  We create a network model to study the spread of an epidemic through
physically proximate and accidental daily human contacts in a city, and
simulate outcomes for two kinds of agents - poor and non-poor. Under
non-intervention, peak caseload is maximised, but no differences are observed
in infection rates across poor and non-poor. Introducing interventions to
control spread, peak caseloads are reduced, but both cumulative infection rates
and current infection rates are systematically higher for the poor than for
non-poor, across all scenarios. Larger populations, higher fractions of poor,
and longer durations of intervention are found to progressively worsen outcomes
for the poor; and these are of particular concern for economically vulnerable
populations in cities of the developing world. Addressing these challenges
requires a deeper, more rigorous understanding of the relationships between
structural poverty and epidemy, as well as effective utilization of extant
community level infrastructure for primary care in developing cities. Finally,
improving iniquitous outcomes for the poor creates better outcomes for the
whole population, including the non-poor.
