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Human mobility estimation is crucial during the COVID-19 pandemic due to itssignificant guidance for policymakers to make non-pharmaceutical interventions.While deep learning approaches outperform conventional estimation techniques ontasks with abundant training data, the continuously evolving pandemic poses asignificant challenge to solving this problem due to data nonstationarity,limited observations, and complex social contexts. Prior works on mobilityestimation either focus on a single city or lack the ability to model thespatio-temporal dependencies across cities and time periods. To address theseissues, we make the first attempt to tackle the cross-city human mobilityestimation problem through a deep meta-generative framework. We propose aSpatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model thatestimates dynamic human mobility responses under a set of social and policyconditions related to COVID-19. Facilitated by a novel spatio-temporaltask-based graph (STTG) embedding, STORM-GAN is capable of learning sharedknowledge from a spatio-temporal distribution of estimation tasks and quicklyadapting to new cities and time periods with limited training samples. The STTGembedding component is designed to capture the similarities among cities tomitigate cross-task heterogeneity. Experimental results on real-world data showthat the proposed approach can greatly improve estimation performance andout-perform baselines.

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19