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 socio-economic 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. The spatial approach presented in this paper contributes to providing a rapid assessment of the socio-economic vulnerability based on administrative decision units-parishes. This essentially equips the public health domain with the right diagnosis to subject the highly exposed and vulnerable communities to regulatory policy, increase resilience incentives in low adaptive areas and optimally deploy resources to avoid the emancipation of high susceptibility areas into an epicentre of Covid-19.
Highlights
This paper, deploys spatial modelling to contribute to an understanding of the spatial variation of COVID-19 vulnerability in the Greater Kampala Metropolitan Area (GKMA) using the socio-economic characteristics of the region
The outbreak of the novel Coronavirus (SARS-CoV-2) in China in December 2019 as a pathogen transmitted by the respiratory route leading to COVID-19 pandemic [1], has refocused the global attention on regional, national, and local spread suppression of a disease that is inherent to social interaction
The major components of the workflow include; 1) Definition of the vulnerability framework; 2) determining a matrix of indicators classified to the intricacies of exposure, adaptive capacity and susceptibility to COVID-19 based on the review of literature and stakeholder expert consultations; 3) Data Collection, primary data collection involved running an online survey in the region to ascertain current pre-existing medical conditions and income levels, secondary data methods involved mining historical datasets
Summary
The outbreak of the novel Coronavirus (SARS-CoV-2) in China in December 2019 as a pathogen transmitted by the respiratory route leading to COVID-19 pandemic [1], has refocused the global attention on regional, national, and local spread suppression of a disease that is inherent to social interaction. In African urban communities, many people live together in close quarters This makes social distancing, a critical prevention strategy in combating COVID-19 problematic to implement [2] to the urban vulnerable population. Uganda registered her first case of COVID-19 on 21st March 2020. The Ministry of Health (MoH) in Uganda quickly noted that this would not work in set-ups with a large number of youth (75% of the population), overcrowded urban areas, and business centres [4] This is typical of the Greater Kampala Metropolitan Area (GKMA). This paper, addresses this issue and contributes to an understanding of the spatial variation of COVID-19 Vulnerability in the GKMA using the socio-economic characteristics of the region
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