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Diseases shown to interact and spread like internet memes

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A University of Vermont study has found that some contagious diseases such as pneumonia and flu are transmitted in a similar manner to memes.

When diseases like flu or Ebola are modelled, they are typically treated as isolated pathogens with the forecast magnitude of an epidemic proportional to the rate of transmission. However, the Vermont researchers argue that the presence of even one extra contagion in a population can make the pattern of transmission far more complex.

“The interplay of diseases is the norm rather than the exception,” said Laurent Hébert-Dufresne, who led the research. “And yet when we model them, it’s almost always one disease in isolation.”

Under these complex dynamics, a tiny change in transmission rate can trigger significant changes in the forecast size of the epidemic. This has much in common with the spreading pattern social scientists have observed in the adoption of social trends, such as slang, internet memes and new technologies.

Hébert-Dufresne studied how social trends are spread through reinforcement (e.g. multiple friends adopting the same slang term or using the same internet meme) before he came to compare social and biological contagions. Just as how different friends adopting a new social behaviour makes a person more likely to adopt that behaviour, the multiple diseases reinforce each other and make an infection more contagious. For instance, a sneezing virus like the common cold can help spread pneumonia, or an infection could temporarily weaken the host’s immune system, making them more susceptible to further infections.  

According to the researchers’ model, the way diseases reinforce each other and accelerate through a population, before slowing as they run out of new hosts, follows the same “super-exponential” pattern as social trends, like viral content being shared online.

The researchers also found that these same super-exponential spreading patterns arose when biological and social contagions meet, such as a virus spreading in conjunction with an anti-vaccination campaign. Hébert-Dufresne's paper recounted the story of an outbreak of dengue fever in Puerto Rico in 2017, in which authorities made mistakes in accounting for the interplay of strains of the infection, reducing the effectiveness of a dengue vaccine. This in turn sparked an anti-vaccination movement, which reinforced a second biological contagion: measles.

“Looking at the data alone, we could observe this complex pattern and not know whether a deadly epidemic was being reinforced by a virus, or by a social phenomenon, or some combination,” said Hébert-Dufresne, acknowledging the challenge in interpreting these outbreaks. “We hope that this will open the door for more exciting models that capture the dynamics of multiple contagions.”

The Nature Physics study may even help understand the spread of the novel coronavirus covid-19, which appears to be highly contagious. “When making predictions, such as for the current coronavirus outbreak occurring in a flu season, it becomes important to know which cases have multiple infections and which patients are in the hospital with flu but scared because of coronavirus,” he continued.

“The interactions can be biological or social in nature, but they all matter.”

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