Researchers develop rapid computer software to track pandemics as they
happen
The novel algorithm can help scientists explore how a virus is evolving
in real time and inform decision-making by government leaders

Date:
November 16, 2021
Source:
Georgia State University
Summary:
Researchers have created lightning-fast computer software that can
help nations track and analyze pandemics, like the one caused by
COVID-19, before they spread like wildfire around the globe.



FULL STORY ========================================================================== Researchers at Georgia State University have created lightning-fast
computer software that can help nations track and analyze pandemics,
like the one caused by COVID-19, before they spread like wildfire around
the globe.


==========================================================================
The group of computer science and mathematics researchers says its new
software is several orders of magnitude faster than existing computer
programs and can process more than 200,000 novel virus genomes in less
than two hours. The software then builds a clear visual tree of the
strains and where they are spreading. This provides information that
can be invaluable for countries making early decisions about lockdowns, quarantines, social distancing and testing during infectious disease
outbreaks.

"The future of infectious outbreaks will no doubt be heavily data driven,"
said Alexander Zelikovsky, a Georgia State computer science professor
who worked on the project.

The new software was co-created with Pavel Skums, assistant professor of computer science, Mark Grinshpon, principal senior lecturer of mathematics
and statistics, Daniel Novikov, a computer science Ph.D. student, and two former Georgia State Ph.D. students -- Sergey Knyazev (now a postdoctoral scholar at the University of California at Los Angeles) and Pelin Icer
(now a postdoctoral scholar at Swiss Federal Institute of Technology,
ETH Zu"rich).

Their paper describing the new approach, "Scalable Reconstruction of
SARS-CoV- 2 Phylogeny with Recurrent Mutations," was published in the
Journal of Computational Biology.

"The COVID-19 pandemic has been an unprecedented challenge and opportunity
for scientists," said Skums, who noted that never before have researchers around the world sequenced so many complete genomes of any virus. The
strains of SARS- CoV-2 are uploaded onto the free global GISAID database (https:// www.gisaid.org/hcov19-variants/), where they can be data-mined
and studied by any scientist. Zelikovsky, Skums and their colleagues
analyzed more than 300,000 different GISAID strains for their new work.



========================================================================== "There are over 5 million genomes in the GISAID database now," said
Zelikovsky.

"Scientists around the globe are probably sequencing a new variant almost
every hour." Zelikovsky said that this astounding amount of data allows scientists to see the evolution of the virus in action in real time --
if we have software capable of rapidly analyzing it.

In the early days of the pandemic, in March 2020, scientists were working
much more slowly. Scientists thought the virus had first arrived on our
shores in the state of Washington in February. However, later sequencing presented in a paper by Skums and his colleagues showed the arcs of
viral variants traveling across countries and oceans. With new studies, scientists learned that the virus had also likely arrived quietly in
New York City in February, from strains originating in Europe.

Back then, scientists were sequencing data too slowly to capture the
true migration of this global virus and its mutations in real time.

"The programs were not fast enough, not scalable enough," said Skums. "The algorithms were not equipped to handle huge amounts of data." It could
take hours or days to process even a small subset of viral genomes,
he said.



========================================================================== Zelikovsky, Skums and their colleagues created a novel algorithm for
viral sequencing called SPHERE (Scalable PHylogEny with Recurrent
mutations.) SPHERE can rapidly handle huge amounts of real-time data
and create evolutionary trees of the virus and its mutations. These visualizations can be easily grasped at a glance. The computer program
itself is freely available for download to any researcher in the world.

When the researchers applied their algorithm to genomes from the GISAID database, they found their SPHERE approach to be highly reliable in
tracking the way the virus was spreading. SPHERE can help scientists
explore how a virus is evolving in real time.

"We can see how the mutations spread from country to country and region
to region," said Zelikovsky. "We can determine how lockdowns and closures impact spread. This has consequences for government policy." The SPHERE algorithm could prove invaluable in future pandemics.

"You could track down chains of transmission very quickly," said
Zelikovsky.

Seeing those chains will help governments to make sound decisions about
social policies such as distancing or lockdowns during times of high transmission.

SPHERE can also show the impact of different approaches to outbreaks. For instance, said Skums, Sweden took a more relaxed approach to the COVID-19 pandemic than other Nordic countries. An analysis of the sequencing data
shows that Swedes have longer "transmission chains." This means that in
Sweden, one strain is able to infect many more people, one by one.

"The danger of long chains is that a new strain may appear," said
Zelikovsky.

"And one of those strains may be a variant that is very good at infecting people." These kinds of insights will help us should we face another
global pandemic.

"The tools we and others have developed can be used anywhere for any
outbreak," said Zelikovsky. "That is the beauty of computer science." ========================================================================== Story Source: Materials provided by Georgia_State_University. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Daniel Novikov, Sergey Knyazev, Mark Grinshpon, Pelin Icer,
Pavel Skums,
Alex Zelikovsky. Scalable Reconstruction of SARS-CoV-2 Phylogeny
with Recurrent Mutations. Journal of Computational Biology, 2021;
28 (11): 1130 DOI: 10.1089/cmb.2021.0306 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211116131558.htm

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