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title: Genomic analysis of COVID-19. Situation report 2020-05-15.
authors:
- Sidney M. Bell
- Emma Hodcroft
- Nicola Müller
- Cassia Wagner
- James Hadfield
- Richard Neher
- Trevor Bedford
authorLinks:
- https://twitter.com/sidneymbell
- https://neherlab.org/emma-hodcroft.html
- https://bedford.io/team/nicola-mueller/
- https://bedford.io/team/cassia-wagner/
- https://bedford.io/team/james-hadfield/
- https://neherlab.org/richard-neher.html
- https://bedford.io/team/trevor-bedford/
affiliations: "Fred Hutch, Seattle, USA; Biozentrum, Basel, Switzerland; CZI, CA, USA"
translators:
translatorLinks:
license: "CC-BY"
licenseLink: "https://creativecommons.org/licenses/by/4.0/"
dataset: "https://nextstrain.org/ncov/global/2020-05-14?d=tree&l=clock&legend=closed"

abstract: "Nextstrain Situation Reports use publicly shared genomic data to track the spread and evolution of SARS-CoV-2. This week, we give an overview of viral mutations and what they mean (and don't mean) for the COVID-19 pandemic."
---
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# [Executive summary](https://nextstrain.org/ncov/2020-05-14?d=tree,entropy&p=grid)

We analyzed 5,193 publicly shared COVID-19 genomes. By comparing these viral genomes to each other, we can characterize how SARS-CoV-2 is evolving and moving around the world. This week, we focus on viral mutations. We cover:
<br><br>
* Where viral mutations come from (they're normal)
* The rate of mutation in SARS-CoV-2 (very typical)
* How many strains of SARS-CoV-2 are circulating (as far as we know: 1)
* How geography and epidemiology contribute to perceived differences in viral genotypes (this is tricky)


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# [COVID-19 Resources](https://nextstrain.org/ncov/global/2020-05-14?d=tree&p=full&legend=closed)

#### Nextstrain Resources
* [START HERE: How to read a phylogeny](https://nextstrain.org/narratives/trees-background/).
* [Previous Situation Reports](https://nextstrain.org/ncov-sit-reps/).
* [Explanatory Twitter threads](https://bedford.io/misc/twitter/).
* [Background on coronaviruses](https://nextstrain.org/help/coronavirus/human-CoV).
* [Common misconceptions](https://nextstrain.org/narratives/ncov/sit-rep/2020-03-13?n=11).

#### External Resources
* [How coronavirus mutations and spreads (NYTimes)](https://www.nytimes.com/interactive/2020/04/30/science/coronavirus-mutations.html).
* [Ask a Scientist & FAQs](https://covid19.fas.org/l/en).
* [WHO Situation Reports](https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports).
* [CDC Resources](https://www.cdc.gov/coronavirus/2019-ncov/index.html).

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```auspiceMainDisplayMarkdown
# Situation Report Hiatus
<p>
Early in the pandemic, it was unclear how SARS-CoV-2 was moving between countries, where it was circulating locally, and how localized outbreaks were related.
Over the last four months, we've tackled these questions - and more - in our weekly Situation Reports. At this stage of the pandemic, it's clear that some broad-strokes patterns are largely consistent across regions and countries:
</p>
<p/>
- Outbreaks across even distant parts of the world are deeply intertwined.<p/>
- Through human migration and travel, the virus has been introduced to most communities multiple times.<p/>
- Once these "sparks" land in a new community, many fizzle out without causing widespread transmission. Subject to local conditions and a bit of chance, some of these sparks grow into local outbreaks.<p/>
- Eventually, these local outbreaks send off sparks of their own, spreading to new locations.
<p/>
Unsurprisingly, we see this pattern in countries experiencing their first wave of infections. More alarmingly, we also see this pattern after re-introductions of the virus to countries where the initial peak passed months beforehand.
Ultimately, this pattern is only broken when a country is able to effectively test, trace, and isolate cases immediately.
<br><br>
This means that outbreaks across the world are deeply connected, and the battle against COVID-19 will always be global -- we can't conquer the virus anywhere without addressing it everywhere.
<br><br>
At this stage of the pandemic, genomic epidemiology is most urgently needed within local and hyperlocal communities through local public health offices.
<br><br>
As such, this will be our last weekly Situation Report for a while, though we aim to support similar reports from local public health officials.
<br><br>
We will, of course, continue daily updates to the global & regional builds as new sequences are made available.
When the big-picture data says something new, we'll also issue additional Situation Reports.
All of these updates will be posted to the [Nextstrain twitter account](https://twitter.com/nextstrain) (as always).
<br><br>
You can also find <a href="https://nextstrain.org/ncov-sit-reps/">all of our previous Situation Reports here</a>.
```

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# [Changes in viral genomes over time are normal](https://nextstrain.org/ncov/global/2020-05-14?c=num_date&d=tree,entropy&m=div&p=full&legend=open)

SARS-CoV-2, the virus that causes the disease COVID-19, is a [coronavirus](https://nextstrain.org/help/coronavirus/human-CoV). Like other coronaviruses, SARS-CoV-2 has a genome encoded in RNA (rather than DNA).
<br><br>
Viruses infect cells because they need to borrow cellular machinery to replicate. For RNA viruses, this process is quite error-prone, as most RNA polymerases (the molecular machines that make copies of RNA) aren't able to proofread and correct their work. This leads to frequent mutations in the viral genome; these are normal and expected.
<br><br>
Importantly, the vast majority of these mutations either "break" the virus such that it can't transmit and/or replicate anymore, or don't change the virus at all because of [redundant encoding](https://en.wikipedia.org/wiki/Synonymous_substitution).
This means that the mutations don't change the proteins, and therefore function, of the virus in any way.
Other changes may modify a protein of the virus very slightly, but not impact the function at all.
Rarely, a change in the genetic code of a virus can help it to replicate and/or transmit better, but nearly all of these changes still have only a tiny effect.

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# [Variation in viruses' genetic code is useful for tracing outbreaks](https://nextstrain.org/ncov/global/2020-05-14?c=gt-ORF3a_57&d=tree,entropy&f_division=New%20York&m=div&p=full)

These differences in the genomes between viruses can be used as "bread crumbs" to trace the history of a given sample.
Just as the people in your family share unique combinations of genetic characteristics, closely related viral samples share unique combinations of genetic mutations.
<br><br>
For example, here we show the viral "family tree" with each sample colored by which amino acid is present at a specific location in the viral genome (gene "ORF1a", site 57).
<br><br>
Highlighting samples from New York, we see that most of the cases from New York and Europe have the amino acid Histidine ("H") rather than Glutamine ("Q") at this position. This, in combination with all the other sites in the genome, tells us that these cases are all closely related to each other.
<br><br>
You can also see the position in gene "ORF3a" highlighted on the chart at the bottom of the left.
<br><br>
For a more in-depth explanation of these concepts, we recommend [this visual explanation](https://www.nytimes.com/interactive/2020/04/30/science/coronavirus-mutations.html) by Jonathan Corum and Carl Zimmer.


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# [The evolutionary rate of SARS-CoV-2 is typical for a coronavirus](https://nextstrain.org/ncov/global/2020-05-14?c=num_date&d=tree,entropy&l=clock&p=full)

Because viral genomes inevitably change, these differences tend to accumulate at a steady rate over time.
<br><br>
Here, the x axis shows the date that each sample was collected. The y axis shows the *total number* of mutations each strain is away from the root of the tree. Each sample is colored by the date it was collected.
<br><br>
While there are certainly some outliers, on average, we see changes accumulate at a rate of ~24 substitutions per year. This means that if a single viral lineage were transmitting from one person to the next for a full year, we'd expect its entire genome to accumulate roughly 24 substitutions by the end of the year.
As the whole SARS-CoV-2 genome is ~30,000 bases, this corresponds to roughly ~1 mutation per 1,000 bases in a year.
<br><br>
For context, influenza would average ~2 mutations per 1,000 bases per year; HIV would average ~4 mutations per 1,000 bases per year.

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# [As far as we know, there is only 1 strain of SARS-CoV-2](https://nextstrain.org/ncov/global/2020-05-14?branchLabel=clade&c=clade_membership&d=tree&p=full)

There have been many reports about multiple "strains" of SARS-CoV-2.
First, a clarification: when virologists use the word "strain," it's often just as a useful label so we can all refer to the same group of samples (e.g., our clade labels, shown here). This does *not* imply any known functional difference.
<br><br>
Separately, "strain" can be used to refer to viral genotypes that are functionally distinct, either biologically (e.g., pathogenicity/disease severity) and/or epidemiologically (e.g., transmissibility).
Importantly, though, determining whether two genotypes actually *are* functionally distinct requires much more experimental, clinical and epidemiological data than we currently have.
<br><br>
One of the most prominent hypotheses about SARS-CoV-2 strains compares possible "D614" vs "G614" strains.


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# [D614G may be related to transmissibility, but could also be explained by geography](https://nextstrain.org/ncov/global/2020-05-14?c=gt-S_614&gmax=25778&gmin=21082&p=full)

[A recent pre-print](https://www.biorxiv.org/content/10.1101/2020.04.29.069054v1) suggests that a single mutation, D614G (from amino acid "D" to "G" at site 614 in the "S" ('spike') protein), may be responsible for increasing the transmissibility of SARS-CoV-2.
<br><br>
In many geographic areas, the relative frequency of 614D compared to 614G increased over time.
If a certain genotype leads to better transmissibility, we would expect it's frequency to increase over time.
There are, however, other explanations to why the relative frequency of some genotypes would increase over time.
<br><br>
For this site, we see a number of geographic locations where initially, most viral lineages in circulation had the D allele; later, this balance flips such that most lineages have the G allele.
This *could* be a hallmark of G being slightly "fitter" than D (e.g., if viruses with the G mutation had a slightly higher R0 through increased transmissibility).
<br><br>
However, this could also just be a side effect of the natural history of the pandemic.
Early on in the pandemic, most strains exported from China had a D allele. Later, most strains from Italy had a G allele. Recently, we've seen more exportation from Europe than from Asia overall (although there's certainly some sampling bias at play here, as well).
Thus, it could just be that this particular genotype got lucky, and spread so rapidly because it got there first.
<br><br>
There's a fair amount of debate regarding the relative merits of these two hypotheses. It'll be important to carefully tease these apart, but we don't have scientific consensus on this issue yet. For a more detailed explanation, [see this thread](https://twitter.com/trvrb/status/1257825352660877313).


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# [What you can do](https://nextstrain.org/ncov/2020-05-14?c=country&d=map&p=full)
#### ...as an individual
* Practice strict social distancing, especially if you are in a vulnerable group.
* Remember that even if you are not super vulnerable, many people around you are; follow these practices to protect others.
* Wash your hands "like you just chopped a jalapeno and have to change a contact lens."
* Stay home as much as possible -- especially if you are sick; be prepared with extra supplies in case you need to self-quarantine.
* If you are an employer, encourage your employees to work from home wherever possible.

#### ...as an official
* Make testing free and broadly available.
* Put strong social distancing measures in place.
* Fund and implement extensive contact tracing efforts.
* Financially support those impacted by social distancing measures.


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```auspiceMainDisplayMarkdown
# Takeaways
#### Viruses naturally acquire mutations as they replicate. This is normal. Mutations can also help us trace the course of epidemics.
#### SARS-CoV-2 mutation rate is very typical for coronaviruses.
#### As far as we know, there is only 1 functional "strain" of SARS-CoV-2.
#### Teasing apart the biological impact of specific mutations is tricky -- many perceived differences can be attributed to chance and epidemiological factors.
```

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# [Scientific credit](https://nextstrain.org/ncov/global/2020-05-14?d=tree&c=author)

We would like to acknowledge the amazing and timely work done by all scientists involved in this outbreak.
Only through the rapid sharing of genomic data and metadata are analyses such as these possible.
<br><br>
**We encourage you to click on 'Explore the Data Yourself' and scroll down for a full list of authors; the author of each individual sequence is available by selecting it on the tree.**
<br><br>
We also gratefully acknowledge GISAID for providing the platform through which these data can be uploaded and shared.

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