In this talk we present an algorithm designed to recast a population (here that of the US) through the lens of county population's mobility patterns. The result is a reclustering of the US population in regions that are not geographically distinct, but instead have low inter-region travelling. Such subpopulations are now well-mixed from the perspective of an ODE model of SEIR-type, hence in-depth analyses of infection spread are much better supported. We highlight differences and similarities in the epidemic evolution of Covid-19 in 2020 in the US, following the population reclustering, and the interplay between population density and sizes of the clusters, differences between regional incidence (peaks) and sizes of reported incidence state-wide Mar-July of 2020, and their implications on localized NPI measures. Further, we look at ways to use the current Covid-19 based modelling to derive insights on the impact of regionality/clustering for different regions of the world. Joint work with: D. Lyver, M. Nica & E. Thommes (U. of Guelph) and C. Cot and G. Cacciapaglia (INRS & Univ. of Lyon).