The spread of the novel coronavirus has been a lesson for epidemiologists in the interplay between contagion of disease and contagion of misinformation. Until recently, however, many epidemiological models have failed to account for the ways that misinformation shapes the spread of disease.  In their op-ed for STAT, former SFI postdoctoral fellow Laurent Hébert-Dufresne (University of Vermont) and Vicky Chuqiao Yang, current Complexity Postdoctoral Fellow and Peters Hurst Scholar, argue that if scientists hope to develop better epidemiological models, they must grasp the complex interplay between social behavior and disease.

To illustrate their argument, Hébert-Dufresne and Yang turn to data from the 2019 measles epidemic that spread across the Philippines, wherein 40,000 people were infected and 500 died. As the authors explain, “the onset of the epidemic was largely driven by the spread of anti-vaccination sentiment, itself fueled by a dengue vaccine that failed to account for the interplay of dengue strains.” In short, the measles contagion took a path that was shaped significantly by social behavior and public misinformation.

For Hébert-Dufresne and Yang, “social communication and behaviors during an outbreak are just as important to public health as tests and diagnoses.” Scientists must seek data on these facets of epidemics if they are to model the complex path that epidemics take on the ground.

Read the article in STAT (April 7, 2020)