  We study the spread of Covid-19 across neighbourhoods of cities in the
developing world and find that small numbers of neighbourhoods account for a
majority of cases (k-index~0.7). We also find that the countrywide distribution
of cases across states/provinces in these nations also displays similar
inequality, indicating self-similarity across scales. Neighbourhoods with slums
are found to contain the highest density of cases across all cities under
consideration, revealing that slums constitute the most at-risk urban locations
in this epidemic. We present a stochastic network model to study the spread of
a respiratory epidemic through physically proximate and accidental daily human
contacts in a city, and simulate outcomes for a city with two kinds of
neighbourhoods - slum and non-slum. The model reproduces observed empirical
outcomes for a broad set of parameter values - reflecting the potential
validity of these findings for epidemic spread in general, especially across
cities of the developing world. We also find that distribution of cases becomes
less unequal as the epidemic runs its course, and that both peak and cumulative
caseloads are worse for slum neighbourhoods than non-slums at the end of an
epidemic. Large slums in the developing world therefore contain the most
vulnerable populations in an outbreak, and the continuing growth of
metropolises in Asia and Africa presents significant challenges for future
respiratory outbreaks from perspectives of public health and socioeconomic
equity.
