Effective vaccination strategy using graph neural network ansatz (2111.00920v3)

Abstract: The effectiveness of vaccination highly depends on the choice of individuals to vaccinate, even if the same number of individuals are vaccinated. Vaccinating individuals with high centrality measures such as betweenness centrality (BC) and eigenvector centrality (EC) are effective in containing epidemics. However, in many real-world cases, each individual has distinct epidemic characteristics such as contagion, recovery, fatality rate, efficacy, and probability of severe reaction to a vaccine. Moreover, the relative effectiveness of vaccination strategies depends on the number of available vaccine shots. Centrality-based strategies cannot take the variability of epidemic characteristics or the availability of vaccines into account. Here, we propose a framework for vaccination strategy based on graph neural network ansatz (GNNA) and microscopic Markov chain approach (MMCA). In this framework, we can formulate an effective vaccination strategy that considers the properties of each node, and tailor the vaccination strategy according to the availability of vaccines. Our approach is highly scalable to large networks. We validate the method in many real-world networks for network dismantling, the susceptible-infected-susceptible (SIS) model with homogeneous and heterogeneous contagion/recovery rates, and the susceptible-infected-recovered-dead (SIRD) model. We also extend our method to edge immunization strategy, which represents non-pharmaceutical containment measures such as travel regulations and social distancing.