LAUSR

In this study, we use an effective word embedding model (word2vec) to systematically track ’vaccine hesitancy’ and ’logistical challenges’ associated with the Covid-19 vaccines, in the USA. To that effect, we use news articles from reputed media sources and create dictionaries to estimate different aspects of vaccine hesitancy and logistical challenges. Using machine learning and natural language processing techniques, we have developed (i) three sub-dictionaries that indicate vaccine hesitancy, and (ii) another dictionary for logistical challenges associated with vaccine production and distribution. Vaccine hesitancy dictionaries capture three aspects: (a) general vaccine related concerns, mistrusts, skepticisms, and hesitancy, (b) discussions on symptoms and side-effects, and (c) discussions on vaccine related physical effects. The dictionary on logistical challenges includes the words and phrases related to the production, storage, and distribution of vaccines. Our results show that over time, as vaccine developers complete different phase trials and get approval for their respective vaccines, the number of vaccine related news articles increases sharply. Accordingly, we also see a sharp increase in vaccine hesitancy related topics in news articles. However, in January 2021, there has been a decrease in the vaccine hesitancy score, which will give some relief to the health administrators and regulators. Our findings further show that as we get closer to the breakthrough of effective Covid-19 vaccines, new logistical challenges continue to rise, even in recent months.