When the coronavirus pandemic hit the United States earlier this year, scientific models forecasting hundreds of thousands of deaths were met by some people with derision.
Those models have been unfortunately vindicated. And they're providing fresh warnings that a recent uptick in cases could mean that the U.S. death toll could almost double in the next four months.
“If we go back to March, at that time, we were saying if this thing is not handled very carefully, we could end up with 200,000 or 300,000 deaths,” said Alessandro Vespignani, a coronavirus modeler and director of Northeastern University’s Network Science Institute. “At that time, everyone was saying that’s impossible. I think we should use that perspective now, especially when we think about the future.”
After beating back an initial wave of coronavirus infections, some countries in Europe are finding themselves in familiar territory: facing a spike in new cases and weighing which restrictions could help drive those numbers down. In the U.S., after a brief dip earlier this month, the number of new cases daily is creeping up again. Since Sept. 18, the seven-day average of new Covid-19 cases in the country has not fallen below 40,000 per day, according to an NBC News tally.
For coronavirus modelers, the writing has been on the wall. Many have watched with a mixture of horror and frustration as their projections of the pandemic’s evolution, and its potential death toll, have come to fruition.
Now, a widely cited model developed by the Institute for Health Metrics and Evaluation at the University of Washington suggests that the U.S. could total more than 378,000 coronavirus deaths by January.
But infectious disease modeling can be a tricky science, and one that is easy to criticize for its uncertainties. Experts say coronavirus models have come a long way since the early days of the pandemic, to the point where some researchers are moving away from long-term projections and focusing instead on forecasts that can more accurately predict Covid-19 trends up to six weeks in the future.
Dr. Christopher Murray, director of the institute and a professor of health metrics sciences at the University of Washington, said his team’s model has undergone numerous refinements throughout the pandemic. Behavioral changes — such as diligent mask-wearing — could drive their projections for January down, but he also worries about fatigue settling in.
Murray added that this new trajectory can already be seen in some European countries, including Spain, France and the United Kingdom.
This is why modelers are hoping people heed their warnings about the coming weeks, when they say growing complacency and changing behaviors tied to the fall and winter seasons could result in a new wave of infections.
“I think some people think the worst is over,” he said. “That progressive decline in vigilance will fuel part of the fall and winter return.”
The University of Washington institute’s model, which isone of several that the Centers for Disease Control and Prevention uses to track the pandemic, has faced criticisms for often including high degrees of uncertainty which can lead to imprecise predictions. Early on, the model underestimated the number of Covid-19 fatalities nationwide, projecting that the U.S. could hit 60,415 deaths by the end of August.
Still, the model is updated frequently and refinements are made as data on case numbers, hospitalizations and a host of other factors become available. By June, the institute’s model was estimating that the U.S. death toll could hit 200,000 by Oct. 1, a projection that ended up being accurate to within two weeks.
But infectious disease models are never static, and there are several unknowns that could significantly alter the existing projections.
One such factor is how the virus’ spread may be affected by the changing seasons. There is no firm evidence to suggest that the coronavirus will be more or less transmissible in the fall and winter. Rather, it’s the effect that falling temperatures have on human behavior that has researchers concerned, particularly since cold weather will likely draw people indoors and make it difficult to practice social distancing.
“In the winter, people tend to stay inside, which could make it easier to transmit the disease,” said Sen Pei, an associate research scientist at Columbia University, who has done extensive Covid-19 modeling work. “But we still don’t know how the virus will perform in the winter.”
Pei said there were enormous challenges with modeling a novel coronavirus, but that after nine months of data from the pandemic, his team’s projections have become significantly more sophisticated. Yet, one of the most difficult things to predict in a model is also one of the most important factors that could change the outcome of an outbreak: how humans respond to the situation.
“It’s a fluid situation because people's behavior changes over time, which is essentially unpredictable,” Pei said.
This uncertainty is partly why Pei and other modelers avoid long-term projections like the institute’s model and focus instead on producing short-term outlooks for the next four to six weeks.
“Nobody really knows what’s going to happen past the next few weeks,” said Youyang Gu, a data scientist who runs a coronavirus model known as Covid-19 Projections. Gu, who doesn’t have a background in epidemiology or infectious disease modeling, designed a model that uses machine learning to “study” certain parameters that evolve with the pandemic, such as the virus’ reproduction number, or R-naught, which represents how contagious a disease is.
“We don’t rely on any implicit assumptions,” Gu said. “We look at the data and say: this is what we learned from what is happening.”
Gu said his model, which only runs forecasts until November, was able to predict that the surge in new cases in June and July would not subsequently lead to an equivalent spike in deaths on par with what the country experienced in March and April.
“We compared what happened in the U.S. to other places around the world and the data didn’t support deaths going up as quickly as cases,” Gu said. “We ended up peaking at about 1,000 deaths per day, which is obviously still very significant, but less than what a lot of people in the scientific community had been expecting.”
The shift away from long-term projections is a desire shared by other modelers, who say that long-term projections are often less accurate because they need to include a wide range of estimates to account for uncertainties. Between now and January, for instance, travel bans, lockdowns or other restrictions could be introduced that would dramatically alter long-term predictions.
This switch “allows us to get away from these scenario projections that we were initially doing and move closer to forecasting, which is the goal,” Shaun Truelove, an assistant scientist and modeling expert at the Johns Hopkins Bloomberg School of Public Health, said. “The forecasts are more understanding what is really going to happen given the situation, rather than this is what could happen.”
Vespignani likened it to weather forecasts, which are harder to nail down the further out they target. He said he hopes people will pay close attention to coronavirus forecasts, especially as the country braces for what could be an uptick in new cases in the coming weeks and months.
“We still have quite a run ahead of us,” he said. “We have to fight this battle because it’s not over.”