A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19

TL;DR

This paper introduces a hybrid spatio-temporal model combining SEIR and RNN on a graph to improve COVID-19 case predictions, capturing local trends and geographic effects for better accuracy.

Contribution

It develops a novel graph-based hybrid model (IeRNN) integrating SEIR-derived features and RNNs to enhance infectious disease forecasting accuracy.

Findings

Outperforms standard models like RNN, SEIR, and ARIMA in 1-day and 7-day forecasts.

Effectively captures local infection trends and geographic neighbor effects.

Provides reliable predictions for various reopening scenarios.

Abstract

As the COVID-19 pandemic evolves, reliable prediction plays an important role for policy making. The classical infectious disease model SEIR (susceptible-exposed-infectious-recovered) is a compact yet simplistic temporal model. The data-driven machine learning models such as RNN (recurrent neural networks) can suffer in case of limited time series data such as COVID-19. In this paper, we combine SEIR and RNN on a graph structure to develop a hybrid spatio-temporal model to achieve both accuracy and efficiency in training and forecasting. We introduce two features on the graph structure: node feature (local temporal infection trend) and edge feature (geographic neighbor effect). For node feature, we derive a discrete recursion (called I-equation) from SEIR so that gradient descend method applies readily to its optimization. For edge feature, we design an RNN model to capture the neighboring effect and regularize the landscape of loss function so that local minima are effective and robust for prediction. The resulting hybrid model (called IeRNN) improves the prediction accuracy on state-level COVID-19 new case data from the US, out-performing standard temporal models (RNN, SEIR, and ARIMA) in 1-day and 7-day ahead forecasting. Our model accommodates various degrees of reopening and provides potential outcomes for policymakers.