  Data-driven epidemic simulation helps better policymaking. Compared with
macro-scale simulations driven by statistical data, individual-level GPS data
can afford finer and spatialized results. However, the big GPS data, usually
collected from mobile phone users, cannot cover all populations. Therefore,
this study proposes a Small World Model, to map the results from the "small
world" (simulation with partially sampled data) to the real world. Based on the
basic principles of disease transmission, this study derives two parameters: a
time scaling factor to map the simulated period to the real period, and an
amount scaling factor to map the simulated infected number to the real infected
number. It is believed that this model could convert the simulation of the
"small world" into the state of the real world, and analyze the effectiveness
of different mobility restriction policies.
