Modeling and Forecast of COVID-19 Vaccinations Population Size in China: A Non-Clinical Study

Abstract

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.