SIAM News Blog

The Coevolution of Infodemics and Epidemics in the Context of COVID-19

By Lina Sorg

The COVID-19 pandemic coincided with a surge of controversial information about disease transmission and effective mitigation strategies known as an infodemic. According to the World Health Organization, an infodemic is “an overabundance of information—some accurate and some not—that occurs during an epidemic. In a similar manner to an epidemic, it spreads between humans via digital and physical information systems. It makes it hard for people to find trustworthy sources and reliable guidance when they need it” [2]. During a minisymposium presentation at the 2024 SIAM Conference on the Life Sciences, which is currently taking place in Portland, Ore., Yi Jiang of Georgia State University combined infodemic and epidemic models with an evolutionary game theoretical framework to better understand the correlation between misinformation, vaccination, and disease transmission. “For social scientists, the COVID-19 pandemic was a unique, natural experiment that cuts across all cultures and socioeconomic groups,” Jiang said.

Figure 1. Transmission rates during the COVID-19 pandemic were closely correlated with a corresponding infodemic, which led to the spread of misinformation and behaved much like a virus. Public domain image.
Jiang’s work was inspired by the startling number of COVID-19-related deaths in the U.S., a country with a high standard of healthcare (see Figure 1). She noted that human behavior constitutes a large part of public health response. Prior to the availability of vaccines, mitigation strategies such as masks, social distancing, and hygiene served as important tactics to control the virus’ spread. However, not everyone followed the suggested protocols. “Distrust in the scientific expertise and health and government authorities drives consumers from ‘traditional’ sources to social media outlets,” Jiang said. “They are then more likely to be subject to misinformation.” Once vaccines entered the market, vaccine hesitancy became a persistent barrier to complete inoculation of the population due to a persistent lack of trust in science and policy.

Jiang began by examining the impact of masks on COVID-19 transmission in high-risk environments, such as the Diamond Princess cruise ship that made news headlines in February 2020 [1]. Of the 3,711 passengers on board the quarantined ship, 712 became infected with COVID-19 and 14 people died. The detailed case report in this uncommonly contained environment allows for a unique modeling opportunity. Using a copy of a traditional susceptible-infected-recovered (SIR) model for masking, Jiang investigated three factors that are associated with mask-wearing: population adherence, timing, and mask type. Simulations revealed a window of opportunity within the early stages of infection during which masking effectively limits the spread of COVID-19. “N95 masks are best, but even cloth masks can significantly reduce disease spread and death when compliance is above 75 percent,” Jiang said. “But getting millions of people to wear them consistently and properly is a far greater challenge.”

To better understand the situation, Jiang coupled behavioral infodemic-based dynamics with traditional epidemic dynamics in a highly simplified coevolution model that treats vaccine hesitancy as a social behavior that is subject to misinformation. She opted for a susceptible-infected-susceptible (SIS) framework—because individuals can get reinfected with COVID-19 multiple times—and split the population into “good” and “bad” susceptible and infected information carriers. Jiang also treated information itself much like a virus, in that good information carriers can become “infected” with misinformation upon exposure to bad carriers, and bad information carriers can switch their orientation to “good” with sufficient education. This net flow creates a steady state solution that also accounts for vaccination (see Figure 2).

Figure 2. A susceptible-infected-susceptible (SIS) framework of “good” and “bad” information carriers models the spread of misinformation during the COVID-19 pandemic. Image courtesy of Yi Jiang.
Next, Jiang incorporated vaccination-based game theory into her model to understand a good susceptible information carrier’s decision to vaccinate based on available information. She identified a number of parameters, including education (the rate of infection of good information to an infected bad carrier), misinformation (the rate of infection of bad information to a susceptible good carrier), the rate of virus infection from a susceptible bad carrier to an infected bad carrier, the rate of virus infection from an infected bad carrier to a susceptible good carrier, vaccine efficacy, disease recovery rate, perceived risk of vaccination relative to virus infection, and strength of one’s beliefs. “It’s a complicated system, and we see several different modes of dynamics,” Jiang said.

Three simple dynamical patterns emerge: a disease-free equilibrium (stable node), a high infection co-existence (stable focus), and simple periodic oscillations (stable limit cycle). Additional complex dynamics that are associated with mixed oscillations include low-frequency large outbreaks (stable oscillation), high-frequency mixed oscillation (unstable mixed mode oscillation), and low-frequency mixed oscillation (unstable mixed mode oscillation).

Jiang concluded her presentation by confirming that COVID-19’s corresponding infodemic drove the epidemic itself. “Stopping misinformation is necessary to stop disease spread,” she said. “The higher the misinformation, the higher the infection. It seems obvious, but we were able to find it in the dynamic space to show it happening.” In the future, Jiang hopes to add further complexities to her framework— such as immunity, geopolitical status, viral variants, and spatial structures—and perhaps even create a more complicated model with multiple layers of different networks.

[1] Baraniuk, C. (2020). What the Diamond Princess taught the world about COVID-19. BMJ, 369, m1632.
[1] Tangcharoensathien, V., Calleja, N., Nguyen, T., Purnat, T., D’Agostino, M., Garcia-Saiso, S., …, Briand, S. (2020). Framework for managing the COVID-19 infodemic: Methods and results of an online, crowdsourced WHO technical consultation. J. Med. Internet Res., 22(6), e19659.

Lina Sorg is the managing editor of SIAM News.
blog comments powered by Disqus