days prior to
and following symptom onset [4, 23]. For example, a contact-
tracing study that followed up 2761 contacts of 100 conrmed
COVID-19 cases demonstrated that infection risk was higher
if the exposure occurred within the rst 5days aer symptom
onset, with no secondary cases documented aer this point [4].
is understanding indicates that viral dose plays an important
role in transmission dynamics. In contrast, higher viral loads in
severe acute respiratory syndrome coronavirus (SARS-CoV-1)
and Middle East respiratory syndrome coronavirus (MERS-
CoV) were identied in the second week aer symptom onset,
suggesting that patients had viral load peak aer hospitalization
[23]. erefore, early viral load peak also explains ecient com-
munity SARS-CoV-2 spread in contrast to SARS-CoV-1 and
MERS-CoV, during which community spread was put under
control; however, nosocomial spread was an important feature
of the outbreaks. In contrast, during COVID-19, only a small
number of hospital-based outbreaks have been reported so far,
which may be due to a downtrend in viral load levels later in the
disease course [23, 24].
Symptoms and severity of illness appear to inuence trans-
mission dynamics as well. People with symptoms appear to have
a higher secondary attack rate compared with presymptomatic
and asymptomatic index cases (those who develop no symptoms
Figure 2. SARS-CoV-2 viral load dynamics and period of infectiousness. Incubation period (time from exposure to symptom onset) of 6days (2–21days), peak viral load
levels documented from day 0 (symptom onset) to day 5, infectious period starts before symptom onset up to 10days (this may be extended in patients with severe illness),
and RNA shedding continues for a prolonged period of time but culturable virus has been identified up to day 9 of illness. (This figure was created by the authors on Biorender, based on available literature about SARS-CoV-2 viral load dynamics.) Abbreviations: max, maximum; PCR, polymerase chain reaction; SARS-CoV-2,
severe acute respiratory syndrome coronavirus 2.
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throughout the illness) [18]. While asymptomatic patients can
transmit the virus to others, the ndings from 9 studies in a sys-
tematic review, including studies published up to 3 July 2020,
found secondary attack rates of 0% to 2.8%, compared with sec-
ondary attack rates of 0.7% to 16.2% in symptomatic cases in
the same studies, suggesting asymptomatic index cases transmit
to fewer secondary cases [18]. Another systematic review that
included studies published up to 10 June 2020 similarly found a
reduced risk of transmission for asymptomatic versus sympto-
matic cases (.35; 95% CI, .10–1.27) and presymptomatic versus
symptomatic cases (.63; 95% CI, .18–2.26) [25]. ere are also
dierences in attack rates based on symptom severity. In the
Zhang etal [26] study the secondary attack rate was 3.5% for
those with mild symptoms, 5.7% for those with moderate symp-
toms, and 4.5% for those with severe symptoms (based on the
China Centers for Disease Control guidelines). In a contact-
tracing study, contacts of severe cases were more likely to de-
velop severe infections themselves [4].
Virus transmission is also aected by a number of other host
factors, including host defense mechanisms and age. Current
synthesis of the literature demonstrates signicantly lower
susceptibility to infection for children aged under 10 years
compared with adults given the same exposure, and elevated
susceptibility to infection in adults aged over 60years compared
with younger or middle-aged adults [27].
Transmission risk is not one-dimensional and contact patterns
also depend on the setting of the encounter. Findings from
contact-tracing studies in Japan suggest an 18.7-fold higher risk
of transmission indoors compared with outdoor environments
[28]. These findings are in keeping with our understanding
about transmission patterns of respiratory viral infections.
While outdoor settings usually have lower risk, prolonged con-
tact in an enclosed setting can lead to increased risk of trans-
mission. Especially when combined with environmental factors
such as poor ventilation and crowding this may lead to further
increases in attack rates. Epidemiological studies so far support
this knowledge. SARS-CoV-2 is much more efficiently spread
in enclosed and crowded environments. The largest outbreaks
from across the world are reported in long-term-care facilities
such as nursing homes, homeless shelters, prisons, and also
workplaces including meat-packing plants and factories, where
many people spend several hours working together, dining and
sharing communal spaces [12, 14]. A study in 6 London care
homes experiencing SARS-CoV-2 outbreaks identified a high
proportion of residents (39.8%) and staff (20.9%) who tested
positive for SARS-CoV-2 [20]. Among 408 individuals residing
at a large homeless shelter in Boston, 36% of those tested were
found to be positive [16]. Although it is much harder to ob-
tain data from incarcerated populations, the largest clusters of
cases observed in the United States have all been associated with
prisons or jails, suggesting a high attack rate in these institu-
tional settings [29]. Social distancing is the opposite of incarcer-
ation, and overcrowding, poor sanitation and ventilation, and
inadequate healthcare contribute to the disproportionate rates
of infections seen in prisons and jails, which demonstrates the
larger pattern of the health disparities in our societies.
Socioeconomic Factors and Racial/Ethnic Disparities
Global figures suggest that there is a strong association between
socioeconomic deprivation, race/ethnicity, and a higher risk of
infection and death from COVID-19 [30, 31]. People facing the
greatest socioeconomic deprivation experience a higher risk
of household and occupational exposure to SARS-CoV-2, and
existing poor health leads to more severe outcomes if infected
[32]. People with lower-paid and public-facing occupations are
often classified as essential workers who must work outside
the home and may travel to work on public transport. Indeed,
in New York City, higher cumulative infection rates were ob-
served in neighborhoods that continued to engage in mobility
behaviors consistent with commuting for work [33]. These oc-
cupations often involve greater social mixing and greater expo-
sure risk due to prolonged working hours, resulting in reduced
ability to practice social distancing among low-income families
[34]. In addition, households in socioeconomically deprived
areas are more likely to be overcrowded, increasing the risk of
transmission within the household. Black, Hispanic, and other
marginalized, racial/ethnic, and migrant groups have also been
shown to be at greater risk of infection, severe disease, and
death from COVID-19 [31, 3537]. These increased risks are
also likely due to socioeconomic conditions that increase the
risk of transmission, inequitable access to adequate healthcare,
and higher rates of comorbidities due to adverse living and
working conditions and structural racism. It is not surprising
that the largest outbreaks are observed in meat-packing plants,
and most commonly exposed occupations include nurses, taxi
and bus drivers, and factory workers [31]. These disparities
also shape the strong geographic heterogeneities observed in
the burden of cases and deaths—for example, across the United
States and the United Kingdom [31, 38]. These findings support
the hypothesis that the COVID-19 pandemic is strongly shaped
by structural inequities that drive household and occupational
risks, emphasizing the need to tailor effective control and re-
covery measures for these disadvantaged communities propor-
tionate to their greater needs and vulnerabilities.
Large Clusters and SuperspreadingEvents
Clusters have become a prominent characteristic of SARS-
CoV-2, which distinguishes it from seasonal influenza [14, 17].
This emphasises that large clusters and superspreading events
may be the driver of the majority of infections, just as they were
for SARS-CoV-1 in 2002–2003 [39, 40]. For instance, during the
2003 SARS-CoV-1 outbreak, over 70% of infections were linked
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to superspreading events in Hong Kong and Singapore [39].
Hallmarks for superspreading events include a combination of
factors, typically a highly infectious individual(s) gathered with
other individuals in enclosed and crowded environments [14,
17]. There have been several superspreading events reported so
far. For example, an outbreak investigation from China identi-
fied that 24 out of 67 passengers were infected during a 50-mi-
nute return bus journey, which was linked to an index case who
was symptomatic the day before the trip. In contrast, during the
event, only 6 people were infected, all of whom were in close
contact with the same index case [41]. In Washington State, a
mildly symptomatic index case attended a choir practice (the
practice was 2.5 hours), and out of 61 persons, 32 confirmed
and 20 probable secondary COVID-19 cases occurred with an
attack rate of 53.3% to 86.7% [42]. While these superspreading
events occur, the frequency of these events and whether they are
caused by a single index case are unclear. The modeling suggests
that several independent introductions might be needed be-
fore a COVID-19 outbreak eventually takes off, meaning often
these large outbreaks occur when multiple infected persons are
introduced to the environment, as shown in the nursing home
investigation [43]. Other large outbreaks are reported in night
clubs, karaoke bars, and pubs [14, 17], which may be related to
crowding, leading to multiple introductions into the same set-
ting as seen in nursing home investigations. These findings and
observations suggest that contact-tracing investigations need to
be combined with phylogenetic analysis to understand the set-
tings and activities most likely to yield a superspreading event
to inform preventative measures.
Increased risk of transmission in deprived areas and among
people in low-paid jobs suggests that poverty and household
crowding need to be addressed with interventions that go be-
yond guidance on social distancing, hand hygiene, and mask
use. Previous research suggests that, although social distancing
during the 2009 H1N1 swine flu pandemic was effective in re-
ducing infections, this effect was most pronounced in house-
holds with greater socioeconomic advantage. Similar findings
are emerging for COVID-19, with the ability to practice social
distancing strongly differentiated by county and household in-
come [34]. The disproportionate impact of COVID-19 on
households living in poverty and the racial and ethnic dispar-
ities observed in many countries emphasize the need to urgently
address these inequities that directly impact health outcomes.
This includes social and income protection and support to en-
sure low-paid, nonsalaried, and zero-hours contract workers can
afford to follow isolation and quarantine recommendations; pro-
vision of protective equipment for workplaces and community
settings; appropriate return-to-work guidelines; and testing and
opportunities for isolation outside of the home to protect those
still atwork.
Second, knowing which contacts and settings confer the
highest risk for transmission can help direct contact-tracing
and testing eorts to increase the eciency of mitigation strat-
egies. Early viral load peak in the disease course indicates
that preventing onward transmission requires immediate self-
isolation with symptom onset, prompt testing, and results with
a 24- to 48-hour turnaround time, and robust contact tracing.
In many countries, people with symptoms access testing late
in the disease course, by which time they may have had mul-
tiple contacts while in the most infectious period. While self-
isolation with symptoms is crucial, 75% of those with symptoms
and their contacts in the United Kingdom reported not fully
self-isolating [44]. While presymptomatic transmission likely
contributes to a fraction of onward transmission, over half of
transmission is caused by those with symptoms, especially in
the rst few days aer symptom onset. ese ndings suggest
that messages should prioritize isolation practice, and policies
should include supported isolation and quarantine.
ird, policymakers and health experts can help the public
dierentiate between lower-risk and higher-risk activities and
environments and public health messages could convey a spec-
trum of risk to the public to support engagement in alterna-
tives for safer interaction, such as in outdoor settings. Without
clear public health communication about risk, individuals may
xate on unlikely sources of transmission—such as outdoor ac-
tivities—while undervaluing higher-risk settings, such as family
and friend gatherings and indoor settings. Enhancing commu-
nity awareness about risk can also encourage symptomatic per-
sons and contacts of ill persons to isolate or self-quarantine to
prevent ongoing transmission.
Finally, because crowded indoor spaces and gatherings likely
will continue to be the driver of transmission, public health
strategies will be needed to mitigate transmission in these set-
tings (eg, nursing homes, prisons and jails, shelters, and meat-
packing plants), such as personal protective equipment and
routine testing to identify infected individuals early in the di-
sease course. As part of the pandemic response we may need
to consider fundamentally redesigning these settings, including
improved ventilation, just as improved sanitation was a re-
sponse to cholera. Such strategies should be adopted in settings
where large outbreaks and superspreading events have been
identied by contact-tracing studies.
While modeling studies and computer simulations could con-
tribute to our understanding of transmission dynamics and aer-
odynamics of droplets, contact-tracing studies provide real-life
transmission dynamics and individual and structural factors as-
sociated with SARS-CoV-2 transmission, which are essential to
shape our public health plans, mitigate superspreading events,
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and control the current pandemic. Further understanding of
transmission dynamics is also critical to developing policy re-
commendations for reopening businesses, primary and sec-
ondary schools, and universities.
Financial support. J. L. M. is supported in part by the US National
Institute of Allergy and Infectious Diseases (grant number K01 AI122853).
Potential conicts of interest. J.L. M.has consulted for Kaiser Permanente
Northern California on a research grant from Gilead Sciences. All other au-
thors report no potential conicts. All authors have submitted the ICMJE
Form for Disclosure of Potential Conicts of Interest. Conicts that the edi-
tors consider relevant to the content of the manuscript have been disclosed.
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... To account for this, we used a crowding metric to evaluate household risk, and found that crowding at home increased odds of having secondary cases more than four-fold. Crowding has previously been associated with both increased transmission and severity of respiratory infections (Cevik et al. 2020), possibly related to limited space in a household, fewer opportunities for ventilation, and/or longer or more direct exposure to index cases (Leclerc et al. 2020), (Villela 2021). This reflects a particular challenge in Uganda, where the average household size is 4.6 persons, yet 45% of households have only one room for sleeping(Uganda Bureau of Statistics 2019), (Okonkwo et al. 2020). ...
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Objective To investigate factors associated with COVID-19 among household members of patients in home-based care (HBC) in western Uganda. Methods We conducted a case-control and cohort study. Cases were RT-PCR-confirmed SARS-CoV-2 diagnosed 1-30 November 2020 among persons in HBC in Kasese or Kabarole Districts. We compared 78 case-households (≥1 secondary case) to 59 control-households (no secondary cases). The cohort included all case-household members. Data were captured by in-person questionnaire. We regressed to calculate odds and risk ratios. Results Case-households were larger than control-households (mean 5.8 vs 4.3 members, p<0.0001). Having ≥1 household member per room (aOR=4.5, 95%CI 2.0-9.9) or symptom development (aOR=2.3, 95%CI 1.1-5.0), interaction (aOR=4.6, 95%CI 1.4-14.7) with primary case-patient increased odds of case-household status. Households assessed for suitability for HBC reduced odds of case-household status (aOR=0.4, 95%CI=0.2-0.8). Interacting with primary case-patient (aRR=1.7, 95%CI 1.1-2.8) increased the risk of individual infection among household members. Conclusion Household and individual factors influence secondary infection risk in HBC. Decisions about HBC should be made with these in mind.
... Int Ophthalmol through the nose, eyes, and mouth following contact with the surfaces contaminated with the virus [1]. SARS-CoV-2 has also been detected in non-respiratory samples, including stool, blood, ocular secretions, and semen, although the role of these sites in the transmission is unclear [2]. ...
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Objective As with any healthcare practice, elective surgeries had to be postponed since the start of the Covid-19 pandemic. This study aimed to examine the characteristics of ophthalmology outpatients and eye surgery admissions during the COVID-19 pandemic and also to compare the pandemic and pre-pandemic periods. Methods This retrospective study included patients admitted to the ophthalmology clinic of a tertiary hospital from April through June 2020. A control sample was formed using the registries from the same interval in the previous year. The primary endpoint was the difference between the number and distribution of types of surgical procedures in the pre-pandemic and pandemic period. Surgical procedures were classified as Group A; major special, Group B; special, Group C; major, Group D; medium, and Group E; minor surgeries. Also surgeries were also divided into 4 groups. Cataract and related surgeries were grouped as “Phaco”, emergency surgeries for trauma patients as “Trauma”, retina and related surgeries were grouped as “Retina”, and eyelid and adnexal surgeries were grouped as “Eyelid”. The secondary endpoint was the comparison between the pre-pandemic and pandemic period. Results A total of 116 operations were performed in 2020 (mean age: 42.3 ± 25.6 years, male: 63.8%). In 2019, 873 surgeries were performed in the same period of the year (mean age: 56.6 ± 20.2 years, male: 48.8%), indicating an 86.7% decrease during the pandemic period, and each surgery type reduced significantly. On the other hand, the proportion of Group A (10.3% in 2019 vs. 25.9% in 2020, p < 0.001), group B (5.4% in 2019–17.24% in 2020, p < 0.001), and group E (3.8% in 2019–8.6% in 2020, p < 0.001) surgeries among the total increased in the pandemic period. The proportion of trauma (3.1% in 2019–16.4% in 2020, p < 0.001) and retina (11.9% in 2019–37.1% in 2020, p < 0.001) surgeries also increased, whereas phaco and eyelid surgeries were recorded at a lesser rate in the pandemic period. Conclusion This study showed a striking reduction in eye surgery during the early period of the Covid-19 pandemic. However, the rates of group A, B, and E surgeries increased significantly compared to the previous year.
Objectives: To better understand the knowledge, practice, importance, awareness, usefulness, and confidence of non-Hispanic Black and English- and Spanish-speaking Hispanic/Latino adults with diabetes. Design: A descriptive cross-sectional survey study design was used and descriptive statistics was conducted. Sample: Non-Hispanic Black and Hispanic/Latino adults with diabetes were recruited from three New York City public hospitals. Measurements: A one-time survey was delivered via email, text message or over the phone. Results: Of the 96 participants, 47.9% were Hispanic/Latino and 52.1% were non-Hispanic Black individuals; 43.8% of the surveys were completed in Spanish and 56.3% in English; 41.7% were female and 58.3% male; 77.1% preferred to complete the survey via the telephone, 14.6% through email, and 8.3% via text message. Chi-square findings showed, 90.6% knew mask wearing prevented COVID-19; 96.9% knew that covering the nose and mouth during mask wearing is needed, 93.8% wore a mask, and 92.8% felt it important or very important to wear a mask to prevent the spread of COVID-19. For social distancing, 88.5% knew it prevented the spread of COVID-19, 93.8% practiced it, and 95.8% felt it important or very important. Conclusion: In having a better understanding of the knowledge and practices of COVID-19 among non-Hispanic Black and Hispanic populations with diabetes, the development of culturally and linguistically tailored community-based mitigation strategies can be developed that are aimed at improving the preparedness of these groups for the next emerging infectious disease, such as COVID-19.
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During the early months of the COVID-19 pandemic in 2020, the majority of the identified COVID-19 patients in Chennai, a southern metropolitan city of India, presented as asymptomatic or with mild clinical illness. Providing facility-based care for these patients was not feasible in an overburdened health system. Thus, providing home-based clinical care for patients who were asymptomatic or with mild clinical illnesses was a viable solution. Because of the imminent possibility of worsening clinical conditions in home-isolated COVID-19 patients, continuous monitoring for red flag signs was essential. With growing evidence of the effectiveness of remote monitoring of patients, the Greater Chennai Corporation in partnership with the National Institute of Epidemiology conceptualized and implemented a remote monitoring program for home-isolated COVID-19 patients. The key steps used to develop the program were to (1) decentralize triage systems and establish a home-isolation protocol, (2) develop a remote monitoring platform and remote health care workforce, and (3) onboard patients and conduct remote hybrid monitoring. In this article, we share the pragmatic solutions, critical components of the systems and processes, lessons, and experiences in implementing a remote monitoring program for home-isolated COVID-19 patients in a large metropolitan setting.
As an important element in the regional containment of the COVID-19 pandemic a PCR testing laboratory with a cooperative character was founded in spring 2021 to screen for SARS-CoV-2 in the Nuremberg region, Germany. The aim was to detect asymptomatic infections in day care facilities for children, schools, and companies. The laboratory used an established RT-PCR protocol and analyzed approximately 18,500 pools of up to 25 pooled samples each from gargles or swabs ("lollipops") from up to 135 facilities between July 2021 and June 2022. Usually, the participating facilities were informed about positive pools within a few hours. Retention samples from positive pools were usually analyzed on the same day, and the results were reported to the facilities as well as the German Electronic Reporting and Information System (DEMIS). In the laboratory results, both the local incidences and the transition from the Delta- to the Omicron surge in early 2022 were well reflected. It is plausible that about 4,800 secondary infections could be prevented from the approximately 1,570 positive individual samples detected in conjunction with appropriate isolation measures. Such a PCR laboratory, which is characterized by short response times and high flexibility, can thus provide valuable services for regional surveillance of infection incidence.
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The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a highly transmissible virus with a likely animal origin, has posed major and unprecedented challenges to millions of lives across the affected nations of the world. This outbreak first occurred in China, and despite massive regional and global attempts shortly thereafter, it spread to other countries and caused millions of deaths worldwide. This review presents key information about the characteristics of SARS-CoV-2 and its associated disease (namely, coronavirus disease 2019) and briefly discusses the origin of the virus. Herein, we also briefly summarize the strategies used against viral spread and transmission.
Background Viral persistence is a crucial factor that influences the transmissibility of SARS-CoV-2. However, the impacts of vaccination and physiological variables on viral persistence have not been adequately clarified. Methods We collected the clinical records of 377 COVID-19 patients, which contained unvaccinated patients and patients received two doses of an inactivated vaccine or an mRNA vaccine. The impacts of vaccination on disease severity and viral persistence and the correlations between 49 laboratory variables and viral persistence were analyzed separately. Finally, we established a multivariate regression model to predict the persistence of viral RNA. Results Both inactivated and mRNA vaccines significantly reduced the rate of moderate cases, while the vaccine related shortening of viral RNA persistence was only observed in moderate patients. Correlation analysis showed that 10 significant laboratory variables were shared by the unvaccinated mild patients and mild patients inoculated with an inactivated vaccine, but not by the mild patients inoculated with an mRNA vaccine. A multivariate regression model established based on the variables correlating with viral persistence in unvaccinated mild patients could predict the persistence of viral RNA for all patients except three moderate patients inoculated with an mRNA vaccine. Conclusion Vaccination contributed limitedly to the clearance of viral RNA in COVID-19 patients. While, laboratory variables in early infection could predict the persistence of viral RNA.
With continuing emergence of new SARS-CoV-2 variants, understanding the proportion of the population protected against infection is crucial for public health risk assessment and decision-making and so that the general public can take preventive measures. We aimed to estimate the protection against symptomatic illness caused by SARS-CoV-2 Omicron variants BA.4 and BA.5 elicited by vaccination against and natural infection with other SARS-CoV-2 Omicron subvariants. We used a logistic model to define the protection rate against symptomatic infection caused by BA.1 and BA.2 as a function of neutralizing antibody titer values. Applying the quantified relationships to BA.4 and BA.5 using two different methods, the estimated protection rate against BA.4 and BA.5 was 11.3% (95% confidence interval [CI]: 0.01–25.4) (method 1) and 12.9% (95% CI: 8.8–18.0) (method 2) at 6 months after a second dose of BNT162b2 vaccine, 44.3% (95% CI: 20.0–59.3) (method 1) and 47.3% (95% CI: 34.1–60.6) (method 2) at 2 weeks after a third BNT162b2 dose, and 52.3% (95% CI: 25.1–69.2) (method 1) and 54.9% (95% CI: 37.6–71.4) (method 2) during the convalescent phase after infection with BA.1 and BA.2, respectively. Our study indicates that the protection rate against BA.4 and BA.5 are significantly lower compared with those against previous variants and may lead to substantial morbidity, and overall estimates were consistent with empirical reports. Our simple yet practical models enable prompt assessment of public health impacts posed by new SARS-CoV-2 variants using small sample-size neutralization titer data to support public health decisions in urgent situations.
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Background Asymptomatic SARS-CoV-2 infection occurring in RT-PCR negative individuals represent a poorly characterized cohort with important infection control connotations. While household and community-based studies have evaluated seroprevalence of antibody and transmission dynamics in this group, workplace-based data is currently unavailable. Methods A cohort study was carried out in July 2021, during and immediately following the peak of the 3 rd wave of COVID-19 in Sri Lanka, prior to mass vaccination. A total of 92 unvaccinated individuals between the ages of 17–65 years were purposively sampled from an office and two factory settings. The selected cohort that had been exposed to RT-PCR positive cases in the workplace was tested RT-PCR negative. Serological samples collected six weeks post exposure were tested for anti-SARS-CoV-2 neutralizing antibody. Results The seroprevalence for SARS-CoV-2 specific neutralizing antibodies in the overall cohort was 63.04% (58/92). Seroprevalences in the office setting, factory setting 1 and factory setting 2 were 69.2% (9/13), 55.7% (34/61) and 83.33% (15/18), respectively. Primary risk factor associated with seropositivity was face to face contact with no mask for > 15 min ( p < 0.024, Odds Ratio (OR); 5.58, 95%CI;1.292– 25.65). Individuals with workspace exposure had significantly higher levels of neutralizing antibodies than those who did not (percentage neutralization in assay 63.3% (SD:21)vs 45.7% (SD:20), p = 0.0042), as did individuals who engaged socially without protective measures (62.4 (SD:21.6)% vs 49.7 (SD:21)%, p = 0.026). Conclusion There was a high seroprevalence for SARS-CoV-2 specific neutralizing antibodies among RT-PCR negative contacts in workplace settings in Sri Lanka. Higher levels of transmission of SARS-CoV-2 infection than estimated based on RT-PCR positive contact data indicate need for targeted infection control measures in these settings during future outbreaks.
This paper investigates the intellectual structure of the literature addressing “epidemic/pandemic” and “aviation industry” through a bibliometric approach to the literature from 1991 to 2021. The final count of 856 publications was collected from Web of Science and analyzed by CiteSpace (version 5.8.R1) and VOS Viewer. Visualization tools are used to perform the co-citation, co-occurrence, and thematic-based cluster analysis. The results highlight the most prominent nodes (articles, authors, journals, countries, and institutions) within the literature on “epidemic/pandemic” and “aviation industry.” Furthermore, this study conceptualizes and compares the growth of literature before theCOVID-19 pandemic and during the COVID-19 (“hotspot”) era. The conclusion is that the aviation industry is an engine for global economics on the road to recovery from COVID-19, in which soft (human) resources can play an integral part.
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Summary Background Viral load kinetics and duration of viral shedding are important determinants for disease transmission. We aimed to characterise viral load dynamics, duration of viral RNA shedding, and viable virus shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in various body fluids, and to compare SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV) viral dynamics. Methods In this systematic review and meta-analysis, we searched databases, including MEDLINE, Embase, Europe PubMed Central, medRxiv, and bioRxiv, and the grey literature, for research articles published between Jan 1, 2003, and June 6, 2020. We included case series (with five or more participants), cohort studies, and randomised controlled trials that reported SARS-CoV-2, SARS-CoV, or MERS-CoV infection, and reported viral load kinetics, duration of viral shedding, or viable virus. Two authors independently extracted data from published studies, or contacted authors to request data, and assessed study quality and risk of bias using the Joanna Briggs Institute Critical Appraisal Checklist tools. We calculated the mean duration of viral shedding and 95% CIs for every study included and applied the random-effects model to estimate a pooled effect size. We used a weighted meta-regression with an unrestricted maximum likelihood model to assess the effect of potential moderators on the pooled effect size. This study is registered with PROSPERO, CRD42020181914. Findings 79 studies (5340 individuals) on SARS-CoV-2, eight studies (1858 individuals) on SARS-CoV, and 11 studies (799 individuals) on MERS-CoV were included. Mean duration of SARS-CoV-2 RNA shedding was 17·0 days (95% CI 15·5–18·6; 43 studies, 3229 individuals) in upper respiratory tract, 14·6 days (9·3–20·0; seven studies, 260 individuals) in lower respiratory tract, 17·2 days (14·4–20·1; 13 studies, 586 individuals) in stool, and 16·6 days (3·6–29·7; two studies, 108 individuals) in serum samples. Maximum shedding duration was 83 days in the upper respiratory tract, 59 days in the lower respiratory tract, 126 days in stools, and 60 days in serum. Pooled mean SARS-CoV-2 shedding duration was positively associated with age (slope 0·304 [95% CI 0·115–0·493]; p=0·0016). No study detected live virus beyond day 9 of illness, despite persistently high viral loads, which were inferred from cycle threshold values. SARS-CoV-2 viral load in the upper respiratory tract appeared to peak in the first week of illness, whereas that of SARS-CoV peaked at days 10–14 and that of MERS-CoV peaked at days 7–10. Interpretation Although SARS-CoV-2 RNA shedding in respiratory and stool samples can be prolonged, duration of viable virus is relatively short-lived. SARS-CoV-2 titres in the upper respiratory tract peak in the first week of illness. Early case finding and isolation, and public education on the spectrum of illness and period of infectiousness are key to the effective containment of SARS-CoV-2.
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Background: There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? (3) What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection or presymptomatic? Methods and findings: We searched PubMed, Embase, bioRxiv, and medRxiv using a database of SARS-CoV-2 literature that is updated daily, on 25 March 2020, 20 April 2020, and 10 June 2020. Studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up or modelling studies were included. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with an adapted checklist for case series, and the relevance and credibility of modelling studies were assessed using a published checklist. We included a total of 94 studies. The overall estimate of the proportion of people who become infected with SARS-CoV-2 and remain asymptomatic throughout infection was 20% (95% confidence interval [CI] 17-25) with a prediction interval of 3%-67% in 79 studies that addressed this review question. There was some evidence that biases in the selection of participants influence the estimate. In seven studies of defined populations screened for SARS-CoV-2 and then followed, 31% (95% CI 26%-37%, prediction interval 24%-38%) remained asymptomatic. The proportion of people that is presymptomatic could not be summarised, owing to heterogeneity. The secondary attack rate was lower in contacts of people with asymptomatic infection than those with symptomatic infection (relative risk 0.35, 95% CI 0.10-1.27). Modelling studies fit to data found a higher proportion of all SARS-CoV-2 infections resulting from transmission from presymptomatic individuals than from asymptomatic individuals. Limitations of the review include that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infections and were at risk of selection biases; we did not consider the possible impact of false negative RT-PCR results, which would underestimate the proportion of asymptomatic infections; and the database does not include all sources. Conclusions: The findings of this living systematic review suggest that most people who become infected with SARS-CoV-2 will not remain asymptomatic throughout the course of the infection. The contribution of presymptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention measures, with enhanced hand hygiene, masks, testing tracing, and isolation strategies and social distancing, will continue to be needed.
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Background Reports suggest that asymptomatic individuals (those with no symptoms at all throughout the infection) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are infectious, but the extent of asymptomatic transmission requires further understanding. Purpose This living review aims to critically appraise available data about secondary attack rates from people with asymptomatic and pre-symptomatic SARS-CoV-2 infection. Data sources Medline, EMBASE, China Academic Journals full-text database (CNKI), and pre-print servers were searched from 30 December 2019 to 3 July 2020 using relevant MESH terms. Study selection Studies that report on contact tracing of index cases with asymptomatic or pre-symptomatic SARS-CoV-2 infection, in either English or Chinese were included. Data extraction Two authors independently extracted data and assessed study quality and risk of bias. We calculated the secondary attack rate as the number of contacts with SARS-CoV-2, divided by the number of contacts tested. Data synthesis Of 928 studies identified, 19 were included. Secondary attack rates from asymptomatic index cases ranged from 0% to 2.8% (9 studies). Pre-symptomatic secondary attack rates ranged from 0.7% to 31.8% (10 studies). The highest secondary attack rates were found in contacts who lived in the same household as the index case. Other activities associated with transmission were group activities such as sharing meals or playing board games with the index case. Limitations We excluded some studies because the index case or number of contacts were unclear. Owing to the anticipated heterogeneity, we did not produce a summary estimate of the included studies. Conclusion Asymptomatic patients can transmit SARS-CoV-2 to others, but our findings indicate that such individuals are responsible for fewer secondary infections than people with symptoms in the same studies. Systematic review registration PROSPERO CRD42020188168
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SARS-CoV-2-related mortality and hospitalizations differ substantially between New York City neighborhoods. Mitigation efforts require knowing the extent to which these disparities reflect differences in prevalence and understanding the associated drivers. Here, we report the prevalence of SARS-CoV-2 in New York City boroughs inferred using tests administered to 1,746 pregnant women hospitalized for delivery between March 22nd and May 3rd, 2020. We also assess the relationship between prevalence and commuting-style movements into and out of each borough. Prevalence ranged from 11.3% (95% credible interval [8.9%, 13.9%]) in Manhattan to 26.0% (15.3%, 38.9%) in South Queens, with an estimated city-wide prevalence of 15.6% (13.9%, 17.4%). Prevalence was lowest in boroughs with the greatest reductions in morning movements out of and evening movements into the borough (Pearson R = -0.88 [-0.52, -0.99]). Widespread testing is needed to further specify disparities in prevalence and assess the risk of future outbreaks.
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Superspreading events (SSEs) have characterized previous epidemics of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) infections1–6. For SARS-CoV-2, the degree to which SSEs are involved in transmission remains unclear, but there is growing evidence that SSEs might be a typical feature of COVID-197,8. Using contact tracing data from 1,038 SARS-CoV-2 cases confirmed between 23 January and 28 April 2020 in Hong Kong, we identified and characterized all local clusters of infection. We identified 4–7 SSEs across 51 clusters (n = 309 cases) and estimated that 19% (95% confidence interval, 15–24%) of cases seeded 80% of all local transmission. Transmission in social settings was associated with more secondary cases than households when controlling for age (P = 0.002). Decreasing the delay between symptom onset and case confirmation did not result in fewer secondary cases (P = 0.98), although the odds that an individual being quarantined as a contact interrupted transmission was 14.4 (95% CI, 1.9–107.2). Public health authorities should focus on rapidly tracing and quarantining contacts, along with implementing restrictions targeting social settings to reduce the risk of SSEs and suppress SARS-CoV-2 transmission.
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Background: Care homes are experiencing large outbreaks of COVID-19 associated with high case-fatality rates. We conducted detailed investigations in six London care homes reporting suspected COVID-19 outbreaks during April 2020. Methods: Residents and staff had nasal swabs for SARS CoV-2 testing using RT-PCR and were followed-up for 14 days. They were categorized as symptomatic, post-symptomatic or pre-symptomatic if they had symptoms at the time of testing, in the two weeks before or two weeks after testing, respectively, or asymptomatic throughout. Virus isolation and whole genome sequencing (WGS) was also performed. Findings: Across the six care homes, 105/264 (39.8%) residents were SARS CoV-2 positive, including 28 (26.7%) symptomatic, 10 (9.5%) post-symptomatic, 21 (20.0%) pre-symptomatic and 46 (43.8%) who remained asymptomatic. Case-fatality at 14-day follow-up was highest among symptomatic SARS-CoV-2 positive residents (10/28, 35.7%) compared to asymptomatic (2/4, 4.2%), post-symptomatic (2/10, 20.0%) or pre-symptomatic (3/21,14.3%) residents. Among staff, 53/254 (20.9%) were SARS-CoV-2 positive and 26/53 (49.1%) remained asymptomatic. RT-PCR cycle-thresholds and live-virus recovery were similar between symptomatic/asymptomatic residents/staff. Higher RT-PCR cycle threshold values (lower virus load) samples were associated with exponentially decreasing ability to recover infectious virus (P<0.001). WGS identified multiple (up to 9) separate introductions of different SARS-CoV-2 strains into individual care homes. Interpretation: A high prevalence of SARS-CoV-2 positivity was found in care homes residents and staff, half of whom were asymptomatic and potential reservoirs for on-going transmission. A third of symptomatic SARS-CoV-2 residents died within 14 days. Symptom-based screening alone is not sufficient for outbreak control. Funding: None.
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Objectives To investigate factors associated with adherence to self-isolation and lockdown measures due to COVID-19 in the UK. Study design Online cross-sectional survey. Methods Data were collected between 6th and 7th May 2020. A total of 2240 participants living in the UK aged 18 years or older were recruited from YouGov's online research panel. Results A total of 217 people (9.7%) reported that they or someone in their household had symptoms of COVID-19 (cough or high temperature/fever) in the last 7 days. Of these people, 75.1% had left the home in the last 24 h (defined as non-adherent). Men were more likely to be non-adherent, as were people who were less worried about COVID-19, and who perceived a smaller risk of catching COVID-19. Adherence was associated with having received help from someone outside your household. Results should be taken with caution as there was no evidence for associations when controlling for multiple analyses. Of people reporting no symptoms in the household, 24.5% had gone out shopping for non-essentials in the last week (defined as non-adherent). Factors associated with non-adherence and with a higher total number of outings in the last week included decreased perceived effectiveness of government ‘lockdown’ measures, decreased perceived severity of COVID-19 and decreased estimates of how many other people were following lockdown rules. Having received help was associated with better adherence. Conclusions Adherence to self-isolation is poor. As we move into a new phase of contact tracing and self-isolation, it is essential that adherence is improved. Communications should aim to increase knowledge about actions to take when symptomatic or if you have been in contact with a possible COVID-19 case. They should also emphasise the risk of catching and spreading COVID-19 when out and about and the effectiveness of preventative measures. Using volunteer networks effectively to support people in isolation may promote adherence.
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Objectives To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond. Design We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined ‘high’ risk counties as those above the 75th percentile. This threshold can be changed using the online tool. Setting US counties. Participants Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings. Results Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour. Conclusion Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.
Background: There is limited information on the effect of age on the transmission of SARS-CoV-2 infection in different settings, including primary, secondary and high schools, households, and the whole community. We undertook a literature review of published studies/data on detection of SARSCoV-2 infection in contacts of COVID-19 cases, as well as serological studies, and studies of infections in the school setting to examine those issues. Results: Our literature review presents evidence for significantly lower susceptibility to infection for children aged under 10 years compared to adults given the same exposure, for elevated susceptibility to infection in adults aged over 60y compared to younger/middle aged adults, and for the risk of SARS-CoV-2 infection associated with sleeping close to an infected individual. Published serological studies also suggest that younger adults (particularly those aged under 35y) often have high cumulative rates of SARS-CoV-2 infection in the community. Additionally, there is some evidence of robust spread of SARS-CoV-2 in secondary/high schools, and there appears to be more limited spread in primary schools. Some countries with relatively large class sizes in primary schools (e.g. Chile and Israel) reported sizeable outbreaks in some of those schools, though routes of transmission of infection to both students and staff are not clear from current reports. Conclusions: Opening secondary/high schools is likely to contribute to the spread of SARS-CoV-2, and, if implemented, it should require both lower levels of community transmission and greater safeguards to reduce transmission. Compared to secondary/high schools, opening primary schools and daycare facilities may have a more limited effect on the spread of SARS-CoV-2 in the community, particularly under smaller class sizes and in the presence of mitigation measures. Efforts to avoid crowding in the classroom and other mitigation measures should be implemented, to the extent possible, when opening primary schools. Efforts should be undertaken to diminish the mixing in younger adults to mitigate the spread of the epidemic in the whole community. The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.