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Comparing how different populations have suffered under COVID-19 is a corepart of ongoing investigations into how public policy and social inequalitiesinfluence the number of and severity of COVID-19 cases. But COVID-19 incidencecan vary multifold from one subpopulation to another, including betweenneighborhoods of the same city, making comparisons of case rates deceptive. Atthe same time, although epidemiological heterogeneities are increasinglywell-represented in mathematical models of disease spread, fitting these modelsto real data on case numbers presents a tremendous challenge, as doesinterpreting the models to answer questions such as: Which public healthpolicies achieve the best outcomes? Which social sacrifices are most worthmaking? Here we compare COVID-19 case-curves between different US states, byclustering case surges between March 2020 and March 2021 into groups withsimilar dynamics. We advance the hypothesis that each surge is driven by asubpopulation of COVID-19 contacting individuals, and make detecting the sizeof that population a step within our clustering algorithm. Clustering revealsthat case trajectories in each state conform to one of a small number (4-6) ofarchetypal dynamics. Our results suggest that while the spread of COVID-19 indifferent states is heterogeneous, there are underlying universalities in thespread of the disease that may yet be predictable by models with reducedmathematical complexity. These universalities also prove to be surprisinglyrobust to school closures, which we choose as a common, but high social cost,public health measure.

Modeling Insights from COVID-19 Incidence Data: Part I -- Comparing COVID-19 Cases Between Different-Sized Populations