2019冠状病毒病疫苗犹豫和超级影响者
Covid-19 vaccine hesitancy and mega-influencers
摘要：
新冠肺炎疫苗在美国广泛使用，但我们的新冠肺炎疫苗接种率仍远低于100%。不仅如此，CDC的数据显示，即使在新冠疫苗接种工作开始时疫苗接受率较高的地方，这种意愿也不一定会在随后的几个月里转化为高疫苗接种率。我们模拟了这种转变是如何产生的，使用的参数与阿拉巴马州的数据一致。模拟结果表明，在阿拉巴马州，当地的互动有利于围绕最初的多数人观点形成紧密的共识，即接受新冠疫苗。但事实并非如此。因此，我们在我们的模型中加入了大型影响者的影响，如大众媒体，州长等。我们的模拟表明，一个单一的疫苗犹豫不决的大型影响者，达到人口的很大一部分，确实可以导致共识从根本上转变，从接受到犹豫。令人惊讶的是，即使超级影响者只接触那些已经有点倾向于同意他们的人，并且在保守的假设下，个人对超级影响者的重视程度不会超过他们对他们的朋友或邻居的重视程度。我们的模拟还表明，一个持相反观点的竞争性超级影响者可以将平均人口观点转变回来，但无法恢复围绕该观点的共识的紧密性。我们的代码和数据分布在ODyN（意见动态网络）库中，可在https://github.com/annahaensch/ODyN上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
