  Non-Pharmaceutical Interventions (NPIs), such as social gathering
restrictions, have shown effectiveness to slow the transmission of COVID-19 by
reducing the contact of people. To support policy-makers, multiple studies have
first modeled human mobility via macro indicators (e.g., average daily travel
distance) and then studied the effectiveness of NPIs. In this work, we focus on
mobility modeling and, from a micro perspective, aim to predict locations that
will be visited by COVID-19 cases. Since NPIs generally cause economic and
societal loss, such a micro perspective prediction benefits governments when
they design and evaluate them. However, in real-world situations, strict
privacy data protection regulations result in severe data sparsity problems
(i.e., limited case and location information). To address these challenges, we
formulate the micro perspective mobility modeling into computing the relevance
score between a diffusion and a location, conditional on a geometric graph. we
propose a model named Deep Graph Diffusion Infomax (DGDI), which jointly models
variables including a geometric graph, a set of diffusions and a set of
locations.To facilitate the research of COVID-19 prediction, we present two
benchmarks that contain geometric graphs and location histories of COVID-19
cases. Extensive experiments on the two benchmarks show that DGDI significantly
outperforms other competing methods.
