Background During uncertainties associated with the COVID-19 pandemic, effectively improving people’s health literacy is more important than ever. Drawing knowledge maps of health literacy research through data mining and visualized measurement technology helps systematically present the research status and development trends in global academic circles. Methods This paper uses CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains. First, based on the production theory of scientific knowledge and the data mining of citations, the main bodies (country, institution and author) that produce health literacy knowledge as well as their mutual cooperation (collaboration network) are both clarified. Additionally, based on the quantitative framework of cocitation analysis, this paper introduces the interdisciplinary features, development trends and hot topics of the field. Finally, by using burst detection technology in the literature, it further reveals the research frontiers of health literacy. Results The results of the BC measures of the global health literacy research collaboration network show that the United States, Australia and the United Kingdom are the major forces in the current international collaboration network on health literacy. There are still relatively very few transnational collaborations between Eastern and Western research institutions. Collaborations in public environmental occupational health, health care science services, nursing and health policy services have been active in the past five years. Research topics in health literacy research evolve over time, mental health has been the most active research field in recent years. Conclusions A systematic approach is needed to address the challenges of health literacy, and the network framework of cooperation on health literacy at regional, national and global levels should be strengthened to further promote the application of health literacy research. In the future, we anticipate that this research field will expand in two directions, namely, mental health literacy and eHealth literacy, both of which are closely linked to social development and issues. The results of this study provide references for future applied research in health literacy.

BACKGROUNDSince its outbreak in December 2019, severe acute respiratory syndrome coronavirus-2, the virus responsible for the COVID-19 pandemic, has considerably affected the worldwide population. Health authorities and the medical community identify vaccines as an effective tool for managing public health.METHODSIn this study, the autoregressive integrated moving average (ARIMA) model built-in Python was adopted to establish the COVID-19 vaccination forecast model. In this study, the sample data were selected from the Our World in Data website. COVID-19 vaccinations administered daily in China from December 16, 2020 to March 21, 2021 were analyzed to establish an autoregressive integrated moving average (ARIMA) model.RESULTSThe built-in ARIMA module function of Python was used, and the optimum model was ARIMA (3, 2, 3) according to the established time series analysis. The analysis showed that the predicted COVID-19 vaccination uptake supplemented well with the actual values with a small relative error.CONCLUSIONSThis indicated that the ARIMA(3, 2, 3) model could be used to forecast the number of COVID-19 vaccinations in China.