Fig. 4: Impact of evictions on COVID-19 epidemics in heterogeneous cities.

a Schematic of our model for inequalities within a city. The city is divided into a “high socioeconomic status (SES)” (purple) and a “low SES” (teal) neighborhood. Evictions only occur in the low SES area, and individuals living in this area are assumed to be less able to adopt social distancing measures, and hence have higher contact rates under interventions (90% vs. 80% reduction in external contacts during lockdown for 85% overall, 75% vs. 65% during a relaxation for 70% overall, and 65% vs. 55% during fall comeback for 60% overall). Before interventions, residents are equally likely to contact someone outside the household who lives within vs. outside their neighborhood. b Cumulative percent of the population infected over time, by neighborhood, in the absence of evictions. Error bars show interquartile ranges across simulations. c The projected daily incidence of new infections (7-day running average) with 1%/month evictions vs. no evictions. Shaded regions represent central 90% of all simulations. d Final epidemic size by Dec 31, 2020, measured as percent individuals who had ever been in any stage of infection, for the heterogeneous city as compared to a homogenous city with the same effective eviction rate and intervention efficacy. e 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). f Relative risk of infection by Dec 31, 2020, for residents of the low SES vs. high SES neighborhood. g The 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”) and for individuals who kept their pre-epidemic household (“Other households”). Data in b, (d–g) shown as median values with interquartile ranges across simulations.