Covid-19 vaccine hesitancy and mega-influencers

Abstract

vaccines are widely available in the United States, yet our Covid-19 vaccination rates have remained far below 100%. Not only that, but CDC data shows that even in places where vaccine acceptance was proportionally high at the outset of the Covid-19 vaccination effort, that willingness has not necessarily translated into high rates of vaccination over the subsequent months. We model how such a shift could have arisen, using parameters in agreement with data from the state of Alabama. The simulations suggest that in Alabama, local interactions would have favored the emergence of tight consensus around the initial majority view, which was to accept the Covid-19 vaccine. Yet this is not what happened. We therefore add to our model the impact of mega-influencers such as mass media, the governor of the state, etc. Our simulations show that a single vaccine-hesitant mega-influencer, reaching a large fraction of the population, can indeed cause the consensus to shift radically, from acceptance to hesitancy. Surprisingly this is true even when the mega-influencer only reaches individuals who are already somewhat inclined to agree with them, and under the conservative assumption that individuals give no more weight to the mega-influencer than they would give to a single one of their friends or neighbors. Our simulations also suggest that a competing mega-influencer with the opposite view can shift the mean population opinion back, but cannot restore the tightness of consensus around that view. Our code and data are distributed in the ODyN (Opinion Dynamic Networks) library available at https://github.com/annahaensch/ODyN.

Unifying human infectious disease models and real-time awareness of population- and subpopulation-level intervention effectiveness

“…One such example is intervention adherence, such as vaccine uptake rate for a vaccination programme. Modellers may make the assumption of a fixed vaccine uptake rate, but omitted variables that may impact an individual's decision to get vaccinated include their initial opinion on the intervention strategy, the cost to the individual to adopt the intervention and their awareness of the intervention's effectiveness of disease control within their social contacts as well as the greater population [27]. Given human decision-making in response to infectious disease outbreaks can be influenced by multiple factors, there is a need to develop mathematical models of infectious disease dynamics that explicitly incorporate such mechanisms.…”