Socio-Economic Vulnerability to COVID-19: The Spatial Case of Greater Kampala Metropolitan Area (GKMA)

Abstract

COVID-19 has presented itself with an extreme impact on the resources of its epi-centres. In Uganda, there is uncertainty about what will happen especially in the main urban hub, the Greater Kampala Metropolitan Area (GKMA). Consequently, public health professionals have scrambled into resource-driven strategies and planning to tame the spread. This paper, therefore, deploys spatial modelling to contribute to an understanding of the spatial variation of COVID-19 vulnerability in the GKMA using the socioeconomic characteristics of the region. Based on expert opinion on the prevailing novel Coronavirus, spatially driven indicators were generated to assess vulnerability. Through an online survey and auxiliary datasets, these indicators were transformed, classified, and weighted based on the BBC vulnerability framework. These were spatially modelled to assess the vulnerability indices. The resultant continuous indices were aggregated, explicitly zoned, classified, and ranked based on parishes. The resultant spatial nature of vulnerability to COVID-19 in the GKMA sprawls out of major urban areas, diffuses into the peri-urban, and thins into the sparsely populated areas. The high levels of vulnerability (24.5% parishes) are concentrated in the major towns where there are many shopping malls, transactional offices, and transport hubs. Nearly half the total parishes in the GKMA (47.3%) were moderately vulnerable, these constituted mainly the parishes on the outskirts of the major towns while 28.2% had a low vulnerability.

Facing a Second Wave from a Regional View: Spatial Patterns of COVID-19 as a Key Determinant for Public Health and Geoprevention Plans

“…Regarding the relation between COVID-19 and socio-economic profile [ 19 , 20 , 21 , 22 ], our study reveals that there is the highest case concentration in areas with low income levels (up to 11,000 euros per household per year) and with a larger average size (mainly from 2 people per household). Thus, a much laxer behavior of cases are observed in sections with higher income levels and reduced average household sizes.…”

“…Social vulnerability and the configuration of depressed areas into the cities increase the differences in the incidence of COVID-19, a pattern of socio-spatial affection that some studies even link to the ethnic or racial component, in that these populations tend to be located in socially vulnerable areas and eventually they end up being more affected by morbidity and mortality [ 19 , 20 ]. Similarly, income is other of the variables that positively correlates with the incidence of COVID-19, allowing to limit neighborhoods with the highest incidence on the intra-urban scale and to disentangle other social variables that could be decisive [ 21 , 22 ].…”

A climate resilience research renewal agenda: learning lessons from the COVID-19 pandemic for urban climate resilience

“…Density is defined in physical terms and can bring community building, economic, environmental and health benefits (Credit, 2020). The extent to which high-density urban living inhibits wellbeing leading to overcrowding is a product of urban planning and service provision combined with demographic and household structure and human behaviour (Bamweyana et al, 2020;Hamidi et al, 2020;Peters, 2020) and is often closely associated with informality in lowincome cities (Satterthwaite et al, 2020). Those living in overcrowded conditions are also often placed at risk through dangerous or precarious employment.…”

Scalable Analysis of COVID-19 Spatiotemporal Patterns Based on Data Mining Tools: Using 3D Bins to Predict Short-time Focus Locations

“…This line of research is likely to be linked with other approaches addressed in works cited in the background, speci cally those aimed at analyzing the socioeconomic, demographic and functional framework of the areas that accumulate COVID-19 cases [16][17][18][19][20]. Thus, we will work to study the conditions and environment variables that occur in the different kinds of hotspots in order to nd social patterns that can be correlated or ultimately explain the spatial patterns in this paper presented.…”

“…Indeed, the predictions about COVID-19 spread are more fuzzies when spatial scale is more detailed and when the future prediction time is longer. Therefore, there are many studies that predict the pandemic evolution in global or national scales [14,15] or even researches that try to analyse indirectly the sprawl of the virus using the characteristics of main affected areas, such as: rent, economic activities, density or mobility among others [16][17][18][19][20].…”