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We create a network model to study the spread of an epidemic throughphysically proximate and accidental daily human contacts in a city, andsimulate outcomes for two kinds of agents - poor and non-poor. Undernon-intervention, peak caseload is maximised, but no differences are observedin infection rates across poor and non-poor. Introducing interventions tocontrol spread, peak caseloads are reduced, but both cumulative infection ratesand current infection rates are systematically higher for the poor than fornon-poor, across all scenarios. Larger populations, higher fractions of poor,and longer durations of intervention are found to progressively worsen outcomesfor the poor; and these are of particular concern for economically vulnerablepopulations in cities of the developing world. Addressing these challengesrequires a deeper, more rigorous understanding of the relationships betweenstructural poverty and epidemy, as well as effective utilization of extantcommunity level infrastructure for primary care in developing cities. Finally,improving iniquitous outcomes for the poor creates better outcomes for thewhole population, including the non-poor.

Poverty in the time of epidemic: A modelling perspective