>> Welcome to Module 1.1, 'What is genomic epidemiology?'
This presentation is a part
of the COVID-19 Genomic Epidemiology Toolkit
from CDC's Office of Advanced Molecular Detection.
My name is Nancy Chow, and I am a Bioinformatics
and Informatics Lead with CDC.
This module is part of our introduction
to genomic epidemiology with specific reference
to the genome of SARS-CoV-2.
Be sure to check out the toolkit's other modules,
which include a combination of case studies
and training materials, to help you start using genome sequence
data to supplement your epidemiologic investigations.
To introduce genomic epidemiology we can start
with the epidemiology component.
As a public health professional,
you know that epidemiology is the study of the distribution
and determinants of disease in a population and its application
to control health problems.
In genomic epidemiology, genomic data is used to aid
in epidemiologic investigations.
In the context of infectious diseases,
public health professionals can use a pathogen's genomic data
to better define the distribution and spread
of an infectious disease
and to apply this information to control it.
In the case of COVID-19, we can sequence the virus, SARS-CoV-2,
and use that genomic data for two main purposes.
First, we can monitor trends at the national level
in the emergence of important new strains of the virus,
for example, after interventions like vaccination.
Second, we can use genomic data
to better understand epidemiology at the local level.
Genomic data may provide evidence for
or against transmission in potential clusters identified
in settings like healthcare facilities, workplaces,
or bars and restaurants.
The data may also reveal other unsuspected clusters
that warrant investigation.
The Washington Post published a useful article in October 2020
that described how SARS-CoV-2 genome sequence data could be
used for epidemiologic investigations.
The article describes a fictional community with homes,
schools, bars and plumber working at various households.
Different sub-strains of SARS-CoV-2 --
defined by their genome sequences and labeled A, B,
and C -- are circulating in this community.
So, for example, sequencing the virus collected
from this person can tell us they have sub-strain A.
With this technology public health investigators can
identify hotspots of transmission.
For example, in this cluster of cases circled here,
all have the same sub-strain, which provides evidence
for localized transmission.
Public health investigators can also use genome sequence data
to rule out transmission within an apparent cluster.
So in this example circled here, we see a school
where different sub-strains
of SARS-CoV-2 have been found among ill children.
This finding suggests that children are not being infected
at school but are rather acquiring SARS-CoV-2
in the community.
Another powerful use of genome sequence data is
to supplement contact tracing.
In this community, a plumber, shown here,
is known to have been in contact with members
of various households that they service.
Sequencing SARS-CoV-2 samples collected from the plumber
and household members will show whether all
of them have acquired the same sub-strain of SARS-CoV-2.
Finding that they all have sub-strain A is evidence
to support epidemiologic contact tracing data suggesting
that the plumber may have spread SARS-CoV-2 to these contacts.
Additional uses of this technology are
to identify new introductions into a population
and provide evidence for the source of the introduction.
The data can be used to identify superspreading events
and by identifying potentially infectious individuals
through contact tracing can limit new viral transmission.
Sequence data can also be used as inputs for modeling studies
that predict severity and size of future outbreak seasons.
Now let's talk about sequencing and what exactly it does.
It goes back to the fact that viruses mutate as they spread.
So, first, sequencing provides a genomic fingerprint,
if you will, of a particular virus.
As the virus spreads and mutates, scientists can pick
up these mutations by sequencing and looking
at the genomic fingerprints.
Scientists can then compare these genomic fingerprints
for various cases in a population of interest
and see how the cases are related.
Whole-genome sequencing or WGS is used
to detect these genomic fingerprints.
This is a form of high-throughput sequencing,
which means we get sequences
on a massive scale using the latest technology.
Step one of this process a patient's sample is collected.
Step two, genomic material, either DNA or RNA,
will be extracted from the sample.
This is often referred to as nucleic acid extraction.
Step three, the nucleic acid will be prepared for WGS
in a process called library preparation.
Step four, this library will be sequenced using a
sequencing platform.
And finally, step five, produces the raw data called sequence
data and this data will be used to generate a phylogenetic tree.
Now there are two main types
of next-generation sequencing technologies:
short-read sequencing and long-read sequencing.
Short-read sequencing technology produces short reads,
which is another way of saying short DNA fragments.
The most common platform in public health laboratories,
called the MiSeq, utilizes short-read technology.
Characteristics of short-read technology include the data has
a high accuracy and it is great
for large projects involving many samples.
However, the equipment can be expensive
to operate and maintain.
In contrast, long-read sequencing technology produces
long DNA fragments.
Currently, long-read technology has a lower accuracy
than short-read technology.
The MinION, a platform that uses long-read technology,
is growing in popularity in many public health laboratories.
The actual sequencer can fit in the palm of your hand
and can be run using a laptop.
It enables scientists to generate actionable data
in a shorter timeframe.
There are lower capital costs to run MinION and it is great
for sequencing in the field or in resource-limited settings.
We also have to say that use of trade names
and commercial sources is for identification only
and does not imply endorsement by the U.S. Department
of Health and Human Services.
When thinking about genomic epidemiology, it is important
to remember that even though we do not have sequences
from every case in the epidemic process or transmission network,
we can still glean important information
to answer key epidemiologic questions.
Here, we are looking at a transmission network
where the x-axis is time.
Every red circle represents a case that arises over time.
You can think of these as all the true cases, whether
or not they are detected by the investigation.
Now, in the shaded blue circles, we are looking at the cases
for which we can acquire a genomic sequence.
We can call these cases A through F. In red are the ones
that we do not have sequences for, either because, one,
we do not know if they are cases they are undetected or, two,
we may not be able to sequence their sample
because of resources, logistics, or other factors.
Now removing the cases
in red leaves only the shaded blue cases,
the ones with genomic sequence data.
They do not give us a complete picture
of the transmission network,
but we can see how they are related in time.
Genome sequences from these cases can be represented
in a phylogenetic tree.
The resulting phylogenetic tree begins to infer relatedness
by analyzing sequence similarities asking questions
about which case is more closely related to which other case.
In this example, case A and case B are more closely related
to each other than they are to case C. And cases A, B, C,
and D are more closely related to each other than to cases E
and F. The next module we'll explore
in much greater detail how
to interpret trees such as this one.
At the national level, genome sequence data are important
for monitoring the ongoing evolution of SARS-CoV-2.
This is an image from Nextstrain.org,
an open-source project created at the University of Washington.
The numbers of available sequences vary by state,
but Nextstrain can display them all
in a single phylogenetic tree.
We will be saying a lot more
about Nextstrain later in the course.
For national trends a prime example is the work performed
by the COVID-19 Genomics UK consortium also called COG-UK.
This is a consortium involving public health agencies,
universities and various sequencing centers
such as the Wellcome Sanger Institute.
Since March 2020 over a 100,000 SARS-CoV-2 genomes have been
sequenced and applied to more
than 100 public health outbreak investigations.
COG-UK has documented the disappearance
of specific viral strains following lockdown,
supporting its success as a public health intervention.
They have also described the explosion
of viral strains following relaxation of control measures.
Now sequencing can also be used
for highly localized cluster investigations.
This is another phylogenetic tree drawn
from samples collected from an outbreak
within a single facility.
This outbreak will be discussed in detail in a later module.
Two examples highlighted here include one investigation
in a Washington State skilled nursing facility
where sequencing data helped demonstrate
that widespread transmission had occurred.
It provided evidence to support that focusing
on solely symptomatic residents is not sufficient
to prevent transmission.
The other example involves US Major League Baseball
where sequencing data suggested a superspreading event involving
team members from a single team.
The sequencing data also revealed
that interactions outside the actual game played
on the field were likely the source of transmission.
This helped narrow down the list of activities to focus
on as potential promoters of transmission.
This concludes Module 1.1.
The next module will start with a very basic introduction
to the SARS-CoV-2 genome.
Please visit the COVID-19 Genomic Epidemiology Toolkit
page where you can find further reading on the 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 very much.