Spread of COVID-19 in urban neighbourhoods and slums of the developing world

Abstract

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 approx. 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.

Sentinel seroprevalence of SARS-CoV-2 in Gauteng Province, South Africa, August - October 2020

“…It has been shown that the COVID-19 caseload, per million population and per km 2 , is systematically higher in high-density areas compared with lower-density areas. [21] A limitation of the present study is that we do not have any data from private laboratories, which serve a different, wealthier population.…”

Sentinel seroprevalence of SARS-CoV-2 in the Gauteng province, South Africa August to October 2020

“…It has been shown that COVID-19 caseload per million population and per square kilometre are systematically higher in high density areas compared to lower density areas. [21] . A limitation of this study is that we do not have any data from private laboratories which serve a different, wealthier population.…”

Analysing Mental Health Problems of Adults During COVID Using a MIMIC Model: Evidence from Indian Metropolitan Cities

“…The negative relationship between economic stress and mental health is one of the "most consistently replicated ndings in the social sciences" [48]. Economic stress impacts mental health through multiple mechanisms-unemployment, nancial threat, indebtedness, economic hardship, volatility in income, deterioration in living standard and decreased levels of welfare support [49][50][51][52]. The psychological impact of economic stress increases under times of economic recession and macroeconomic shocks [49,52], manifesting itself in increased levels of depression, anxiety and depression [53].…”