A two-step vaccination technique to limit COVID-19 spread using mobile data

Vaccination is one of the most effective methods to prevent the spread of infectious diseases, but due to limitations in vaccines' availability, especially when faced with a new disease such as COVID-19, not all individuals in the community can be vaccinated. A limited number of candidates should be selected when the supply of vaccines is limited. In this paper, a method is introduced to prioritize the individuals for vaccination in order to achieve the best results in preventing the spread of COVID-19. We divide this problem into two steps: vaccine allocation and targeted vaccination. In vaccine allocation, vaccines are allocated among different population. An algorithm is proposed by defining the maximization of the total immunity among populations as an optimization problem. The aim of the targeted vaccination step is to select the individuals in each population that when vaccinated, create the greatest reduction in the transmission paths of the disease. The contact tracing data for this step is obtained from wireless communication networks and is modeled using graph theory. A metric is presented for selection of the candidates, based on centrality metrics. Simulations indicate that a 30% drop in infection rate could be achieved compared to random vaccination.