TDEFSI
Abstract
Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present T heory-guided D eep Learning-based E pidemic F orecasting with S ynthetic I nformation (TDEFSI), 1 an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yields accurate high-resolution spatiotemporal forecasts using low-resolution time-series data.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 29, 2020
- Source ID
- 10.1145/3380971
Entities
People
- Jiangzhuo Chen
- Lijing Wang
- Madhav Marathe
Organizations
- Center for Information Technology
- Defense Threat Reduction Agency
- University of Virginia