Crowded Cities: New Methodology in COVID-19 Risk Assessment

Abstract

In this paper, we provide a novel approach to distinguish livable urban densities from crowded cities and describe how this distinction has proved to be critical in predicting COVID-19 contagion hotspots in cities in low- and middle-income country. Urban population density—considered as the ratio of population to land area, without reference to floor space consumption or other measures of livability—can have large drawbacks. To address this drawback and distinguish between density and crowding, it is important to adjust for measures of floor space as well as open space and neighborhood amenities. We use a dataset on building heights, representative of cities worldwide, to measure densities based on floor area consumption per person as well as apply this measure to develop a COVID-19 hotspot predictive tool to help city leaders prioritize civic and medical resources during the pandemic. We conclude by outlining priority interventions that could enable city leaders and local governments to transform crowded cities into livable places.

Multi-Source Data and Machine Learning-Based Refined Governance for Responding to Public Health Emergencies in Beijing: A Case Study of COVID-19

“…Maxent machine learning models have also been widely used for the evaluation of regional epidemic risk levels [26,27]. In addition, a portion of research has also focused on a specific element for risk prediction, with building density [28] and human activity patterns [29] being used to determine and identify potential site transmission risk in cities.…”

Lights out: The economic impacts of Covid‐19 on cities globally

“…Bisin and Moro (2022) show that incorporating a spatial dimension into the now well‐known Susceptible, Infectious, and/or Recovered model reveals that city density is a crucial determinant of epidemic diffusion—since an affected person's number of contacts is proportional to density. This is especially the case in cities in lower‐income countries where density is often associated with crowding in the form of slums, as opposed to vertical development through the construction of taller buildings, and social distancing is difficult (Bhardwaj et al, 2020; Jedwab, Loungani, et al, 2021; Lall & Wahba, 2021).…”

“…7 To the best of our knowledge, our study is the first to examine the more immediate, short-run response of city trajectories to the outbreak, and how these vary across cities, at a global scale. In doing so, we build on countryspecific applications of nighttime lights data to study the economic impacts of the Covid-19 crisis on China (Elvidge et al, 2020;Liu et al, 2020), India (Beyer et al, 2023), and Morocco (Roberts, 2021). Our paper also contributes to the debate on the efficacy of NPIs in containing the negative health and economic impacts of pandemics (Barro, 2022;Correia et al, 2022;Demirgüç-Kunt et al, 2021).…”

Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya

“…A similar study aimed to identify high-risk locations for disease transmission in several cities (Cairo, Kinshasa and Mumbai), considering population density, building height and associated floor space area, as well as access to public toilets and water points. 6 , 7 A third study in Cape Town, South Africa, identified the small spacing between dwelling units in informal settlements as posing a challenge for physical distancing. 8 These studies have shown the intra-urban geographical variation in risk factors associated with community transmission, and highlighted informal settlements as potentially higher risk locations.…”