Interacting contagious diseases like influenza and pneumonia follow the same complex spreading patterns as social trends, according to researchers in the U.S. The model suggested by the scientists could potentially lead to better tracking and intervention when multiple diseases spread through a population at the same time, and could feasibly shed light on the spread of coronavirus.
“The interplay of diseases is the norm rather than the exception,” says Laurent Hébert-Dufresne, a complexity scientist at the University of Vermont, who co-led the new research. “And yet when we model them, it’s almost always one disease in isolation … 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. The interactions can be biological or social in nature, but they all matter.”
Hébert-Dufresne, a professor of computer science, together with co-researchers Samuel Scarpino, PhD, at Northeastern University, and Jean-Gabriel Young, PhD, at the University of Michigan, report on their model in Nature Physics, in a paper titled, “Macroscopic patterns of interacting contagions are indistinguishable from social reinforcement.”
“… contagions never occur in a vacuum; instead, pathogens and ideas interact with each other and with externalities such as host connectivity, behavior, and mobility,” the team noted. Yet despite this interplay, “ … many biological contagions are still considered to be ‘simple’, where infectious individuals transmit to susceptible individuals independently of anything else occurring around the individuals.” In fact, simple and complex contagions tend to induce substantially different dynamics, the authors noted, and can lead to “incompatible conclusions about intervention strategies or risk.”
The researchers first began to compare biological contagions and social contagions in 2015 at the Santa Fe Institute, where Hébert-Dufresne was modeling how social trends propagate through reinforcement. The classic example of social reinforcement, according to Hébert-Dufresne, is “the phenomenon through which ten friends telling you to go see the new Star Wars movie is different from one friend telling you the same thing ten times.” Hébert-Dufresne and colleagues point out that, like multiple friends reinforcing a social behavior, the presence of multiple diseases makes an infection more contagious than it would be on its own.
They noted that biological diseases can reinforce each other through symptoms, for example, as in the case of a sneezing virus that helps to spread a second infection like pneumonia. Or its possible that one disease can weaken the host’s immune system, making the population more susceptible to a second, third, or additional contagion. “Consider for example the interaction between influenza and other respiratory pathogens—for example, Streptococcus pneumoniae, rhinoviruses, adenovirus and so on—which can interact in different ways: an individual with a compromised immune system due to one infection might be more susceptible to the other, or an individual with both infections might exhibit heightened symptoms and increased transmission rates,” they wrote. “A population can then build up a latent epidemic potential where many individuals would infect their susceptible neighbors if only a few of them were compromised by a second disease.”
And when diseases reinforce each other, they rapidly accelerate through the population, but will decline as they run out of new hosts. According to the researchers’ model, the same super-exponential pattern characterizes the spread of social trends, like viral videos, which are widely shared and then cease to be relevant after a critical mass of people have viewed them.
Their findings indicated that the same complex patterns that can arise for interacting diseases also appear when a biological contagion interacts with a social contagion, as in the example of a virus spreading in conjunction with an anti-vaccination campaign. The researchers cited a 2005 Dengue outbreak in Puerto Rico, and Hébert-Dufresne highlighted an additional example of a 2017 Dengue outbreak in Puerto Rico where failure to accurately account for the interplay of Dengue strains reduced the effectiveness of a Dengue vaccine. This then sparked an anti-vaccination movement— a social epidemic—that ultimately led to the resurgence of measles—a second biological epidemic. It’s a classic example of real-world complexity, where unintended consequences emerge from many interacting phenomena.
The team highlighted the recent upsurgence of measles in the context of the interaction social and biological mechanisms. In September 2016 WHO declared that measles had been eliminated from the Americas, but within the next two years an outbreak of the disease in Venezuela triggered an epidemic across South America, which is still ongoing, they wrote. Concurrently, the number of measles cases has increased in all but one of the World Health Organization regions. In the U.S. there has been 17 measles outbreaks, and there have been more than 80,000 cases in the E.U.
The majority of measles cases have occurred in unvaccinated individuals, and while there could be a myriad of reasons why individuals go unvaccinated—“from collapsing public health infrastructure and lack of access to vaccines to non-medical exemptions, for example, religious beliefs, and the spread of fraudulent science,” the team pointed out—underlying all of these mechanisms is “the coupled transmission of two contagions, one biological and one—or more— social.”
Although it is interesting to observe a universal spreading pattern across complex social and biological systems, Hébert-Dufresne notes that this complexity also presents a unique challenge. “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.” And as the authors concluded, “Future work should focus on leveraging our effective, complex contagion model to more broadly uncover known and unknown interactions across infectious diseases and social contagions.”
Hébert-Dufresne commented, “We hope this will open the door for more exciting models that capture the dynamics of multiple contagions. Our work shows that it is time for the disease modeling community to move beyond looking at contagions individually.”
