The paper “A data-driven analysis of the impact of non-compliant individuals on epidemic diffusion in urban setting” has been published in Proceedings A.
We introduce a heterogeneous extension of the susceptible–infected–recovered (SIR) model distinguishing between ordinary and non-compliant individuals, who are more infectious and/or more susceptible.
By combining electoral data with recent findings on vaccine hesitancy, we obtain spatially heterogeneous distributions of non-compliance. Simulations show that even small fractions of non-compliant individuals can significantly increase epidemic size, accelerate peak timing, and generate spatially heterogeneous infection hotspots.