Interrupted time series and machine learning with application to effect of Influenza Vaccine

Abstract

Interrupted time series analysis is being increasingly employed to assess the effects of extensive health interventions. Autocorrelation and seasonality are best captured but are not well captured by the simple implementation of the time series model like segmented regression, which is commonly used. An Autoregressive Integrated Moving Average (ARIMA) model presents an alternative approach to address these issues. In this study, the fundamental principles of ARIMA and LSTM models are expounded upon, along with their application in evaluating interventions at a population level, such as determining the effect of influenza vaccine administration. Considerations such as determining the impact shape, model selection process, transfer functions, loss functions, selection of batch sizes and epochs training the neural networks, evaluation metrics, and interpreting results are discussed. Additionally, detailed R and Python codes are provided for result replication. The application of ARIMA and LSTM predictive modeling is demonstrated through an analysis of influenza vaccination intervention to reduce the number of medically attended respiratory illnesses among children under five years. Precisely, from November 2019 to November 2021, an influenza vaccination demonstration project. In conclusion, ARIMA modeling and LSTM serve as valuable tools for assessing the effects of large-scale interventions when traditional methods are not applicable, given their ability to consider underlying patterns, autocorrelation, seasonality, and flexibility in modeling various impacts. Comparing the MAE and RMSE error results, LSTM outperformed the ARIMA model. Key terms: Interrupted time series analysis, Autoregressive integrated moving average models, LSTM, Intervention analysis