>> Welcome to Module 2.1,
SARS-CoV-2 Sequencing in Arizona.
This presentation is a part
of the COVID-19 Genomic Epidemiology Toolkit
from CDC's Office of Advanced Molecular Detection.
My name is Hayley Yaglom and I work
at the Translational Genomics Research Institute in Arizona
as a genomic epidemiologist.
This toolkit is the first of three case studies
that will provide insight
into how whole genome sequencing can be used
as an investigative tool in outbreak settings.
Be sure to check out the toolkit's other modules,
which include an introduction to genomic sequencing
and training materials to help you
and your team start using genome sequence data
to supplement your epidemiologic investigations.
TGen North in Flagstaff is home to our Pathogen
and Microbiome Division, where we have a 13-year history
of applying genomics to clinical medicine and public health.
Before joining TGen North,
I was an infectious disease epidemiologist
at the Arizona Department of Health Services,
where shoe-leather disease surveillance was part
of my everyday job.
In this module, I will be sharing our use
of sequencing SARS-CoV-2
to understand the genomic epidemiology
of COVID-19 in Arizona.
To visualize our story, we are using Nextstrain,
which is an online, interactive platform used to display
and track pathogen genome data.
The circles shown here on this map indicate the country
or state of origin for the SARS-CoV-2 genomes we have used
in our analyses.
They are only a subset of all available genomes
from around the world.
North American genomes are highlighted
in red, as you can see.
And since we are mostly concerned with Arizona cases,
the circle representing Arizona genomes is the largest.
Like other states, Arizona has reported many COVID-19 cases --
more than 450,000 -- since the beginning of the pandemic.
We have also sequenced and analyzed more
than 5,000 SARS-CoV-2 genomes,
making Arizona's sequencing effort one
of the most extensive in the nation.
Sequence data help us map transmission of the virus within
and among Arizona communities and study mutations
for their potential to cause increased transmission
or virulence.
Genomic information can alert public health
to super-spreader events, hot spots of local spread,
and clusters of disease.
It can also help identify introductions
of new SARS-CoV-2 strains into the region and point
to possible sources of infection.
By comparing SARS-CoV-2 genomes sequenced in Arizona to others
from around the world, we have learned a lot
about COVID-19 in Arizona.
First, the virus came into the state
from several different places, as demonstrated
by this phylogenetic tree, which shows how different strains
of the virus from around the world are related,
much like a family tree.
The colored dots you see -- called "leaf nodes" --
represent sequences from the fifteen counties in Arizona.
The white and gray dots represent other sequences
obtained from around the world.
The strains that are now circulating
in Arizona are closely related to strains
from many other countries and US states.
Let's take a closer look at some examples in Nextstrain
by clicking on the box in the top right.
Our current view is displaying samples over time
by their collection date, which is shown here on the X-axis.
I use this view for navigation purposes, and it is helpful
when we want to look at the evolution
of pathogens over time.
You can also explore the data in Nextstrain.
In this view, you can search for specific samples
or strains using the box on the left-hand side of the screen.
If you want to search for samples from Germany,
for example, I type in the country name
and you can see a few
of the darker gray leaf nodes are highlighted.
Now I'm going to zoom in on the top portion of the tree.
As I pass over the leaf nodes, which represent viral genomes
from COVID-19 cases, metadata for each case are displayed.
In bioinformatics, any data other
than genomic data are typically referred to as metadata.
The metadata shown here include collection date
and location of original sample.
I'm now going to switch to the divergence view
of the phylogenetic tree.
In this view, in contrast to the time view,
the X-axis indicates the number of mutations
by which individual sequences differ from each other.
I will point out that Arizona sequences are related to others
from Washington, California, New York, and Louisiana.
In fact, some Arizona sequences (the ones
with the colored leaf nodes on this main branch
at the top) are identical to others collected in April
from Louisiana, providing evidence of the spread
of SARS-CoV-2 after Mardi Gras.
By analyzing our phylogenetic trees closely, we can infer
that the virus was introduced into Arizona
at least twelve times during the early part of the pandemic,
and that the first introductions resulting
in community transmission occurred
in mid- to late February.
When I reset the layout to view the entire tree,
you can see that several lineages are present
in our Arizona samples.
In some areas of the tree, we see a limited number
of introductions into these counties (the yellow, blue,
orange sections), followed by community transmission.
In the lower portion of the tree,
where there are multiple colors, it is more likely
that multiple introductions occurred,
mostly via domestic travel.
Lastly, at the very bottom of the tree, you can see samples
from Wuhan, China, highlighted in red.
The next image is going to zoom
in on this section of the global tree.
One of the very first cases of COVID-19
in the US was identified in Arizona on January 22,
when a 26-year-old man presented to a health clinic,
reporting a two-day history of nonproductive cough.
He had recently returned from Wuhan City, China,
and reported having contact with a person with cough
and fever while traveling.
Rapid public health response efforts, including quarantine,
testing, and contact tracing,
helped to prevent further transmission
from this initial case.
Genomic analysis shows this virus strain (highlighted
in blue) is closely related to others from Asia,
pointing to its origin.
In the background, you can see the faint red lines
from the previous slide.
The most important result of this analysis is
that we don't see any more viruses in Arizona related
to this one, supporting the epidemiologic evidence
that spread from this case was stopped.
This example shows how sequencing, used in tandem
with contact tracing and epidemiologic information,
can document successful public health interventions.
On March 26, just 3 weeks
after community transmission had been identified
by public health investigators, the Arizona Department
of Health Services declared COVID-19 to be widespread,
with 12 of the 15 counties in the state reporting cases.
This map shows that by that date, we had sequenced genomes
from COVID-19 cases from all 12 counties,
allowing us to use genomic information
to help tell the story
and retrospectively assess the impact
of early public health efforts.
Now I am going to dive into a few Arizona stories
that demonstrate how rapid genomic analyses can provide a
better understanding of pathogen transmission patterns
and help guide public health response efforts.
In mid-May, local public health officials responded
to an outbreak of COVID-19 among staff and residents
of a multi-residential group home setting that provides care
for adults with intellectual and developmental disabilities.
Samples were collected at 16 of the homes.
In four of the homes, all residents tested positive,
and in the remaining 12 houses where samples were collected,
all residents tested negative.
This tree displays in yellow the genomes that we were able
to sequence from this outbreak.
It shows that they are closely related to each other,
which you can see here by the very small numbers
of mutations separating them.
In fact, the 11 samples on this main branch are identical
to each other.
These genomic data correlate with epidemiologic information
and suggest that an infected staff member introduced
SARS-CoV-2 into this setting.
Staff typically interacted with residents from multiple homes
until SARS-CoV-2 was detected, when management responded
by restricting movement of staff between homes
to care for residents.
Through our sequencing of samples collected
from COVID-19 cases in the same region of Arizona,
we learned that this outbreak was not limited
to the group home and that some community transmission
of this strain also occurred.
The enhanced precautions and infection control measures taken
by staff and timely intervention
by public health officials likely curbed this outbreak.
We have also been working closely with one
of Arizona's Native American communities,
which was hit hard by COVID-19.
This tribal community has experienced high infection rates
but very low mortality, due in part to successful,
on-the-ground interventions by public health staff
and community health workers, including rapid contact tracing,
testing, and prompt clinical management.
Early on, evidence pointed to four independent clusters
of COVID-19 occurring in this tribal community; however,
our genomic analyses showed that cases associated
with these clusters stemmed from a single transmission chain.
The tribe's index case was a healthcare worker
who had recently traveled to Phoenix and experienced an onset
of fever and cough on March 25 but continued to work.
Genomic analysis shows that this case seeded an outbreak
on this reservation, with cases comprising the clade colored
in pink.
This clade, or group of closely related strains,
is defined by an amino acid change
in the spike protein H245Y.
A second clade, colored purple, contains another group of cases
in this tribal community;
sequences in this group are defined
by a second unique mutation, R209I.
Let's explore this pink subclade further.
I am now highlighting the earliest sample,
collected from the index case on March 29.
All cases identified in this tribal community in April,
and most of those identified in May, make up this clade,
demonstrating the rapid spread of a single clone.
This figure summarizes data
from the epidemiologic investigation of this outbreak.
The index case, labeled A1, had five household contacts,
all of whom became symptomatic
and tested positive for SARS-CoV-2.
More extensive contact tracing was conducted,
finding two coworkers living in households C and E;
both developed mild symptoms and tested positive.
In an attempt to protect them from infection by adults
in their household, three asymptomatic children
from household C were sent to live in household D. In time,
the three children from household C
and the eight members of household D all tested positive
for SARS-CoV-2, suggesting
that asymptomatic children had spread infection
to household D. This turned out to be a common occurrence,
providing valuable public health insight
into the outbreak dynamics in this unique community.
In fact, frequent interactions amongst community
and family members living near each other likely played a role
in the widespread transmission
of the virus later observed in this community.
Results of this analysis were crucial for understanding
that continuous travel off the reservation was not responsible
for spreading SARS-CoV-2 in this area.
Instead, it focused attention on working
with the tribal community to interrupt transmission.
The phylogenetic analysis of this cluster correlates well
with the series of events uncovered
in the epidemiological investigation I just presented.
Although isolation efforts were successful in limiting spread
within and among these households,
our genomic analysis also revealed
that the initial case gave rise to a larger outbreak
in the tribal community.
This indicates that despite rapid, efficient contact tracing
and isolation directives from public health officials,
community transmission was not stopped.
I want to emphasize that our genomic data were not complete,
as only some of the known cases were available for sequencing,
and the known cases probably represent only a fraction
of all infections that occurred.
These stories from Arizona highlight the value
of genomic epidemiology in public health practice.
We are fortunate to have so many Arizona genomes, as seen in red
on this tree, to help us gain a more detailed picture
of COVID-19 in our state.
Although we can tell so much more of the epidemiologic story
with genomics, it is important to point out some limitations.
First, as I mentioned in the last example,
we need to remember that we are only subsampling the virus
population by analyzing the sequence data
from a subset of infections.
Other cases not included
in our genomic analyses could have played an important role
in transmission.
Second, just because two samples are identical doesn't mean
that there weren't intermediate transmission events (such
as other people) connecting two cases.
Genomic and epidemiologic information must be considered
together when interpreting findings,
formalizing conclusions, and implementing actions.
As a former epidemiologist with the Arizona Department
of Health Services, I have truly learned the value
of these new technologies
and how they can significantly advance the state
of infectious disease surveillance
and public health response.
Beyond COVID-19, genomic surveillance has the potential
to identify infectious disease clusters with greater speed
and accuracy and at earlier time points to help trigger
and guide a more informed
and effective public health response.
The link to Arizona's full Nextstain narrative,
as well as the condensed version I presented here,
will be available
in the COVID-19 Genomic Epidemiology Toolkit.
As you explore the data with your team,
I hope you will find ways to include genomic epidemiology
in your public health practice and maybe even become
as passionate about it as I have.
This concludes module 2.1.
The next module will review two separate outbreaks
at long-term care settings,
and how sequencing helped clarify the pattern
of transmission in these settings.
Please visit the COVID-19 Genomic Epidemiology Toolkit
page where you can find further reading on this topic.
On the toolkit page, you can also subscribe
to our mailing list and receive announcements as new modules
and materials are released.
Thank you for watching.