Forecasting COVID-19 Vaccine Distribution in the United States, Japan, Taiwan, and China using the Auto-Regressive Integrated Moving Average (ARIMA) model

Abstract

Developed at unprecedented speeds, vaccines have thus far played a crucial role in slowing down the COVID-19 pandemic around the world. Therefore, it is an absolute necessity for countries to be able to accu-rately forecast the distribution of vaccines. This paper uses an Auto-Regressive Integrated Moving Average (ARIMA) model to analyze and forecast 30 days of COVID-19 vaccine distribution for the United States, Japan, Taiwan, and China. Specifically, for the United States and Japan, the predicted variable was the percent of the population that was fully vaccinated while the predicted variable for Taiwan and China was the total number of doses administered. The data used to fit our model was pulled from a publicly available dataset compiled from various sources around the world. For each country, the training data consisted of that country’s vaccination data from whenever they first administered vaccines until July 19, 2021. After fitting the model on the training data, the model was then tested against 30 days of data from July 20, 2021 to August 18, 2021. The paper found that the univariate ARIMA model was able to, on average, forecast the distribution of COVID-19 vaccines within 5% of the actual value for each country.