A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China

Abstract

Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments.