State-of-the-art learning COVID-19 vaccine effectiveness using LSTM

Abstract

The effect of COVID-19 vaccines in reducing hospitalization risks was studied using the Long Short-Term Memory (LSTM) model. We first devised a dynamic environment using an LSTM that characterizes the impact of COVID-19 vaccine administrations on COVID-19 infections in the real-world setting from May 2021 to April 2023. Then, we generated hypothetical subjects with various vaccination profiles (e.g., all subjects received or not received the booster vaccine, or all subjects had followed the vaccine policy) and predicted their counterfactual outcomes based on the LSTM to make inferences on the vaccine effectiveness and estimate the population-averaged risk of infection if there was full compliance for the vaccine policy. Our findings confirm that booster doses significantly reduced the risk of COVID-19 hospitalization while bivalent booster had similar or slightly better effectiveness than the monovalent booster. Additionally, our analysis highlights the importance of adhering to vaccine policies in effectively reducing the risk of hospitalizations. Our study contributes to understanding the dynamics of vaccine efficacy and supports informed decision-making in public health strategies.

Keywords

• COVID-19 vaccines
• Causal inference
• LSTM
• Electronic health records (EHR)
• Vaccine effectiveness
• Vaccine policy compliance