SARS-CoV-2 as seen through a transmission electron microscope

A transmission electron microscope image shows SARS-CoV-2, the virus that causes COVID-19, isolated from a patient in the US. Virus particles are shown emerging from the surface of cells cultured in the lab. The spikes on the outer edge of the virus particles give coronaviruses their name: crownlike. 


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For the most up-to-date news and information about the coronavirus pandemic, visit the WHO website.

Vicky Chuqiao Yang is living through the coronavirus pandemic for the second time. 

She’s been working from home in New Mexico ever since the Santa Fe Institute — where Yang is a post-doctoral researcher using mathematical models to investigate societal  complexities — shut down in mid-March due to the virus’ spread.

She had already witnessed the jarring process of closing down society earlier in the year through her parents back in her hometown of Wuhan, China.

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Yang at the Santa Fe Institute.


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In the early days of the outbreak in Wuhan, information about the situation was spotty and often inconsistent as officials censored initial reports of the virus’ rapid spread. All the while, Yang and her parents were keeping tabs on the epidemic from opposite sides of the globe via myriad social media rumors and news reports. 

“There were all these internet campaigns about how many people there are, untested, who have symptoms, who are obviously suffering; some people are dying; they [were] not counted in the official statistics,” Yang recalls.

She says celebrities and average people alike went online to share stories of infected people in need of medical help. This outcry helped lead to a change in policy, and officials in Hubei province began converting stadiums and convention centers into temporary treatment centers to contain infected individuals and keep them from circulating in the population at large. 

Yang and other researchers argue anecdotes like this show just how complicated the spread of COVID-19 is. Beyond coughs and contaminated surfaces, its movement also depends on the spread of information through the media and online, which can change human behavior and the trajectory of the epidemic. 

One way to think about this: Tracking the disease by simply checking numbers of confirmed cases and deaths and looking at the same graphs and curves day after day is like trying to catch an arsonist by looking up at the rising smoke from a burning building while the criminal scurries undetected across town to set another blaze. 

Another example: In January, a frantic campaign swept across China to convince people to cancel their travel plans for the Chinese New Year. The message was sent out across the media, and young people tried to convince their parents to stay home. 

“There were even arts and media campaigns that resembled Communist propaganda that the older generations might buy into more,” Yang says. 

The response to the COVID-19 pandemic in the US, Europe and elsewhere around the world has followed a similar pattern.

Mixed messages in the media about how contagious or serious the virus is and how it affects younger people, along with conspiracy theories, spread online, and the promise of unproven treatments touted even by the US president have all impacted how this pandemic has taken shape. 

“How people are talking about the disease influences how it’s going to spread,” says Laurent Hébert-Dufresne, an assistant computer science professor at the Vermont Complex Systems Center, adding that models used to track and predict the spread of COVID-19 don’t account for this.

“There’s an obvious problem here.”

Don’t leave out the memes

Hébert-Dufresne began looking into the impact of social messaging and communications on infectious disease outbreaks while at the Santa Fe Institute in 2015, on the heels of the 2014 Ebola outbreak in West Africa. Much of that work culminated in a paper published in Nature Physics in February.

The ways we tend to think about and predict the spread of infectious diseases like COVID-19 may be far too simplistic, the study argues.  

http://www.cnet.com/


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Planning and response for pandemics revolves around one key factor called the “basic reproduction number,” or R0 (pronounced “R naught”). The R0 value for an infectious disease is the expected number of cases that can be traced back to one individual case. COVID-19 is thought to have an R0 value somewhere between 1.4 and 5.7, meaning a person infected by the coronavirus that causes the disease can be expected to spread it, on average, to more than one person and perhaps more than five people. 

“Many biological contagions are still considered to be ‘simple,’ where infectious individuals transmit to susceptible individuals independently of anything else occurring around the individuals,” the paper reads. “Clearly, contagions never occur in a vacuum; instead, pathogens and ideas interact with each other and with externalities such as host connectivity, behavior and mobility.”

Hébert-Dufresne and co-authors Samuel Scarpino and Jean-Gabriel Young argue that the spread of infectious disease is far more complex than calculating an R0 value or the many models that rely on similar figures. 

Passing a virus like SARS-CoV-2 around the population involves a complicated network of interactions that can be nearly impossible to map. There are the person-to-person interactions we’re all trying to reduce through social distancing, but the virus also interacts with a person’s underlying health issues or potentially with other infections. As a result, it spreads in a more complex way, just like the way a meme or other bits of information (or disinformation) whip around the internet.

It’s no wonder we talk about things going “viral.” Viruses not only spread just like a viral meme, the spread of a virus is also influenced by viral memes.

Blind spots

The data sets and models that scientists and officials work with to try to track or predict the spread of COVID-19 are woefully incomplete or misunderstood, argues Scarpino, a physics professor at  Northeastern University in Boston.

“The way we see these diseases is through the data sets, and the social contagions (the spread of information about the disease) can bias the data sets and make it look like the disease is spreading in a certain way but it’s not necessarily actually spreading that way, it’s just how we see it.”

As long as behaviors driven by the spread of news, rumors, memes and disinformation affect who gets counted as a COVID-19 case and who doesn’t, Scarpino says, they need to be taken into account.

An example of this can be in seen one of the first cases of COVID-19 reported in the United States, a student in Boston who returned in January from traveling in China. The young person had very mild symptoms, and Scarpino argues they may have never been tested if not for the spread of information and fear about the new coronavirus creating a hyper-awareness that influenced the individual’s decision to get tested.

Now, however, the prevailing biases have shifted, and a lack of widespread testing is the main factor that biases how we understand the pandemic. 

Scarpino says it’s not being communicated just how complex it is to interpret the results of testing and the levels of uncertainty that are baked into models like the IHME (Institute for Health Metrics and Evaluation) projections of when COVID-19 will peak in different areas. 

“We don’t necessarily need to figure out how to solve for that complexity,” he says. “We’re just not at the point where it’s being communicated appropriately that you really can’t just look at the numbers and think we have a good understanding.”

He cites backlogs in getting test results back and other delays and inconsistencies in how testing is being handled across the country as just a few of the factors that blur the picture.

“We just don’t really have any sense for what’s going on in those data.”

So the spread of COVID-19 is more complex than the public discussion around the disease might lead you to believe. 

This is partly because models are just that. They can never capture the full reality of where a disease like COVID-19 will move tomorrow. Projections are therefore always going to be flawed, but Hébert-Dufresne says models can still help us make better-informed decisions. He’s hopeful investigating the shared physics between the spread of infectious disease and memes could help improve our projections of how diseases like COVID-19 will spread and be better contained.

“That’s the big hope for the future,” he says. “We’re going to come up with new generations of infectious disease models that allow us to take a lot more [social] interactions into account. … Those models are going to be very different than what we’ve been doing for the last 100 years.”