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SARS-CoV-2 transmission

U.K. Study Shows Power of Digital Contact Tracing for COVID-19

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COVID-19 cases in the United Kindom. Hands hold a smart phone with the NHS COVID-19 app
Credit: Adapted from Getty Image and Wymant C, Nature, 2021

There’s been much interest in using digital technology to help contain the spread of COVID-19 in our communities. The idea is to make available opt-in smart phone apps that create a log of other apps operating on the phones of nearby participants. If a participant tests positive for COVID-19 and enters the result, the app will then send automatic alerts to those phones—and participants—who recently came into close proximity with them.

In theory, digital tracing would be much faster and more efficient than the challenging detective work involved in traditional contract tracing. But many have wondered how well such an opt-in system would work in practice. A recent paper, published in the journal Nature, shows that a COVID-19 digital tracing app worked quite well in the United Kingdom [1].

The research comes from Christophe Fraser, Oxford University, and his colleagues in the U.K. The team studied the NHS COVID-19 app, the National Health Service’s digital tracing smart phone app for England and Wales. Launched in September 2020, the app has been downloaded onto 21 million devices and used regularly by about half of eligible smart phone users, ages 16 and older. That’s 16.5 million of 33.7 million people, or more than a quarter of the total population of England and Wales.

From the end of September through December 2020, the app sent about 1.7 million exposure notifications. That’s 4.4 on average for every person with COVID-19 who opted-in to the digital tracing app.

The researchers estimate that around 6 percent of app users who received notifications of close contact with a positive case went on to test positive themselves. That’s similar to what’s been observed in traditional contact tracing.

Next, they used two different approaches to construct mathematical and statistical models to determine how likely it was that a notified contact, if infected, would quarantine in a timely manner. Though the two approaches arrived at somewhat different answers, their combined outputs suggest that the app may have stopped anywhere from 200,000 to 900,000 infections in just three months. This means that roughly one case was averted for each COVID-19 case that consented to having their contacts notified through the app.

Of course, these apps are only as good as the total number of people who download and use them faithfully. They estimate that for every 1 percent increase in app users, the number of COVID-19 cases could be reduced by another 1 or 2 percent. While those numbers might sound small, they can be quite significant when one considers the devastating impact that COVID-19 continues to have on the lives and livelihoods of people all around the world.

Reference:

[1] The epidemiological impact of the NHS COVID-19 App. Wymant C, Ferretti L, Tsallis D, Charalambides M, Abeler-Dörner L, Bonsall D, Hinch R, Kendall M, Milsom L, Ayres M, Holmes C, Briers M, Fraser C. Nature. 2021 May 12.

Links:

COVID-19 Research (NIH)

NHS COVID-19 App

Christophe Fraser (Oxford University, UK)


Infections with ‘U.K. Variant’ B.1.1.7 Have Greater Risk of Mortality

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One coronavirus in a group looks different and is labeled B.1.1.7 variant. Lines radiate from Britain on a map.

Since the genome sequence of SARS-CoV-2, the virus responsible for COVID-19, was first reported in January 2020, thousands of variants have been reported. In the vast majority of cases, these variants, which arise from random genomic changes as SARS-CoV-2 makes copies of itself in an infected person, haven’t raised any alarm among public health officials. But that’s now changed with the emergence of at least three variants carrying mutations that potentially make them even more dangerous.

At the top of this short list is a variant known as B.1.1.7, first detected in the United Kingdom in September 2020. This variant is considerably more contagious than the original virus. It has spread rapidly around the globe and likely accounts already for at least one-third of all cases in the United States [1]. Now comes more troubling news: emerging evidence indicates that infection with this B.1.1.7 variant also comes with an increased risk of severe illness and death [2].

The findings, reported in Nature, come from Nicholas Davies, Karla Diaz-Ordaz, and Ruth Keogh, London School of Hygiene and Tropical Medicine. The London team earlier showed that this new variant is 43 to 90 percent more transmissible than pre-existing variants that had been circulating in England [3]. But in the latest paper, the researchers followed up on conflicting reports about the virulence of B.1.1.7.

They did so with a large British dataset linking more than 2.2 million positive SARS-CoV-2 tests to 17,452 COVID-19 deaths from September 1, 2020, to February 14, 2021. In about half of the cases (accounting for nearly 5,000 deaths), it was possible to discern whether or not the infection had been caused by the B.1.1.7 variant.

Based on this evidence, the researchers calculated the risk of death associated with B.1.1.7 infection. Their estimates suggest that B.1.1.7 infection was associated with 55 percent greater mortality compared to other SARS-CoV-2 variants over this time period.

For a 55- to 69-year-old male, this translates to a 0.9-percent absolute, or personal, risk of death, up from 0.6 percent for the older variants. That means nine in every 1,000 people in this age group who test positive with the B.1.1.7 variant would be expected to die from COVID-19 a month later. For those infected with the original virus, that number would be six.

The U.S. percentage of B.1.1.7 started near zero on January 2, 2021 but by March 13 was over 20%.
Adapted from Centers for Disease Control and Prevention

These findings are in keeping with those of another recent study reported in the British Medical Journal [4]. In that case, researchers at the University of Exeter and the University of Bristol found that the B.1.1.7 variant was associated with a 64 percent greater chance of dying compared to earlier variants. That’s based on an analysis of data from more than 100,000 COVID-19 patients in the U.K. from October 1, 2020, to January 28, 2021.

That this variant comes with increased disease severity and mortality is particularly troubling news, given the highly contagious nature of B.1.1.7. In fact, Davies’ team has concluded that the emergence of new SARS-CoV-2 variants now threaten to slow or even cancel out improvements in COVID-19 treatment that have been made over the last year. These variants include not only B1.1.7, but also B.1.351 originating in South Africa and P.1 from Brazil.

The findings are yet another reminder that, while we’re making truly remarkable progress in the fight against COVID-19 with increasing availability of safe and effective vaccines (more than 45 million Americans are now fully immunized), now is not the time to get complacent. This devastating pandemic isn’t over yet.

The best way to continue the fight against all SARS-CoV-2 variants is for each one of us to do absolutely everything we can to stop their spread. This means that taking the opportunity to get vaccinated as soon as it is offered to you, and continuing to practice those public health measures we summarize as the three Ws: Wear a mask, Watch your distance, Wash your hands often.

References:

[1] US COVID-19 Cases Caused by Variants. Centers for Disease Control and Prevention.

[2] Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7. Davies NG, Jarvis CI; CMMID COVID-19 Working Group, Edmunds WJ, Jewell NP, Diaz-Ordaz K, Keogh RH. Nature. 2021 Mar 15.

[3] Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Davies NG, Abbott S, Barnard RC, Jarvis CI, Kucharski AJ, Munday JD, Pearson CAB, Russell TW, Tully DC, Washburne AD, Wenseleers T, Gimma A, Waites W, Wong KLM, van Zandvoort K, Silverman JD; CMMID COVID-19 Working Group; COVID-19 Genomics UK (COG-UK) Consortium, Diaz-Ordaz K, Keogh R, Eggo RM, Funk S, Jit M, Atkins KE, Edmunds WJ.
Science. 2021 Mar 3:eabg3055.

[4] Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study. Challen R, Brooks-Pollock E, Read JM, Dyson L, Tsaneva-Atanasova K, Danon L. BMJ. 2021 Mar 9;372:n579.

Links:

COVID-19 Research (NIH)

Nicholas Davies (London School of Hygiene and Tropical Medicine, U.K.)

Ruth Keogh (London School of Hygiene and Tropical Medicine, U.K.)


South Africa Study Shows Power of Genomic Surveillance Amid COVID-19 Pandemic

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COVID-19 testing in South Africa
Credit: iStock/Thomas Faull

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)


A Close-up of COVID-19 in Lung Cells

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SARS-CoV-2 infected lung cells
Credit: Ehre Lab, UNC School of Medicine

If you or a loved one have come down with SARS-CoV-2, the coronavirus responsible for COVID-19, you know it often takes hold in the respiratory system. This image offers a striking example of exactly what happens to cells in the human airway when this coronavirus infects them.

This colorized scanning electron microscope (SEM) image shows SARS-CoV-2-infected human lung cells (purple) covered in hair-like cilia (blue). Those cilia line the inner surface of the airways and help to clear mucus (yellow-green) containing dust and other debris from the lungs. Emerging from the surface of those infected airway cells are many thousands of coronavirus particles (red).

This dramatic image, published recently in the New England Journal of Medicine, comes from the lab of pediatric pulmonologist Camille Ehre, University of North Carolina at Chapel Hill. Ehre and team study mucus and how its properties change in cystic fibrosis, chronic obstructive pulmonary disease (COPD), and various other conditions that affect the lungs. These days, they’re also focusing their attention on SARS-CoV-2 and potentially new ways to block viral entry into cells of the human airway.

As part of that effort, she and her colleagues captured this snapshot of SARS-CoV-2 viruses exiting from lung cells in a lab dish. They first cultured cells from the lining of a human airway, then inoculated them with the virus. Ninety-six hours later, this is what they saw in greyscale. The vivid colors were added later by UNC medical student Cameron Morrison.

The image illustrates the astoundingly large number of viral particles that can be produced and released from infected human cells. Ehre notes that in a lab dish containing about a million human cells, they’ve witnessed the virus explode from about 1,000 particles to about 10 million in just a couple of days.

The dramatic increase in viral particles helps to explain how COVID-19 spreads so easily from the lungs to other parts of the body and—all too often—on to other individuals, especially in crowded, indoor places where people aren’t able to keep their distance. Hopefully, images like this one will help to inspire more of us this winter to avoid the crowds (especially indoors), wear masks, and wash our hands frequently.

Reference:

[1] SARS-CoV-2 infection of airway cells. Ehre C. NEJM. 2020 Sep 3;383(10):969.

Links:

Coronavirus (COVID-19) (NIH)

Camille Ehre (University of North Carolina, Chapel Hill)


Genome Data Help Track Community Spread of COVID-19

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RNA Virus
Credit: iStock/vchal

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)