Prediction of influenza-like illness incidence using meteorological factors in Kunming : deep learning model study.

BACKGROUND

The global incidence of Influenza-Like Illnesses (ILI) has demonstrated an overall increasing trend. In the context of climate change, it is imperative to conduct research on the impact of meteorological factors on epidemic prediction.

OBJECTIVES

To assess the potential of meteorological factors with Long Short-Term Memory (LSTM) models for improving ILI incidence prediction accuracy, providing a reference for the future development of related public health applicability.

METHODS

Data on ILI incidence from November 2017 to January 2022, along with corresponding meteorological data over the same period. Pearson correlation analysis was employed to validate the relationship between the meteorological data and ILI incidence. Various LSTM architectures to forecast ILI incidence. These models were tested both with and without incorporating the the meteorological data as an additional feature. Additionally, Kernel Attention Network (KAN) was introduced into the LSTM models to enhance their nonlinear learning capability.

RESULTS

The description of ILI incidence and meteorological show that all the related variables are characterized by certain periodic changes. After incorporating the meteorological data into the analysis, the Mean Absolute Percentage Error (MAPE) for predicting ILI incidence using LSTM and attention-based stacked LSTM was 46.31% and 30.74%. Additionally, the application of KAN to these models further enhanced their performance.

CONCLUSIONS

The study demonstrates that stacking layers within LSTM models and incorporating KAN can further enhance the representational capabilities of these models. These improvements suggest that by leveraging meteorological data and utilizing advanced LSTM architectures, those can achieve more accurate and reliable predictions of ILI incidence.