  Covid-19 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.
