>> Welcome to the COVID-19 Genomic Epidemiology Toolkit
from CDC's Office of Advanced Molecular Detection.
My name is Greg Armstrong, and I will introduce our topic,
Using Genomics to Investigate the Epidemiology of COVID-19.
Then I'll explain why we developed this toolkit,
what it contains, and who should use it.
Finally, I'll preview the other modules in the toolkit,
which have been designed to help you start using genomic sequence
data to supplement your epidemiologic investigations.
There's been an explosion of interest in the past decade
in the application of microbial genomics to public health.
The widespread use of genomics
in recent years was made possible
by a technological revolution
that took place in the early 2000s.
The ability to sequence the DNA of microbes has been
around since the 1970s but for three decades was dominated
by a single method known as Sanger sequencing,
which was itself advancing at a fairly fast pace.
Starting in 2005, however, a new group of technologies,
collectively known as next-generation sequencing,
began to enter into use.
These technologies have completely transformed what is
possible to do with genomic sequencing.
It's now possible to sequence many thousands of times
as much DNA or RNA as was previously possible.
You may also hear the terms "high-throughput sequencing"
or "whole-genome sequencing."
These terms are often used interchangeably with
"next-generation sequencing."
At the time of this recording, in late 2020,
microbial genomics are widely used
in the US public health system.
In food safety, for example,
sequencing technology has replaced pulsed-field gel
electrophoresis (or PFGE) in the national PulseNet system.
With respect to antimicrobial resistance,
high-throughput sequencing can be used to infer resistance
and to better understand the dynamics of transmission.
For influenza, sequencing is now a core part
of national strain surveillance, playing an important role
in informing decisions about vaccine strain selection.
In the tuberculosis world, sequencing has replaced older,
lower-resolution technologies for identifying
and investigating transmission clusters.
For group A Strep, it is used to investigate outbreaks
in healthcare settings.
For malaria, it provides a practical tool
for monitoring resistance and other important traits,
such as mutations that can reduce the effectiveness
of rapid diagnostics.
For rabies, sequence variants are often used
to track transmission between geographically
distinct populations.
High-throughput sequencing is now in use in some manner
in almost all areas
of infectious disease public health in the United States.
Genomic technology has been widely adopted
by US public health departments.
As of last year, all state public health laboratories have
high-throughput sequencing capacity.
All are using it for foodborne disease surveillance,
and many are also using it
for an increasing variety of applications.
Which brings us to COVID-19 and the virus
that causes it, SARS-CoV-2.
In the next few slides, I will review the rationale
for applying sequencing technology to this virus.
From a very broad perspective, it is useful to look
at this rationale at two levels.
At the national level,
sequencing of the virus is a core part
of strain surveillance, which looks for trends
that could have implications in diagnostics,
vaccines, or therapeutics.
At the state and local levels,
genomic data can supplement control efforts
and provide a more granular picture
of the local epidemiology.
The next few slides will describe these applications
in a bit more detail.
At the national level,
sequencing plays an important role in monitoring the emergence
of important new strains and in monitoring trends
after interventions such as vaccination.
These data will be important in supplementing epidemiological
and clinical findings.
This national surveillance is managed by CDC with support
from state and local health departments.
Genomic data are made publicly available in real time,
so that researchers in academia
and in the private sector can analyze them as well.
At the state and local level, sequencing is also useful
and can have an even more immediate impact.
In fact, it is because of this potential
for local investigation and control efforts
that CDC has developed this toolkit.
One application of sequencing is identifying clusters
of COVID-19, as highlighted in this study from one region
of the United Kingdom, where routine sequencing
of SARS-CoV-2 unexpectedly uncovered a sustained outbreak
in six healthcare facilities.
Another application is investigating clusters,
as illustrated in an MMWR article.
In this report, the Minnesota Department
of Health described investigations of outbreaks
in two skilled nursing facilities.
In each of these, the genomic data strongly suggested
that the outbreak was caused by a single introduction of virus
into the facility, which then spread among its residents
and staff.
These findings strengthened the push
to improve infection prevention and control (or IPC) practices
in these two facilities.
The findings from these investigations will be presented
in more detail in a later module in this toolkit.
Genomic data can also add granularity to,
and improve the understanding of, local epidemiology,
as highlighted in this study, which looks at the genomics
of early SARS-CoV-2 infections in the Boston area.
One of the interesting insights here is
around a superspreading event tied
to an international conference that took place in Boston.
The genomic data provided further evidence
that superspreading had occurred,
but in addition was able to show how virus
from that event had spread locally, including to
and among individuals experiencing homelessness,
then throughout the United States and even internationally.
The paper also describes investigations
of two subsequent outbreaks in a Boston acute-care facility.
In both cases, in contrast to what was described
on the previous slide, genomics showed
that the viruses involved were not closely related.
This was evidence that the cases most likely came from spread
in the community and that IPC practices had been effective
in stopping transmission within the facility.
Genomics has many other applications in the prevention
and control of COVID-19.
For example, this paper from the Nevada Public Health Laboratory
describes how genomics was used to confirm a case of reinfection
by showing that the viruses causing the two illnesses were
genetically distinct.
In this investigation, the authors went a step further:
by working with the state forensics laboratory,
they were able to confirm that both specimens were
from the same individual, thus ruling out the possibility
of specimen mislabeling.
We have designed this toolkit to help epidemiologists at state
and local levels to respond to COVID-19.
Although targeted to epidemiologists working
in infectious disease programs, the toolkit may also be of use
to laboratory scientists, bioinformaticians,
and public health administrators.
We are including additional resources
to encourage broader application of genomic epidemiology
in controlling the spread of SARS-CoV-2.
We are building the toolkit in a modular fashion,
trying to keep the modules as small and as self-contained
as possible, so that epidemiologists can choose
to use the modules most relevant to their work.
Over the next few weeks, we will be releasing additional modules
in three categories.
Part 1 introduces basic concepts in genomic epidemiology
and in the genomics of SARS-CoV-2.
Part 2 contains case studies demonstrating specific uses
of genomics in response to COVID-19.
Part 3 discusses some key tools and skills
for analyzing genomic data and integrating it
with epidemiologic or clinical data on COVID-19.
This toolkit is the product of a collaboration
with several groups, and we'd like to take a moment
to thank the Translational Genomics Research Institute
or T-Gen North, and in particular, Hayley Yaglom.
We'd also like to thank the Minnesota Department of Health,
particularly Nick Lehnertz, Ruth Lynfield, and Sean Wang.
And here at CDC, the following individuals have taken charge
of producing this toolkit: Nancy Chow, Luciana Draper,
Marta Twinn, Shatavia Morrison, and Michael Weigand.
The COVID-19 Genomic Epidemiology Toolkit team plans
to add more modules and materials regularly.
So please check back for updates.
Or better yet, subscribe to our mailing list
from the CDC Genomic Epidemiology Toolkit page.
Thank you.