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Reporting in , a team from the ӳ����ý of MIT and Harvard, Massachusetts General Hospital, the Massachusetts Department of Public Health, the Boston Health Care for the Homeless Program, and elsewhere describes the early introduction and spread of SARS-CoV-2 in the Boston area and beyond.

The researchers analyzed 772 SARS-CoV-2 genomes from local cases, providing a window into the amplification of transmission in an urban setting — including the impact of superspreading events. One such event led to extensive local, national, and international spread.

To learn more about the work, we spoke with co-corresponding authors , an infectious diseases physician at Massachusetts General Hospital and postdoctoral researcher at the ӳ����ý, Pardis Sabeti, ӳ����ý member and professor at Harvard University, and Bronwyn MacInnis, director of pathogen genomic surveillance in the ӳ����ý’s Infectious Disease and Microbiome Program.

Q: What is the most significant finding of this paper?

The paper is one of the most comprehensive analyses of how the pandemic unfolded in a hard-hit urban area, and shows us how readily SARS-CoV-2 can spread, even between apparently disparate communities. Perhaps most importantly, we show how superspreading events shaped the trajectory of the local epidemic, and show the devastating consequences these events can have — far beyond the gatherings themselves.

Approximately one-third of the genomes in our dataset were linked to downstream spread from a single introduction, which was amplified by superspreading at a conference, subsequent community transmission, and travel. Not only did this event impact Massachusetts, but it also sent viruses to at least 29 other states and 9 countries, resulting in hundreds of thousands of infections.

Q: How can genomic data continue to help us understand how SARS-CoV-2 is spreading?

Viral genomic data allows us to identify transmission links and patterns that are missed by contact tracing and conventional epidemiology, which are especially challenging approaches given the continued scale of the pandemic. This kind of data allows us to identify sources of infections, whether these are routes by which viruses continue to enter the state from elsewhere or, at the local level, hotspots that continue to seed infections in the community.

It can also help support “cluster investigations” — identifying outbreaks in our hospitals, schools, and workplaces, and helping public health teams determine the best interventions to stop these. Additionally, genomic data is critical to surveillance of diagnostic and vaccine efficacy, by providing an early warning of viruses that may be evolving resistance in response to these selective pressures. 

Q: What are your main takeaways from this work?

One of the surprising things is the extent to which viruses can spread so quickly, particularly when younger, healthier people are involved who don’t realize they are infected. When you look at death rates associated with the two main superspreading events we studied, clearly the situation in the nursing facility was the most devastating in terms of the event itself: nearly 25 percent of residents died after testing positive for SARS-CoV-2. But when you look at the cases that resulted overall, the conference had the greatest impact by far. (We were not able to measure deaths associated with these cases.)

Therefore, the ultimate impact to society can paradoxically be greater when chains of transmission involve younger, healthier, and more mobile populations. That has important implications for the precautions people need to take, even after vaccination.

Q: What implications do these conclusions have for the current vaccine rollout?

Although the current vaccines seem to be very effective at preventing symptoms of disease, we don’t yet know if they prevent infection by the virus or onward spread to others. So it’s possible that people who have been vaccinated could still be infected, carrying and transmitting the virus, without feeling sick. Until we know more, it is critical that those who have been vaccinated still wear masks, practice physical distancing, and take other precautions to avoid spread. Otherwise we could see a reprise of the conditions that led to the superspreading events that sent viruses throughout our city, across the country, and around the world.

Q: How can we make genomic epidemiology a part of real-time infection control at the city, state, or national level?

Genomic epidemiology is a powerful tool that helps public health officials respond to outbreaks, identify sources of infection and routes of transmission, and detect diagnostic and vaccine “escape” mutations at the earliest possible stage. The approach is already being used to inform infection control and public health decision-making in many contexts, including through our partnerships with local hospitals and institutions, the MA Department of Public Health, and the CDC.

We expect this field will continue to grow as the value of genomic data is demonstrated and as we can more seamlessly connect SARS-CoV-2 samples to sequencing capacity in academic centers and public health labs in real time. The faster the genomic data is available, the more informed and effective the response to the pandemic will be. 
 

Paper(s) cited:

Lemieux JE, Siddle KJ, et al. . Science. Online December 10, 2020. DOI: 10.1126/science.abe3261

Study authors: Jacob Lemieux (ӳ����ý, MGH), Katherine Siddle (ӳ����ý, Harvard), Bennett Shaw (ӳ����ý, MGH), Christine Loreth (ӳ����ý), Stephen Schaffner (ӳ����ý, Harvard), Adrianne Gladden-Young (ӳ����ý), Gordon Adams (ӳ����ý), Timelia Fink (MA Department of Public Health), Christopher Tomkins-Tinch (ӳ����ý, Harvard), Lydia Krasilnikova (ӳ����ý, Harvard), Katherine DeRuff (ӳ����ý), Melissa Rudy (ӳ����ý), Matthew Bauer (ӳ����ý, Harvard), Kim Lagerborg (ӳ����ý, Harvard), Erica Normandin (ӳ����ý, Harvard), Sinéad Chapman (ӳ����ý), Steven Reilly (ӳ����ý, Harvard), Melis Anahtar (MGH), Aaron Lin (ӳ����ý, Harvard), Amber Carter (ӳ����ý), Cameron Myhrvold (ӳ����ý, Harvard), Molly Kemball (ӳ����ý, Harvard), Sushma Chaluvadi (ӳ����ý), Caroline Cusick (ӳ����ý), Katelyn Flowers (ӳ����ý), Anna Neumann (ӳ����ý), Felecia Cerrato (ӳ����ý), Maha Farhat (Harvard, MGH), Damien Slater (MGH), Jason Harris (MGH, Harvard), John Branda (MGH), David Hooper (MGH), Jessie Gaeta (Boston Health Care for the Homeless Program), Travis Baggett (Boston Health Care for the Homeless Program, MGH, Harvard), James O'Connell (Boston Health Care for the Homeless Program, MGH, Harvard), Andreas Gnirke (ӳ����ý), Tami Lieberman (ӳ����ý, MIT), Anthony Philippakis (ӳ����ý), Meagan Burns (MA Department of Public Health), Catherine Brown (MA Department of Public Health), Jeremy Luban (ӳ����ý, UMass), Edward Ryan (MGH, Harvard), Sarah Turbett (MGH, Harvard), Regina LaRocque (MGH, Harvard), William Hanage (Harvard), Glen Gallagher (MA Department of Public Health), Lawrence Madoff (MA Department of Public Health, UMass), Sandra Smole (MA Department of Public Health), Virginia Pierce (MGH, Harvard), Eric Rosenberg (MGH), Pardis Sabeti (ӳ����ý, Harvard), Daniel Park (ӳ����ý), Bronwyn MacInnis (ӳ����ý, Harvard)