29 Dec Tracking Virus Mutations Reveals Success of Stay-at-Home Orders
By Chris Barncard, University Communications, University of Wisconsin-Madison
A family tree of more than 200 variations in the virus that causes COVID-19 helps explain why two Wisconsin counties – just 75 miles apart, but far from the origins of the virus – had such different early experiences with the pandemic, and shows how well public health orders initially slowed the rate of infection.
By the end of April 2020, Dane County had counted 405 coronavirus infections and 19 deaths. Milwaukee County, with fewer than twice as many residents, had more than six times the infections (2,629) and 126 deaths.
There was little meaningful viral transmission between the two counties, according to an analysis of the genome sequences of virus samples collected from infected patients in Dane and Milwaukee counties published in November 2020 by University of Wisconsin-Madison researchers in the journal Nature Communications.
“These two communities, that are very close together and share a lot of cultural, political and economic ties, nonetheless had pretty different strains of virus circulating in them,” says Thomas Friedrich, UW School of Veterinary Medicine professor and an author of the new study. “That suggests that the stay-at-home orders, the sheltering in place and the non-pharmaceutical interventions that we put in place at that time were effective in preventing mixing of viral lineages. They kept the two separate.”
By tracking slight mutations in the virus’ genetic sequences, the researchers know the earliest introductions of the virus to Milwaukee County came from Asia and began spreading between locals, according to graduate student Gage Moreno, a coauthor of the study. Dane County, on the other hand, got its viral visitors largely from Europe, and in a greater number of distinct variations.
But, despite more opportunities to jump-start community transmission, Dane County managed to avoid Milwaukee’s fate. In fact, Dane County had the 12th COVID-19 case in the U.S., which arrived with a traveler from China on January 30, 2020, but didn’t spread to anyone else.
“Despite the variations, the biology of the virus is probably pretty much the same in Dane and Milwaukee counties,” Friedrich says. “There’s no suggestion that any of these mutations changed the transmissibility of the virus, for example.”
That points to other factors, and the most likely are the demographic and socioeconomic differences between Milwaukee and Dane counties. Milwaukee County’s population lives closer together, has a lower average income, is less likely to have access to health care, and is more likely to have medical conditions like obesity and diabetes – all of which could make people more susceptible to COVID-19 infection and more serious outcomes.
Human factors shifted in late March 2020 when the Wisconsin Department of Health Services’ “Safer at Home” order closed nonessential businesses and prohibited gatherings. By tracking the spread of different variations of the virus after the order was enacted, the researchers can tell how well it arrested spread by estimating the virus’ basic reproduction number, the number of new cases expected to arise from a single infected person.
“In Dane County, that number dropped by at least 40 percent, and in Milwaukee County closer to 70 percent,” says Kasen Riemersma, a postdoctoral researcher in Friedrich’s lab and a coauthor of the study, which also included contributors from Emory University, the Milwaukee Health Department and Middleton Memorial Veterans Hospital in Madison.
The fine detail on viral transmission – the flow of virus from one place or group of people to another, the way it reacts to changes in policy, and people’s habits – that can be gleaned from collecting genetic sequences from virus samples can be very useful to public health officials.
“Everyone should wear masks and everyone should avoid gatherings. Everyone should take these precautions,” Friedrich says. “But to target interventions, to really dig into what’s happening on different levels of infection, sequencing provides the data that supports that. We’d really like to see this become a routine part of outbreak investigations.”