Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading.

The mitigation of an infectious disease spreading has recently gained considerable attention from the research community. It may be obtained by adopting sanitary measurements (e.g., vaccination, wearing masks), social rules (e.g., social distancing), together with an extensive vaccination campaign. Vaccination is currently the primary way for mitigating the Coronavirus Disease (COVID-19) outbreak without severe lockdown. Its effectiveness also depends on the number and timeliness of administrations and thus demands strict prioritization criteria. Almost all countries have prioritized similar classes of exposed workers: healthcare professionals and the elderly, obtaining to maximize the survival of patients and years of life saved. Nevertheless, the virus is currently spreading at high rates, and any prioritization criterion so far adopted did not account for the structural organization of the contact networks. We reckon that a network where nodes are people while the edges represent their social contacts may efficiently model the virus's spreading. It is known that tailored interventions (e.g., vaccination) on nodes may efficiently stop the propagation, thereby eliminating the "bridge edges." We then introduce such a model and consider both synthetic and real datasets. We present the benefits of a topology-aware versus an age-based vaccination strategy to mitigate the spreading of the virus. The code is available at https://github.com/mazzalab/playgrounds.