This paper aims at constructing a probabilistic node-level time-dependent contagious disease spreading model for coronavirus disease (COVID-19) pandemic which is called SEINRVseinr by introducing exposed and asymptomatic infectious state, imperfect vaccination, reinfected possibility and weighted undirected graph for social network into the traditional probabilistic node-level Susceptible-Infectious-Recovered (SIR) network model. This paper simulates the effectiveness of five vaccination strategies (including random base, degree-target base, random acquaintance, first-neighbor and second neighbor strategies) in random network, small world network and scale-free network. Compared with the benchmark model, the results show that random acquaintance strategy is efficient strategy and neighbors’ strategies perform better in certain interval.
