By: Christopher Sirota, CPCU
Science Daily has reported that researchers from University of Vermont, Northeastern University, and the University of Michigan have been considering newer, more complex pandemic modeling after identifying similar patterns in both biologic and social contagions.
According to the article, typical pandemic models estimate transmission of a disease in isolation. However, the real world may contain multiple diseases active throughout a population. To better understand the potential complexity of multiple diseases interacting within a population, the researchers reportedly noticed a comparable situation with social contagions, such as memes, innovative technologies, and slang language.
The article cites an example of so-called social reinforcement to explain as follows:
‘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.’
Like multiple friends reinforcing a social behavior, the presence of multiple diseases makes an infection more contagious that it would be on its own. Biological diseases can reinforce each other through symptoms, as in the case of a sneezing virus that helps to spread a second infection like pneumonia. Or, one disease can weaken the host's immune system, making the population more susceptible to a second, third, or additional contagion.
The researchers reportedly noted that when diseases interact, they can cause not only accelerated transmission but also accelerated disappearance as hosts disappear; this is reportedly similar to the social trend of viral videos.
Of interest, the researchers also reportedly identified the potential for biological contagions to interact with social contagions, thus resulting in a worsened situation. Per the article, one example would be a Dengue outbreak in 2017 in Puerto Rico, in which a vaccine with limited effectiveness ultimately fueled an anti-vaxxing campaign that subsequently resulted in the return of the measles.
One researcher opined that future pandemic modeling needs to better reflect real-world situations. Using the COVID-19 outbreak as an example, the researcher explained that:
'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 […].'