Computer model offers insights on COVID-19 super-spreading

As the number of COVID-19 cases in the United States streaks toward 5 million, it may surprise you to learn that 80% of those who test positive for the disease never infect anyone else with the virus.

Instead, new research suggests the disease weaves its way, rapidly, into the population because of a confluence of events. They are largely driven by contact with people who happen, briefly, to be highly contagious in the wrong place, at the wrong time.

It is a phenomenon known as super-spreading, and experts at Seattle’s Fred Hutchinson Cancer Research Center are using complex computer simulations of viral transmission among individuals to gain a better understanding of how this virus, SARS-CoV-2, with an unknowing assist from super-spreaders, manages to wreak so much havoc in populations.

In research newly posted in medRxiv, a team led by Fred Hutch postdoctoral scientist Dr. Ashish Goyal, infectious diseases physician Dr. Joshua T. Schiffer and epidemiologist Dr. Bryan T. Mayer reports some surprising insights using a computer model that compares the behavior of COVID-19 with influenza.

While existing data shows that people who are infected with SARS-CoV-2 may shed tiny amounts of the virus for weeks, the model suggests that they usually only shed enough to be contagious for a brief period — likely only one or two days.

To gain a better understanding of COVID-19’s spread, the team built their computer model to simulate the transmission patterns of viruses, based on similar methods that Mayer and Schiffer have used to track the behavior of several herpesviruses. The goal was to simulate the infectious behavior of COVID-19 using thousands of scenarios based on different assumptions about factors that might influence transmission.

Among those assumptions were:

• Infectious dose: how many millions of viral particles are needed to actually infect a person.
• How steeply infection risk increases among people as the assumed dose goes up.
• The average number of times an infected person has a potential transmission exposure with an uninfected person per day
• A measure of whether that average number of exposures is constant or varies greatly from day to day.

After loading different numbers for these and other variables into their computers, the researchers ran approximately half a million simulations that — using the magic of computer algorithms — spat out thousands of predicted courses of infection. Think of them like strands of spaghetti tracking across a chart.

Next, they plotted the course of actual transmissions based on epidemiological data — more spaghetti — and found that only a few of the simulated scenarios matched that of the real-world data. At that point, the researchers could infer which of their assumptions about the virus were likely to be correct.

When people are shedding virus at their peak

The scientists also ran transmission scenarios for influenza and found some similarities and some differences that have implications for understanding how to prevent this new disease.

“Let’s say you have two people walk into a crowded, closed room, with poor ventilation,” Schiffer explained, “and one of those people has influenza, and one has SARS-CoV-2. Both are unfortunately shedding at the highest viral load possible. Our model shows the person with influenza will likely expose far fewer people to their virus within that crowded environment than the person with SARS-CoV-2.

“That’s what drives the results in our model. And to me, the most likely explanation for that would be a predisposition towards aerosolization — meaning the virus is physically dispersed over a larger area and perhaps for a longer duration of time with SARS-CoV-2 than with influenza.”

Another inference from the model: “Super-spreader events come from when people are shedding virus at their peak,” Schiffer said. That underscores the importance of knowing, if possible, how soon after exposure a person might reach their two-day window of contagiousness. In other words, most people who are infected with SARS-CoV-2 do not infect anybody, but many people have the potential to be super-spreaders.

“They have to show up in a crowded place, and they have to do so when they are shedding at a high viral load,” he said.

Dr. Dan Reeves, a research associate in Schiffer’s lab and co-author of the paper, said that there are some potentially reassuring findings from the computer simulations. One is that contagiousness lasts only a couple of days, so repeat testing after a positive result may not be necessary. Also, any early treatment that can reduce the viral load (the amount of virus measured from a swab sample) might have “an outsized impact on transmission reduction.”

Finally, said Reeves, because masks can help reduce the amount of virus, even slightly, during an exposure, “wearing a mask really will help!”