Lines on charts can tell you something about the state of the Covid pandemic in the United States. Deaths: declining, even with the looming milestone of the millionth US death. Hospitalizations: at historically low levels, but ticking back up. Cases: rising, particularly in the Northeast, reliably a harbinger for the rest of the country.
What you can't do with those lines is use them to map your way forward—because at this point, we’ve reached the choose-your-own-adventure stage of the pandemic. Most mask mandates have been lifted. Testing programs have been cut back, here and in other countries. Congress has declined to fund big chunks of the White House’s Covid agenda. Knowing where you are at risk is more confusing than ever, and likely to get more challenging as predicted new variants arrive.
All the indicators suggest the US is likely poised for a new surge of Covid; in some parts of the country, that surge may already be arriving. But in our zeal to declare the pandemic over, we may have maneuvered ourselves into a position where it is now harder to detect a coming wave. “More and more, the relaxation of public health requirements, mandates, has placed responsibility on the individual and the employer,” says Saskia Popescu, an infectious-disease epidemiologist and an assistant professor at George Mason University. “But I've noticed that when we relax these mandates, we're doing that at times that are really inopportune, when case numbers are already increasing.”
And cases are increasing in the US. The seven-day moving average calculated by the US Centers for Disease Control and Prevention stood at 42,605 cases last week, which was 35 percent higher than one week earlier. The number of counties that score high and medium on the CDC’s “community levels” map both increased last week.
All of this is due to the Omicron variant that roared across the world last November, and even more to its fast-emerging sublineages. What most of us think of as Omicron is known scientifically as BA.1; that was supplanted in January by a newer version, BA.2. Omicron’s initial success and laser-fast spread were driven by its ability to work around the immune protections created by vaccination. Even though it triggered less-severe disease, it caused so many cases that it crushed hospitals. BA.2 did not leverage immune escape in the same manner BA.1 did, but it turned out to be more ferociously transmissible than its already-contagious predecessor. By mid-March, the World Health Organization reported that BA.2 had become the dominant strain worldwide.
Simultaneously, BA.3 emerged, then BA.4 and BA.5, rapidly displacing other variants in southern Africa and migrating to countries in Europe. Meanwhile, a variation on BA.2 with the technical designation BA.2.12.1 is surging as well. In a weekly assessment published Tuesday by the CDC, it now accounts for almost 29 percent of US cases.
These subvariants matter for two reasons: First, each is a sign that SARS-CoV-2 won’t be lifting its siege anytime soon. On Monday, evolutionary biologist Tom Wenseleers predicted “a significant wave every six months with significant mortality and morbidity.”
And secondly, this relentlessness has a biological objective. Think of the endlessly evolving coronavirus like a burglar trying to get into your house: twisting every doorknob, testing every window, looking for any gap that might let it enter. On a societal level, the protections we maintain against Covid have a lot of gaps now: Only two-thirds of the US population have received two doses of a Covid vaccine, and fewer than half have been boosted—though it’s this immunity tune-up that confers protection against Omicron and its relations.
There is no guarantee future variants will be milder than the first version of Omicron was. Virologists argue the opposite: In March, European researchers wrote in Nature that “the lower severity of Omicron is nothing but a lucky coincidence.”
All of this puts us in line for a surge, one that we don’t see coming. Since the beginning of the year, states have shuttered their PCR testing sites, the Department of Health and Human Services has changed the metrics for data it reports, and huge numbers of Americans have switched to at-home antigen tests, which do not require reporting results. A recent CDC report tried to quantify home test use, using data from public survey platforms, and found their use had tripled since the beginning of the Omicron wave. Rapid home tests are great for individuals, making people aware of when they are contagious and giving them a head start on launching antiviral treatment. But they fail on a societal level because most of the time, the test result isn’t handed off to be aggregated with anyone else’s data.
“With so many at-home tests being done, the pullback on publicly available PCR testing, and reduced access to testing in general, it's not surprising that we would have data gaps,” Popescu says. “That more and more people have access to antigen testing is great. But there’s not a good mechanism for using that data for surveillance.”
To do the most useful surveillance, you need more than a yes-no test result. You need a biological sample, the kind that the swabs for PCR tests retrieve. As PCR tests diminish, those samples become less available. Plus, the ability to extract genomic information from them, in order to characterize variants and track their evolution, differs with location and local investment. “There’s a large amount of variability of sequencing by state,” says Lee Harrison, a physician and professor of infectious diseases at the University of Pittsburgh School of Medicine. “Some states are sequencing north of 20 percent of their specimens. Some are way, way down.”
Some experts say it’s time to create new structures that will let us detect the virus’ movement and evolution before it slips from our grasp. One idea is to create a new surveillance program for Covid based on what the CDC has already built for flu. The flu’s arrival is somewhat predictable—but most people who experience symptoms during flu season simply assume they have it, and don’t seek testing to prove it. Since few test results are available, the CDC compensates with other overlapping data-collection schemes. Some involve re-analyzing the respiratory specimens that hospitals use to diagnose flu in admitted patients, looking for the virus type and its susceptibility to antivirals. Others collect reports of illnesses in nursing homes or symptoms recorded in outpatients to build nationwide portraits of flu-like illness.
“Something like what we do for flu is where we probably need to go,” says Kelly Wroblewski, director of infectious disease programs at the Association of Public Health Laboratories. “SARS-CoV-2 is not going to go away. It is going to be a virus that we have to monitor and pay attention to for the foreseeable future.”
Another idea, which Harrison’s team at Pitt has been trialing since before Covid struck, is to apply genome sequencing as broadly as possible, in as close to real time as possible, to samples taken from hospitalized patients. Sequencing routinely and running the results through machine learning, as Pitt’s hospital now does for some pathogens that cause hospital infections, allowed the team to detect unrecognized outbreaks within the hospital population, lifting the signal of transmission out of the background noise of random infections. Applying that method to other infections as well, Harrison said, could allow additional insight into patterns of spread. “I'm convinced that whole genome sequencing surveillance is going to have to become a routine part of US health care,” he says. “When you sequence, it brings the epidemiology to life.”
An overarching challenge of creating new surveillance methods is receiving results fast enough to act on them. Throughout the pandemic, scientists have been adding whatever genomic sequences they identify to an international database known as GISAID (the Global Initiative on Sharing Avian Influenza Data, which was its first purpose). That database is comprehensive, but not quick; depending on the jurisdiction the sample is coming from, it can take a month to get data added.
Wastewater surveillance, another novel tool, detects the presence of the virus regardless of whether people take tests, and it can flag a variant’s arrival more than a week before traditional tests do. It represents what a laboratory specialist would call a pooled sample; whatever virus lurks within it cannot be traced back to a patient. But the distribution of these systems, too, is patchy. The CDC collects data from almost 500 points of analysis, but most of them are clustered in only a few states, and 18 states have no sewage surveillance at all.
None of these tools are perfect. Some exist only at the small, pilot scale; others remain conceptual. And yet our existing tools for tracking Covid’s movement are being downgraded or put aside, thanks to changed government priorities and disinvestment. So for now we face a data gap—and the next surge may take us by surprise.
