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.