Poverty, slums, and epidemics: A modeling perspective

Sahasranaman, Anand; Jensen, Henrik Jeldtoft

We model the spread of an epidemic through physically proximate human contacts using an Erdos-Renyi random graph representing 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. When we introduce different forms of social distancing interventions, peak caseloads are reduced, but infection rates of the poor are systematically higher than for non-poor across all scenarios. Larger populations, higher fractions of poor, and greater intensity of interventions are found to progressively worsen outcomes for the poor, vis-a-vis the non-poor. Increasing population leads to higher infection rates among the poor, supporting concerns that households in large high-density settlements like slums amongst the most at-risk population groups during an outbreak. Related to this, increasing population of the urban poor is found to worsen infection outcomes of the poor, and given the expansion of developing cities into large metropolises, this is an issue of significant concern for public health and economic wellbeing. The poor are also found disproportionately affected by increasing length and intensity of social distancing measures, pointing to the immediate need for countries to have clear guidelines on how social distancing can be practiced in high density slum settlements, given local contexts. Sustainable solutions for the long-term will require concerted investment in environmental and physical infrastructure to improve living conditions. Finally, addressing these iniquitous outcomes for the poor creates better outcomes for the whole population, including the non-poor.