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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)


How COVID-19 Took Hold in North America and Europe

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SARS-CoV-2 Tracking
Caption: SARS-CoV-2 introductions to U.S. and Europe. Credit: Modified from Worobey M, Science, 2020.

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

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Boston skyline
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

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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)


The Challenge of Tracking COVID-19’s Stealthy Spread

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Floating SARS-CoV-2 particles
Credit: CDC/ Alissa Eckert, MS; Dan Higgins, MAMS

As our nation looks with hope toward controlling the coronavirus 2019 disease (COVID-19) pandemic, researchers are forging ahead with efforts to develop and implement strategies to prevent future outbreaks. It sounds straightforward. However, several new studies indicate that containing SARS-CoV-2—the novel coronavirus that causes COVID-19—will involve many complex challenges, not the least of which is figuring out ways to use testing technologies to our best advantage in the battle against this stealthy foe.

The first thing that testing may help us do is to identify those SARS-CoV-2-infected individuals who have no symptoms, but who are still capable of transmitting the virus. These individuals, along with their close contacts, will need to be quarantined rapidly to protect others. These kinds of tests detect viral material and generally analyze cells collected via nasal or throat swabs.

The second way we can use testing is to identify individuals who’ve already been infected with SARS-CoV-2, but who didn’t get seriously ill and can no longer transmit the virus to others. These individuals may now be protected against future infections, and, consequently, may be in a good position to care for people with COVID-19 or who are vulnerable to the infection. Such tests use blood samples to detect antibodies, which are blood proteins that our immune systems produce to attack viruses and other foreign invaders.

A new study, published in Nature Medicine [1], models what testing of asymptomatic individuals with active SARS-CoV-2 infections may mean for future containment efforts. To develop their model, researchers at China’s Guangzhou Medical University and the University of Hong Kong School of Public Health analyzed throat swabs collected from 94 people who were moderately ill and hospitalized with COVID-19. Frequent in-hospital swabbing provided an objective, chronological record—in some cases, for more than a month after a diagnosis—of each patient’s viral loads and infectiousness.

The model, which also factored in patients’ subjective recollections of when they felt poorly, indicates:

• On average, patients became infectious 2.3 days before onset of symptoms.
• Their highest level of potential viral spreading likely peaked hours before their symptoms appeared.
• Patients became rapidly less infectious within a week, although the virus likely remains in the body for some time.

The researchers then turned to data from a separate, previously published study [2], which documented the timing of 77 person-to-person transmissions of SARS-CoV-2. Comparing the two data sets, the researchers estimated that 44 percent of SARS-CoV-2 transmissions occur before people get sick.

Based on this two-part model, the researchers warned that traditional containment strategies (testing only of people with symptoms, contact tracing, quarantine) will face a stiff challenge keeping up with COVID-19. Indeed, they estimated that if more than 30 percent of new infections come from people who are asymptomatic, and they aren’t tested and found positive until 2 or 3 days later, public health officials will need to track down more than 90 percent of their close contacts and get them quarantined quickly to contain the virus.

The researchers also suggested alternate strategies for curbing SARS-CoV-2 transmission fueled by people who are initially asymptomatic. One possibility is digital tracing. It involves creating large networks of people who’ve agreed to install a special tracing app on their smart phones. If a phone user tests positive for COVID-19, everyone with the app who happened to have come in close contact with that person would be alerted anonymously and advised to shelter at home.

The NIH has a team that’s exploring various ways to carry out digital tracing while still protecting personal privacy. The private sector also has been exploring technological solutions, with Apple and Google recently announcing a partnership to develop application programming interfaces (APIs) to allow voluntary digital tracing for COVID-19 [3], The rollout of their first API is expected in May.

Of course, all these approaches depend upon widespread access to point-of-care testing that can give rapid results. The NIH is developing an ambitious program to accelerate the development of such testing technologies; stay tuned for more information about this in a forthcoming blog.

The second crucial piece of the containment puzzle is identifying those individuals who’ve already been infected by SARS-CoV-2, many unknowingly, but who are no longer infectious. Early results from an ongoing study on residents in Los Angeles County indicated that approximately 4.1 percent tested positive for antibodies against SARS-CoV-2 [4]. That figure is much higher than expected based on the county’s number of known COVID-19 cases, but jibes with preliminary findings from a different research group that conducted antibody testing on residents of Santa Clara County, CA [5].

Still, it’s important to keep in mind that SARS-CoV-2 antibody tests are just in the development stage. It’s possible some of these results might represent false positives—perhaps caused by antibodies to some other less serious coronavirus that’s been in the human population for a while.

More work needs to be done to sort this out. In fact, the NIH’s National Institute of Allergy and Infectious Diseases (NIAID), which is our lead institute for infectious disease research, recently launched a study to help gauge how many adults in the U. S. with no confirmed history of a SARS-CoV-2 infection have antibodies to the virus. In this investigation, researchers will collect and analyze blood samples from as many as 10,000 volunteers to get a better picture of SARS-CoV-2’s prevalence and potential to spread within our country.

There’s still an enormous amount to learn about this major public health threat. In fact, NIAID just released its strategic plan for COVID-19 to outline its research priorities. The plan provides more information about the challenges of tracking SARS-CoV-2, as well as about efforts to accelerate research into possible treatments and vaccines. Take a look!

References:

[1] Temporal dynamics in viral shedding and transmissibility of COVID-19. He X, Lau EHY, Wu P, Deng X, Wang J, Hao X, Lau YC, Wong JY, Guan Y, Tan X, Mo X, Chen Y, Liao B, Chen W, Hu F, Zhang Q, Zhong M, Wu Y, Zhao L, Zhang F, Cowling BJ, Li F, Leung GM. Nat Med. 2020 Apr 15. [Epub ahead of publication]

[2] Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. Li, Q. et al. N. Engl. J. Med. 2020 Mar 26;382, 1199–1207.

[3] Apple and Google partner on COVID-19 contact tracing technology. Apple news release, April 10, 2020.

[4] USC-LA County Study: Early Results of Antibody Testing Suggest Number of COVID-19 Infections Far Exceeds Number of Confirmed Cases in Los Angeles County. County of Los Angeles Public Health News Release, April 20, 2020.

[5] COVID-19 Antibody Seroprevalence in Santa Clara County, California. Bendavid E, Mulaney B, Sood N, Sjah S, Ling E, Bromley-Dulfano R, Lai C, Saavedra-Walker R, Tedrow J, Tversky D, Bogan A, Kupiec T, Eichner D, Gupta R, Ioannidis JP, Bhattacharya J. medRxiv, Preprint posted on April 14, 2020.

Links:

Coronavirus (COVID-19) (NIH)

COVID-19, MERS & SARS (NIAID)

NIAID Strategic Plan for COVID-19 Research, FY 2020-2024

NIH Support: National Institute of Allergy and Infectious Diseases


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