  Coronavirus disease (COVID-19), which was caused by SARS-CoV-2, has become a
global public health concern. A great proportion of the world needs to be
vaccinated in order to stop the rapid spread of the disease. In addition to
prioritising vulnerable sections of the population to receive the vaccine, an
ideal degree-based vaccination strategy uses fine-grained contact networks to
prioritise vaccine recipients. This strategy is costly and impractical due to
the enormous amount of specific contact information needed. It also does not
capture indirect famine or aerosol-based transmission. We recently proposed a
new vaccination strategy called Individual's Movement-based Vaccination (IMV),
which takes into account both direct and indirect transmission and is based on
the types of places people visit. IMV was shown to be cost-efficient in the
case of influenza-like diseases. This paper studies the application of IMV to
COVID-19 using its documented transmission parameters. We conduct large scale
computer simulations based on a city-wide empirical mobility dataset to
evaluate the performance and practicability of the strategy. Results show that
the proposed strategy achieves nearly five times the efficiency of random
vaccination and performs comparably to the degree-based strategy, while
significantly reducing the data collection requirements.

Social and Information Networks;Physics and Society