Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE 2 AT Center study protocol

African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The obje...

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Bibliographic Details
Published in:BMJ open 2024-06, Vol.14 (6), p.e077529
Main Authors: Jack, Christopher, Parker, Craig, Kouakou, Yao Etienne, Joubert, Bonnie, McAllister, Kimberly A, Ilias, Maliha, Maimela, Gloria, Chersich, Matthew, Makhanya, Sibusisiwe, Luchters, Stanley, Makanga, Prestige Tatenda, Vos, Etienne, Ebi, Kristie L, Koné, Brama, Waljee, Akbar K, Cissé, Guéladio
Format: Article
Language:English
Subjects:
Cities
Climate Change
Heat Stress Disorders - epidemiology
Hot Temperature - adverse effects
Humans
Machine Learning
Research Design
Satellite Imagery
South Africa
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Summary:African cities, particularly Abidjan and Johannesburg, face challenges of rapid urban growth, informality and strained health services, compounded by increasing temperatures due to climate change. This study aims to understand the complexities of heat-related health impacts in these cities. The objectives are: (1) mapping intraurban heat risk and exposure using health, socioeconomic, climate and satellite imagery data; (2) creating a stratified heat-health forecast model to predict adverse health outcomes; and (3) establishing an early warning system for timely heatwave alerts. The ultimate goal is to foster climate-resilient African cities, protecting disproportionately affected populations from heat hazards. The research will acquire health-related datasets from eligible adult clinical trials or cohort studies conducted in Johannesburg and Abidjan between 2000 and 2022. Additional data will be collected, including socioeconomic, climate datasets and satellite imagery. These resources will aid in mapping heat hazards and quantifying heat-health exposure, the extent of elevated risk and morbidity. Outcomes will be determined using advanced data analysis methods, including statistical evaluation, machine learning and deep learning techniques. The study has been approved by the Wits Human Research Ethics Committee (reference no: 220606). Data management will follow approved procedures. The results will be disseminated through workshops, community forums, conferences and publications. Data deposition and curation plans will be established in line with ethical and safety considerations.
ISSN:2044-6055