genomic surveillance
Tracking the Evolution of a ‘Variant of Concern’ in Brazil
Posted on by Dr. Francis Collins
By last October, about three out of every four residents of Manaus, Brazil already had been infected with SARS-CoV-2, the virus that causes COVID-19 [1]. And yet, despite hopes of achieving “herd immunity” in this city of 2.2 million in the Amazon region, the virus came roaring back in late 2020 and early 2021 to cause a second wave of illness and death [2]. How is this possible?
The answer offers a lesson in viral evolution, especially when an infectious virus such as SARS-CoV-2 replicates and spreads through a population largely unchecked. In a recent study in the journal Science, researchers tied the city’s resurgence of SARS-CoV-2 to the emergence and rapid spread of a new SARS-CoV-2 “variant of concern” known as P.1 [3]. This variant carries a unique constellation of mutations that allow it not only to sneak past the human immune system and re-infect people, but also to be about twice as transmissible as earlier variants.
To understand how this is possible, consider that each time the coronavirus SARS-CoV-2 makes copies of itself in an infected person, there’s a chance a mistake will be made. Each mistake can produce a new variant that may go on to make more copies of itself. In most cases, those random errors are of little to no consequence. This is evolution in action.
But sometimes a spelling change can occur that benefits the virus. In the special case of patients with suppressed immune systems, the virus can have ample opportunity to accrue an unusually high number of mutations. Variants carrying beneficial mutations can make more copies of themselves than other variants, allowing them to build their numbers and spread to cause more infection.
At this advanced stage of the COVID-19 pandemic, such rapidly spreading new variants remain cause for serious concern. That includes variants such as B.1.351, which originated in South Africa; B.1.1.7 which emerged in the United Kingdom; and now P.1 from Manaus, Brazil.
In the new study, Nuno Faria and Samir Bhatt, Imperial College London, U.K., and Ester Cerdeira Sabino, Universidade de Sao Paulo, Brazil, and their colleagues sequenced SARS-CoV-2 genomes from 184 patient samples collected in Manaus in November and December 2020. The research was conducted under the auspices of the Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE), a project focused on viral genomics and epidemiology for public health.
Those genomic data revealed the P.1 variant had acquired 17 new mutations. Ten were in the spike protein, which is the segment of the virus that binds onto human cells and the target of current COVID-19 vaccines. In fact, the new work reveals that three of these spike protein mutations make it easier for the P.1 spike to bind the human ACE2 receptor, which is SARS-CoV-2’s preferred entry point.
The first P.1 variant case was detected by genomic surveillance on December 6, 2020, after which it spread rapidly. Through further evolutionary analysis, the team estimates that P.1 must have emerged, undetected for a brief time, in mid-November 2020.
To understand better how the P.1 variant led to such an explosion of new COVID-19 cases, the researchers developed a mathematical model that integrated the genomic data with mortality data. The model suggests that P.1 may be 1.7 to 2.4 times more transmissible than earlier variants. They also estimate that a person previously infected with a variant other than P.1 will have only 54 percent to 79 percent protection against a subsequent infection with P.1.
The researchers also observed an increase in mortality following the emergence of the P.1 variant. However, it’s not yet clear if that’s an indication P.1 is inherently more deadly than earlier variants. It’s possible the increased mortality is related primarily to the extra stress on the healthcare system in Manaus from treating so many people with COVID-19.
These findings are yet another reminder of the importance of genomic surveillance and international data sharing for detecting and characterizing emerging SARS-CoV-2 variants quickly. It’s worth noting that at about the same time this variant was detected in Brazil, it also was reported in four individuals who had traveled to Brazil from Japan. The P.1 variant continues to spread rapidly across Brazil. It has also been detected in more than 37 countries [4], including the United States, where it now accounts for more than 1 percent of new cases [5].
No doubt you are wondering what this means for vaccines, such as the Pfizer and Moderna mRNA vaccines, that have been used to immunize (at least one dose) over 140 million people in the United States. Here the news is encouraging. Serum from individuals who received the Pfizer vaccine had titers of neutralizing antibodies that were only slightly reduced for P.1 compared to the original SARS-CoV-2 virus [6]. Therefore, the vaccine is predicted to be highly protective. This is another example of a vaccine providing more protection than a natural infection.
The United States has made truly remarkable progress in combating COVID-19, but we must heed this lesson from Manaus: this terrible pandemic isn’t over just yet. While the P.1 variant remains at low levels here for now, the “U.K. variant” B.1.1.7 continues to spread rapidly and now is the most prevalent variant circulating in the U.S., accounting for 44 percent of new cases [6]. Fortunately, the mRNA vaccines also work well against B.1.1.7.
We must continue to do absolutely everything possible, individually and collectively, to prevent these new SARS-CoV-2 variants from slowing or even canceling the progress made over the last year. We need to remain vigilant for just a while longer, while encouraging our friends, neighbors, and loved ones to get vaccinated.
References:
[1] Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic. Buss, L. F., C. A. Prete, Jr., C. M. M. Abrahim, A. C. Dye, V. H. Nascimento, N. R. Faria and E. C. Sabino et al. (2021). Science 371(6526): 288-292.
[2] Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence. Sabino EC, Buss LF, Carvalho MPS, Prete Jr CCA, Crispim MAE, Fraiji NA, Pereira RHM, Paraga KV, Peixoto PS, Kraemer MUG, Oikawa MJ, Salomon T, Cucunuba ZM, Castro MC, Santos AAAS, Nascimento VH, Pereira HS, Ferguson NM, Pybus OG, Kucharski A, Busch MP, Dye C, Faria NR Lancet. 2021 Feb 6;397(10273):452-455.
[3] Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Faria NR, Mellan TA, Whittaker C, Claro IM, Fraiji NA, Carvalho MDPSS, Pybus OG, Flaxman S, Bhatt S, Sabino EC et al. Science. 2021 Apr 14:eabh2644.
[4] GRINCH Global Report Investigating novel coronavirus haplotypes. PANGO Lineages.
[5] COVID Data Tracker. Variant Proportions. Centers for Disease Control and Prevention.
[6] Antibody evasion by the P.1 strain of SARS-CoV-2. Dejnirattisai W, Zhou D, Supasa P, Liu C, Mongkolsapaya J, Ren J, Stuart DI, Screaton GR, et al. Cell. 2021 Mar 30:S0092-8674(21)00428-1.
Links:
COVID-19 Research (NIH)
Brazil-UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE)
Nuno Faria (Imperial College, London, U.K.)
Samir Bhatt (Imperial College)
Ester Cerdeira Sabino (Universidade de Sao Paulo, Brazil)
NIH Support: National Institute of Allergy and Infectious Diseases
South Africa Study Shows Power of Genomic Surveillance Amid COVID-19 Pandemic
Posted on by Dr. Francis Collins
Considerable research is underway around the world to monitor the spread of new variants of SARS-CoV-2, the coronavirus that causes COVID-19. That includes the variant B.1.351 (also known as 501Y.V2), which emerged in South Africa towards the end of 2020 [1, 2]. Public health officials in South Africa have been busy tracing the spread of this genomic variant and others across their country. And a new analysis of such data reveals that dozens of distinct coronavirus variants were already circulating in South Africa well before the appearance of B.1.351.
A study of more than 1,300 near-whole genome sequences of SARS-CoV-2, published recently in the journal Nature Medicine, shows there were in fact at least 42 SARS-CoV-2 variants spreading in South Africa within the pandemic’s first six months in that country [3]. Among them were 16 variants that had never before been described. Most of the single-letter changes carried by these variants didn’t change the virus in important ways and didn’t rise to significant frequency. But the findings come as another critical reminder of the value of genomic surveillance to track the spread of SARS-CoV-2 to identify any potentially worrisome new variants and to inform measures to get this devastating pandemic under control.
SARS-CoV-2 was first detected in South Africa on March 5, 2020, in a traveler returning from Italy. By November 2020, despite considerable efforts to slow the spread, more than 785,000 people in South Africa were infected, accounting for about half of all reported COVID-19 cases on the African continent.
Recognizing the importance of genomic surveillance, researchers led by Houriiyah Tegally and Tulio de Oliveira, University of KwaZulu-Natal, Durban, South Africa, wasted no time in producing 1,365 near-complete SARS-CoV-2 genomes by mid-September, near the end of the coronavirus’s first peak in the country. Those samples had been collected in hundreds of clinics over the course of the pandemic in eight of South Africa’s nine provinces, offering a broad picture of the spread and emergence of new variants across the country.
The data revealed three main variants, dubbed B.1.1.54, B.1.1.56, and C.1, that were responsible for 42 percent of all the infections in South Africa’s first wave. Of the 16 newly described variants, most carried single-letter changes that haven’t been identified in other countries.
The majority of changes were what scientists refer to as “synonymous,” meaning that they don’t change the structure or function of any of the virus’s essential proteins. The exception is the newly identified C.1, which includes 16 single-letter changes compared to the original sequence from Wuhan, China. One of those 16 changes swaps a single amino acid for another on SARS-CoV-2’s spike protein. That’s notable because the spike protein is a key target of antibodies and also is essential to the virus’s ability to infect human cells.
In fact, four of the most prevalent variants in South Africa all carry this same mutation. The researchers also saw three other changes that would alter the spike protein in different ways, although the significance of these for viral spread and our efforts to stop it isn’t yet clear.
Importantly, the data show that the bulk of introductions to South Africa happened early on, before lockdown and travel restrictions were implemented in late March. Subsequently, much of the spread within South Africa stemmed from hospital outbreaks. For example, an outbreak of the C.1 variant in the North West Province in April ultimately led this variant to become the most geographically widespread in South Africa by the end of August. Meanwhile, an earlier identified South African-specific variant, B.1.106, first identified in April, vanished altogether after outbreaks were controlled in KwaZulu-Natal Province, where the researchers reside.
Genomic surveillance has remarkable power for understanding the evolution of SARS-CoV-2 and tracking the dynamics of its transmission. Tegally and de Oliveira’s team notes that this type of intensive genomic surveillance now can be used on a large scale across Africa and around the world to identify new variants of SARS-CoV-2 and to develop timely measures to control the spread of the virus. They’re now working with the African CDC to expand genomic surveillance across Africa [4].
Such genomic surveillance was crucial in the subsequent identification of the B.1.351 variant in South Africa that we’ve been hearing so much about, with its potential to evade our current treatments and vaccines. By picking up on such concerning mutations early through genomic surveillance and understanding how the virus is spreading over time and space, the hope is we’ll be better informed and more adept in our efforts to get this pandemic under control.
References:
[1] Emerging SARS-CoV-2 variants. Centers for Disease Control and Prevention.
[2] Emergence and rapid spread of a new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) lineage with multiple spike mutations in South Africa. Tegally H, Wilkinson E, Giovanetti M, Iranzadeh A, Bhiman J, Williamson C, de Oliveira T, et al. medRxiv 2020 Dec 22.
[3] Sixteen novel lineages of SARS-CoV-2 in South Africa. Tegally H, Wilkinson E, Lessells RJ, Giandhari J, Pillay S, Msomi N, Mlisana K, Bhiman JN, von Gottberg A, Walaza S, Fonseca V, Allam M, Ismail A, Glass AJ, Engelbrecht S, Van Zyl G, Preiser W, Williamson C, Petruccione F, Sigal A, Gazy I, Hardie D, Hsiao NY, Martin D, York D, Goedhals D, San EJ, Giovanetti M, Lourenço J, Alcantara LCJ, de Oliveira T. Nat Med. 2021 Feb 2.
[4] Accelerating genomics-based surveillance for COVID-19 response in Africa. Tessema SK, Inzaule SC, Christoffels A, Kebede Y, de Oliveira T, Ouma AEO, Happi CT, Nkengasong JN.Lancet Microbe. 2020 Aug 18.
Links:
COVID-19 Research (NIH)
Houriiyah Tegally (University of KwaZulu-Natal, Durban, South Africa)
Tulio de Oliveira (University of KwaZulu-Natal)
How COVID-19 Took Hold in North America and Europe
Posted on by Dr. Francis Collins
It was nearly 10 months ago on January 15 that a traveler returned home to the Seattle area after visiting family in Wuhan, China. A few days later, he started feeling poorly and became the first laboratory-confirmed case of coronavirus disease 2019 (COVID-19) in the United States. The rest is history.
However, new evidence published in the journal Science suggests that this first COVID-19 case on the West Coast didn’t snowball into the current epidemic. Instead, while public health officials in Washington state worked tirelessly and ultimately succeeded in containing its sustained transmission, the novel coronavirus slipped in via another individual about two weeks later, around the beginning of February.
COVID-19 is caused by the novel coronavirus SARS-CoV-2. Last winter, researchers sequenced the genetic material from the SARS-CoV-2 that was isolated from the returned Seattle traveler. While contact tracing didn’t identify any spread of this particular virus, dubbed WA1, questions arose when a genetically similar virus known as WA2 turned up in Washington state. Not long after, WA2-like viruses then appeared in California; British Columbia, Canada; and eventually 3,000 miles away in Connecticut. By mid-March, this WA2 cluster accounted for the vast majority—85 percent—of the cases in Washington state.
But was it possible that the WA2 cluster is a direct descendent of WA1? Did WA1 cause an unnoticed chain of transmission over several weeks, making the Seattle the epicenter of the outbreak in North America?
To answer those questions and others from around the globe, Michael Worobey, University of Arizona, Tucson, and his colleagues drew on multiple sources of information. These included data peretaining to viral genomes, airline passenger flow, and disease incidence in China’s Hubei Province and other places that likely would have influenced the probability that infected travelers were moving the virus around the globe. Based on all the evidence, the researchers simulated the outbreak more than 1,000 times on a computer over a two-month period, beginning on January 15 and assuming the epidemic started with WA1. And, not once did any of their simulated outbreaks match up to the actual genome data.
Those findings suggest to the researchers that the idea WA1 is responsible for all that came later is exceedingly unlikely. The evidence and simulations also appear to rule out the notion that the earliest cases in Washington state entered the United States by way of Canada. A deep dive into the data suggests a more likely scenario is that the outbreak was set off by one or more introductions of genetically similar viruses from China to the West Coast. Though we still don’t know exactly where, the Seattle area is the most likely site given the large number of WA2-like viruses sampled there.
Worobey’s team conducted a second analysis of the outbreak in Europe, and those simulations paint a similar picture to the one in the United States. The researchers conclude that the first known case of COVID-19 in Europe, arriving in Germany on January 20, led to a relatively small number of cases before being stamped out by aggressive testing and contact tracing efforts. That small, early outbreak probably didn’t spark the later one in Northern Italy, which eventually spread to the United States.
Their findings also show that the chain of transmission from China to Italy to New York City sparked outbreaks on the East Coast slightly later in February than those that spread from China directly to Washington state. It confirms that the Seattle outbreak was indeed the first, predating others on the East Coast and in California.
The findings in this report are yet another reminder of the value of integrating genome surveillance together with other sources of data when it comes to understanding, tracking, and containing the spread of COVID-19. They also show that swift and decisive public health measures to contain the virus worked when SARS-CoV-2 first entered the United States and Europe, and can now serve as models of containment.
Since the suffering and death from this pandemic continues in the United States, this historical reconstruction from early in 2020 is one more reminder that all of us have the opportunity and the responsibility to try to limit further spread. Wear your mask when you are outside the home; maintain physical distancing; wash your hands frequently; and don’t congregate indoors, where the risks are greatest. These lessons will enable us to better anticipate, prevent, and respond to additional outbreaks of COVID-19 or any other novel viruses that may arise in the future.
Reference:
[1] The emergence of SARS-CoV-2 in Europe and North America. Worobey M, Pekar J, Larsen BB, Nelson MI, Hill V, Joy JB, Rambaut A, Suchard MA, Wertheim JO, Lemey P. Science. 2020 Sep 10:eabc8169 [Epub ahead of print]
Links:
Coronavirus (COVID-19) (NIH)
Michael Worobey (University of Arizona, Tucson)
NIH Support: National Institute of Allergy and Infectious Diseases; Fogarty International Center; National Library of Medicine
Genome Data Help to Track COVID-19 Superspreading Event
Posted on by Dr. Francis Collins
Credit: iStock/Chaay_Tee
When it comes to COVID-19, anyone, even without symptoms, can be a “superspreader” capable of unknowingly infecting a large number of people and causing a community outbreak. That’s why it is so important right now to wear masks when out in public and avoid large gatherings, especially those held indoors, where a superspreader can readily infect others with SARS-CoV-2, the virus responsible for COVID-19.
Driving home this point is a new NIH-funded study on the effects of just one superspreader event in the Boston area: an international biotech conference held in February, before the public health risks of COVID-19 had been fully realized [1]. Almost a hundred people were infected. But it didn’t end there.
In the study, the researchers sequenced close to 800 viral genomes, including cases from across the first wave of the epidemic in the Boston area. Using the fact that the viral genome changes in very subtle ways over time, they found that SARS-CoV-2 was actually introduced independently to the region more than 80 times, primarily from Europe and other parts of the United States. But the data also suggest that a single superspreading event at the biotech conference led to the infection of almost 20,000 people in the area, not to mention additional COVID-19 cases in other states and around the world.
The findings, posted on medRxiv as a pre-print, come from Bronwyn MacInnis and Pardis Sabeti at the Broad Institute of MIT and Harvard in Cambridge, MA, and their many close colleagues at Massachusetts General Hospital, the Massachusetts Department of Public Health, and the Boston Health Care for the Homeless Program. The initial focus of MacInnis, Sabeti, and their Broad colleagues has been on developing genome data and tools for surveillance of viruses and other infectious diseases in and viral outbreaks in West Africa, including Lassa fever and Ebola virus disease.
Closer to home, they’d expected to focus their attention on West Nile virus and tick-borne diseases. But, when the COVID-19 outbreak erupted, they were ready to pivot quickly to assist several Centers for Disease Control and Prevention (CDC) and state labs in the northeastern United States to use genomic tools to investigate local outbreaks.
It’s been clear from the beginning of the pandemic that COVID-19 cases often arise in clusters, linked to gatherings in places such as cruise ships, nursing homes, and homeless shelters. But the Broad Institute team and their colleagues realized, it’s difficult to see how extensively a virus spreads from such places into the wider community based on case counts alone.
Contact tracing certainly helps to track community spread of the virus. This surveillance strategy depends on quick, efficient identification of an infected individual. It follows up with the identification of all who’ve recently been in close contact with that person, allowing the contacts to self-quarantine and break the chain of transmission.
But contact tracing has its limitations. It’s not always possible to identify all the people that an infected person has been in recent contact with. Genome data, however, is particularly useful after the fact for connecting those dots to get a big picture view of viral transmission.
Here’s how it works: as SARS-CoV-2 spreads, the virus sometimes picks up a new mutation. Those tiny spelling changes in the viral genome usually have no effect on how the virus causes disease, but they do serve as distinct genomic fingerprints. Using those fingerprints to guide the way, researchers can trace the path the virus took through a community and beyond, identifying connections among cases that would be untrackable otherwise.
With this in mind, MacInnis and Sabeti’s team set out to help Boston’s public health officials understand just how the epidemic escalated so quickly in the Boston area, and just how much the February conference had contributed to community transmission of the virus. They also investigated other case clusters in the area, including within a skilled nursing facility, homeless shelters, and at Massachusetts General Hospital itself, to understand the spread of COVID-19 in these settings.
Based on contact tracing, officials had already connected approximately 90 cases of COVID-19 to the biotech conference, 28 of which were included in the original 772 viral genomes in this dataset. Based on the distinct genomic fingerprint carried by the 28 genomes, the researchers went on to discover that more than one-third of Boston area cases without any known link to the conference could indeed be traced back to the event.
When the researchers considered this proportion to the number of cases recorded in the region during the study, they extrapolated that the superspreader event led to nearly 20,000 cases in the Boston area. In contrast, the genome data show cases linked to another superspreader event that took place within a skilled nursing facility, while devastating to the residents, had much less of an impact on the surrounding community.
The analysis also uncovered some unexpected connections. The dataset showed that SARS-CoV-2 was brought to clients and staff at the Boston Health Care for the Homeless Program at least seven times. Remarkably, two of those introductions also traced back to the biotech conference. Researchers also found infections in Chelsea, Revere, and Everett, which were some of the hardest hit communities in the Boston area, that were connected to the original superspreading event.
There was some reassuring news about how precautions in hospitals are working. The researchers examined cases that were diagnosed among patients at Massachusetts General Hospital, raising concerns that the virus might have spread from one patient to another within the hospital. But the genome data show that those cases, in fact, weren’t part of the same transmission chain. They may have contracted the virus before they were hospitalized. Or it’s possible that staff may have inadvertently brought the virus into the hospital. But there was no patient-to-patient transmission.
Massachusetts is one of the states in which the COVID-19 pandemic had a particularly severe early impact. As such, these results present broadly applicable lessons for other states and urban areas about how the virus spreads. The findings highlight the value of genomic surveillance, along with standard contact tracing, for better understanding of viral transmission in our communities and improved prevention of future outbreaks.
Reference:
[1] Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events. Lemieux J. et al. medRxiv. August 25, 2020.
Links:
Coronavirus (COVID-19) (NIH)
Bronwyn MacInnis (Broad Institute of Harvard and MIT, Cambridge, MA)
Sabeti Lab (Broad Institute of Harvard and MIT)
NIH Support: National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; National Institute of General Medical Sciences
Genome Data Help Track Community Spread of COVID-19
Posted on by Dr. Francis Collins
Contact tracing, a term that’s been in the news lately, is a crucial tool for controlling the spread of SARS-CoV-2, the novel coronavirus that causes COVID-19. It depends on quick, efficient identification of an infected individual, followed by identification of all who’ve recently been in close contact with that person so the contacts can self-quarantine to break the chain of transmission.
Properly carried out, contact tracing can be extremely effective. It can also be extremely challenging when battling a stealth virus like SARS-CoV-2, especially when the virus is spreading rapidly.
But there are some innovative ways to enhance contact tracing. In a new study, published in the journal Nature Medicine, researchers in Australia demonstrate one of them: assembling genomic data about the virus to assist contact tracing efforts. This so-called genomic surveillance builds on the idea that when the virus is passed from person to person over a few months, it can acquire random variations in the sequence of its genetic material. These unique variations serve as distinctive genomic “fingerprints.”
When COVID-19 starts circulating in a community, researchers can fingerprint the genomes of SARS-CoV-2 obtained from newly infected people. This timely information helps to tell whether that particular virus has been spreading locally for a while or has just arrived from another part of the world. It can also show where the viral subtype has been spreading through a community or, best of all, when it has stopped circulating.
The recent study was led by Vitali Sintchenko at the University of Sydney. His team worked in parallel with contact tracers at the Ministry of Health in New South Wales (NSW), Australia’s most populous state, to contain the initial SARS-CoV-2 outbreak from late January through March 2020.
The team performed genomic surveillance, using sequencing data obtained within about five days, to understand local transmission patterns. They also wanted to compare what they learned from genomic surveillance to predictions made by a sophisticated computer model of how the virus might spread amongst Australia’s approximately 24 million citizens.
Of the 1,617 known cases in Sydney over the three-month study period, researchers sequenced viral genomes from 209 (13 percent) of them. By comparing those sequences to others circulating overseas, they found a lot of sequence diversity, indicating that the novel coronavirus had been introduced to Sydney many times from many places all over the world.
They then used the sequencing data to better understand how the virus was spreading through the local community. Their analysis found that the 209 cases under study included 27 distinct genomic fingerprints. Based on the close similarity of their genomic fingerprints, a significant share of the COVID-19 cases appeared to have stemmed from the direct spread of the virus among people in specific places or facilities.
What was most striking was that the genomic evidence helped to provide information that contact tracers otherwise would have lacked. For instance, the genomic data allowed the researchers to identify previously unsuspected links between certain cases of COVID-19. It also helped to confirm other links that were otherwise unclear.
All told, researchers used the genomic evidence to cluster almost 40 percent of COVID-19 cases (81 of 209) for which the community-based data alone couldn’t identify a known contact source for the infection. That included 26 cases in which an individual who’d recently arrived in Australia from overseas spread the infection to others who hadn’t traveled. The genomic information also helped to identify likely sources in the community for another 15 locally acquired cases that weren’t known based on community data.
The researchers compared their genome surveillance data to SARS-CoV-2’s expected spread as modeled in a computer simulation based on travel to and from Australia over the time period in question. Because the study involved just 13 percent of all known COVID-19 cases in Sydney between late January through March, it’s not surprising that the genomic data presents an incomplete picture, detecting only a portion of the possible chains of transmission expected in the simulation model.
Nevertheless, the findings demonstrate the value of genomic data for tracking the virus and pinpointing exactly where in the community it is spreading. This can help to fill in important gaps in the community-based data that contact tracers often use. Even more exciting, by combining traditional contact tracing, genomic surveillance, and mathematical modeling with other emerging tools at our disposal, it may be possible to get a clearer picture of the movement of SARS-CoV-2 and put more targeted public health measures in place to slow and eventually stop its deadly spread.
Reference:
[1] Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling. Rockett RJ, Arnott A, Lam C, et al. Nat Med. 2020 July 9. [Published online ahead of print]
Links:
Coronavirus (COVID-19) (NIH)
Vitali Sintchenko (University of Sydney, Australia)
