SARS-CoV-2 transmission dynamics should inform policy

Abstract

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.

Full text

ViewpointS
VIEWPOINTS • CID 2020:XX (XX XXXX) • 1
Clinical Infectious Diseases
Received 11 June 2020; editorial decision 15 September 2020; published online 23 Septembe r
2020.
Correspondence: M. Cevik, Division of Infection and Global Health Research, School of
Medicine, University of St Andrews, Fife, KY16 9TF UK (mc349@st-andrews.ac.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: journals.permissions@oup.com.
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.
FACTORS INFLUENCING TRANSMISSION DYNAMICS
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Pattern
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 [1–3]. 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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2 • CID 2020:XX (XX XXXX) • VIEWPOINTS
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, 4–8]. 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
transmission.
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 [14–17]. 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 [19–21]. In an investigation of 17
nursing homes that implemented voluntary sta connement
with residents, including 794 sta members and 1250 residents
Environment
Host
factors
Socio-economic
factors
Contact
paern
Proximity to index cas
e
Time of contact
Duraon of exposure
Contact frequency
Acvity
Indoor/Outdoor
Venlaon
Long term care facilies
Age
Infecousness
Severity of illness
Host defence factors
Poverty
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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VIEWPOINTS • CID 2020:XX (XX XXXX) • 3
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.
HostFactors
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,
https://biorender.com 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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4 • CID 2020:XX (XX XXXX) • VIEWPOINTS
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].
Environment
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, 35–37]. 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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VIEWPOINTS • CID 2020:XX (XX XXXX) • 5
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.
RECOMMENDATIONS
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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6 • CID 2020:XX (XX XXXX) • VIEWPOINTS
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.
Notes
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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