Modeling and Forecasting Influenza Outbreaks: A Robust Laplace-ARDL Framework vs. Deep Learning LSTM for Epidemiological Surveillance
TL;DR
Empirical evidence is provided that it is vital to solve the leptokurtic distribution in infection data for obtaining stable forecasts and a new model called Robust Laplace-ARDL is proposed, which uses a Double Exponential (Laplace) distribution instead of the standard normal distribution to accommodate heavy-tailed distributions.
Abstract
Accurate forecasts of seasonal influenza are imperative to successfully manage public health resources. However, epidemiological time series data often show significant spiky volatility along with heavy-tailed distributions that do not satisfy the normality assumption required by traditional linear models. This paper proposes a new model called Robust Laplace-ARDL, which uses a Double Exponential (Laplace) distribution instead of the standard normal distribution to accommodate heavy-tailed distributions. Using 792 weekly observations (2005–2020) and benchmarking against a Long Short-term Memory (LSTM) model, the Laplace-ARDL ( p = 5 ) model reduces the mean square error (MSE) by 33.5 \% compared to the LSTM model. This paper provides empirical evidence that it is vital to solve the leptokurtic distribution in infection data for obtaining stable forecasts.