A Multilateral Attention-enhanced Deep Neural Network for Disease Outbreak Forecasting: A Case Study on COVID-19

TL;DR

This paper introduces a multilateral attention-enhanced neural network model that integrates multiple data sources to improve COVID-19 outbreak forecasting accuracy, demonstrating superior performance over existing methods.

Contribution

The study presents a novel multilateral attention mechanism within a GRU model and curates a comprehensive multi-source COVID-19 dataset for enhanced outbreak prediction.

Findings

The proposed model outperforms existing techniques in RMSE and MAE metrics.

Attention mechanisms improve the model's ability to follow pandemic trajectories.

Multi-source data integration enhances forecasting accuracy.

Abstract

The worldwide impact of the recent COVID-19 pandemic has been substantial, necessitating the development of accurate forecasting models to predict the spread and course of a pandemic. Previous methods for outbreak forecasting have faced limitations by not utilizing multiple sources of input and yielding suboptimal performance due to the limited availability of data. In this study, we propose a novel approach to address the challenges of infectious disease forecasting. We introduce a Multilateral Attention-enhanced GRU model that leverages information from multiple sources, thus enabling a comprehensive analysis of factors influencing the spread of a pandemic. By incorporating attention mechanisms within a GRU framework, our model can effectively capture complex relationships and temporal dependencies in the data, leading to improved forecasting performance. Further, we have curated a well-structured multi-source dataset for the recent COVID-19 pandemic that the research community can utilize as a great resource to conduct experiments and analysis on time-series forecasting. We evaluated the proposed model on our COVID-19 dataset and reported the output in terms of RMSE and MAE. The experimental results provide evidence that our proposed model surpasses existing techniques in terms of performance. We also performed performance gain and qualitative analysis on our dataset to evaluate the impact of the attention mechanism and show that the proposed model closely follows the trajectory of the pandemic.