COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks

The COVID-19 pandemic has posed grand challenges to policy makers, raising major social conflicts between public health and economic resilience. Policies such as closure or reopen of businesses are made based on scientific projections of infection risks obtained from infection dynamics models. While most parameters in infection dynamics models can be set using domain knowledge of COVID-19, a key parameter - human mobility - is often challenging to estimate due to complex social contexts and limited training data under escalating COVID-19 conditions. To address these challenges, we formulate the problem as a spatio-temporal data generation problem and propose COVID-GAN, a spatio-temporal Conditional Generative Adversarial Network, to estimate mobility (e.g., changes in POI visits) under various real-world conditions (e.g., COVID-19 severity, local policy interventions) integrated from multiple data sources. We also introduce a domain-constraint correction layer in the generator of COVID-GAN to reduce the difficulty of learning. Experiments using urban mobility data derived from cell phone records and census data show that COVID-GAN can well approximate real-world human mobility responses, and that the proposed domain-constraint based correction can greatly improve solution quality.