  Saving lives or economy is a dilemma for epidemic control in most cities
while smart-tracing technology raises people's privacy concerns. In this paper,
we propose a solution for the life-or-economy dilemma that does not require
private data. We bypass the private-data requirement by suppressing epidemic
transmission through a dynamic control on inter-regional mobility that only
relies on Origin-Designation (OD) data. We develop DUal-objective
Reinforcement-Learning Epidemic Control Agent (DURLECA) to search
mobility-control policies that can simultaneously minimize infection spread and
maximally retain mobility. DURLECA hires a novel graph neural network, namely
Flow-GNN, to estimate the virus-transmission risk induced by urban mobility.
The estimated risk is used to support a reinforcement learning agent to
generate mobility-control actions. The training of DURLECA is guided with a
well-constructed reward function, which captures the natural trade-off relation
between epidemic control and mobility retaining. Besides, we design two
exploration strategies to improve the agent's searching efficiency and help it
get rid of local optimums. Extensive experimental results on a real-world OD
dataset show that DURLECA is able to suppress infections at an extremely low
level while retaining 76\% of the mobility in the city. Our implementation is
available at https://github.com/anyleopeace/DURLECA/.
