PopNet: Real-Time Population-Level Disease Prediction with Data Latency

TL;DR

PopNet is a real-time disease prediction model that effectively incorporates data latency and updates, significantly improving accuracy over existing methods by modeling spatial-temporal effects with hybrid neural networks.

Contribution

This work introduces PopNet, a novel model that captures data latency effects in real-time disease prediction using dual systems and latency-aware attention mechanisms.

Findings

Achieves up to 47% lower RMSE compared to baselines.

Achieves up to 24% lower MAE compared to baselines.

Outperforms existing spatial-temporal prediction models.

Abstract

Population-level disease prediction estimates the number of potential patients of particular diseases in some location at a future time based on (frequently updated) historical disease statistics. Existing approaches often assume the existing disease statistics are reliable and will not change. However, in practice, data collection is often time-consuming and has time delays, with both historical and current disease statistics being updated continuously. In this work, we propose a real-time population-level disease prediction model which captures data latency (PopNet) and incorporates the updated data for improved predictions. To achieve this goal, PopNet models real-time data and updated data using two separate systems, each capturing spatial and temporal effects using hybrid graph attention networks and recurrent neural networks. PopNet then fuses the two systems using both spatial and temporal latency-aware attentions in an end-to-end manner. We evaluate PopNet on real-world disease datasets and show that PopNet consistently outperforms all baseline disease prediction and general spatial-temporal prediction models, achieving up to 47% lower root mean squared error and 24% lower mean absolute error compared with the best baselines.