# Leveraging universal and transfer learning models for influenza prediction in Thailand

## TL;DR

This paper presents a deep learning approach to predict influenza outbreaks in Thailand, using universal models and transfer learning to improve accuracy in data-limited regions.

## Contribution

The novel use of universal deep learning models and transfer learning for influenza prediction in data-scarce regions is introduced.

## Key findings

- Single hidden layer models with 128 nodes performed best in the universal framework.
- Transfer learning significantly outperformed baseline models in provinces with limited data.
- Incorporating domain-specific knowledge improved epidemic management strategies.

## Abstract

Influenza is a major respiratory disease that causes significant morbidity and mortality worldwide. Accurate predictions of influenza incidence enable public health organizations to monitor and prepare for outbreaks, ultimately reducing mortality and optimizing resource allocation. However, many countries, including Thailand, face challenges in generating accurate forecasts due to limited feature data in certain regions. To address this, we developed universal deep learning (DL)-based models to predict influenza incidence across multiple provinces in Thailand from 2010 to 2019. We evaluated various model configurations and implemented a feature selection process to enhance model generalizability and performance by ensuring equal contributions from multiple time series features. Our findings indicate that single hidden layer models with 128 nodes performed the best in the universal framework. To extend predictions to provinces without meteorological and PM10 data, we applied transfer learning (TL) using pre-trained models. The TL-based model, fine-tuned for each province, significantly outperformed baseline models trained solely on previous incidence, achieving the highest accuracy. Our results demonstrate the potential of universal DL and TL frameworks in forecasting influenza trends, even in limited data regions, and highlight the importance of incorporating domain-specific knowledge for robust epidemic management strategies.

The online version contains supplementary material available at 10.1038/s41598-026-37855-7.