Leveraging artificial intelligence for pandemic management: Case of COVID-19 in the United States

Abstract

The COVID-19 pandemic revealed significant limitations in traditional approaches to analyzing time-series data that use one-dimensional data such as historical infection rates. Such approaches do not capture the complex, multifactor influences on disease spread. This paper addresses these challenges by proposing a comprehensive methodology that integrates multiple data sources, including community mobility, census information, Google search trends, socioeconomic variables, vaccination coverage, and political data. In addition, this paper proposes a new cross-learning (CL) methodology that allows for the training of machine learning models on multiple related time series simultaneously, enabling more accurate and robust predictions. Applying the CL approach with four machine learning algorithms, we successfully forecasted confirmed COVID-19 cases 30 days in advance with greater accuracy than the traditional ARIMAX model and the newer Transformer deep learning technique. Our findings identified daily hospital admissions as a significant predictor at the state level and vaccination status at the national level. Random Forest with CL was very effective, performing best in 44 states, while ARIMAX outperformed in seven larger states. These findings highlight the importance of advanced predictive modeling in resource optimization and response strategy development for future health emergencies.