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The COVID-19 pandemic has shed light on how the spread of infectious diseasesworldwide are importantly shaped by both human mobility networks andsocio-economic factors. Few studies, however, have examined the interaction ofmobility networks with socio-spatial inequalities to understand the spread ofinfection. We introduce a novel methodology, called the Infection Delay Model,to calculate how the arrival time of an infection varies geographically,considering both effective distance-based metrics and differences in regions'capacity to isolate -- a feature associated with socioeconomic inequalities. Toillustrate an application of the Infection Delay Model, this paper integrateshousehold travel survey data with cell phone mobility data from the S\~ao Paulometropolitan region to assess the effectiveness of lockdowns to slow the spreadof COVID-19. Rather than operating under the assumption that the next pandemicwill begin in the same region as the last, the model estimates infection delaysunder every possible outbreak scenario, allowing for generalizable insightsinto the effectiveness of interventions to delay a region's first case. Themodel sheds light on how the effectiveness of lockdowns to slow the spread ofdisease is influenced by the interaction of mobility networks andsocio-economic levels. We find that a negative relationship emerges betweennetwork centrality and the infection delay after lockdown, irrespective ofincome. Furthermore, for regions across all income and centrality levels,outbreaks starting in less central locations were more effectively slowed by alockdown. Using the Infection Delay Model, this paper identifies and quantifiesa new dimension of disease risk faced by those most central in a mobilitynetwork.

Impact of Network Centrality and Income on Slowing Infection Spread after Outbreaks