Fig. 5: A detailed example of how evictions might affect SARS-CoV-2 transmission in the city of Philadelphia, Pennsylvania, USA.

a Map of Philadelphia, with each zip code colored by the cluster it was assigned to. Properties of clusters are Table 2 and Supplementary Tables 1, 2. b Schematic of our model for inequalities within the city. Each cluster is modeled as a group of households, and the eviction rate and ability to adopt social distancing measures vary by cluster. c Simulated cumulative percent of the population infected over time, by cluster, in the absence of evictions. Data points from seroprevalence studies in Philadelphia or Pennsylvania: x⁵⁰, +³⁵, triangle⁴⁹, square³⁶. d The projected daily incidence of new infections (7-day running average) with evictions at 5-fold the 2019 rate vs. no evictions. Shaded regions represent central 90% of all simulations. e Final epidemic size by Dec 31, 2020, measured as percent individuals who had ever been in any stage of infection. f The predicted increase in infections due to evictions through Dec 31, 2020, measured as the excess percent of the population infected (left Y-axis) or the number of excess infections (right Y-axis). g Relative risk of infection by Dec 31, 2020, for residents compared by neighborhood. h–i Relative risk of infection by Dec 31, 2020, in the presence vs. absence of evictions, for individuals who merged households due to evictions (“Doubled-up”, h) and for individuals who kept their pre-epidemic household (“Other households”, i). Data in c, e–i showed as median values with interquartile ranges across simulations.