Single Model for Influenza Forecasting of Multiple Countries by Multi-task Learning

TL;DR

This paper introduces a multi-task learning model for influenza forecasting across multiple countries, effectively utilizing search query data and transfer learning to improve prediction accuracy over existing methods.

Contribution

The paper presents a novel multi-task learning approach that incorporates search queries and transfer learning for multi-country influenza forecasting.

Findings

Model significantly outperforms baselines in five countries.

Leveraging search queries improves forecast accuracy.

Multi-task learning enhances performance across countries.

Abstract

The accurate forecasting of infectious epidemic diseases such as influenza is a crucial task undertaken by medical institutions. Although numerous flu forecasting methods and models based mainly on historical flu activity data and online user-generated contents have been proposed in previous studies, no flu forecasting model targeting multiple countries using two types of data exists at present. Our paper leverages multi-task learning to tackle the challenge of building one flu forecasting model targeting multiple countries; each country as each task. Also, to develop the flu prediction model with higher performance, we solved two issues; finding suitable search queries, which are part of the user-generated contents, and how to leverage search queries efficiently in the model creation. For the first issue, we propose the transfer approaches from English to other languages. For the second issue, we propose a novel flu forecasting model that takes advantage of search queries using an attention mechanism and extend the model to a multi-task model for multiple countries' flu forecasts. Experiments on forecasting flu epidemics in five countries demonstrate that our model significantly improved the performance by leveraging the search queries and multi-task learning compared to the baselines.