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

Introduction: 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. Methods and analysis: 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. Ethics and dissemination: 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

Protocol of an individual participant data meta-analysis to quantify the impact of high ambient temperatures on maternal and child health in Africa (HE 2 AT IPD)

Introduction: Globally, recognition is growing of the harmful impacts of high ambient temperatures (heat) on health in pregnant women and children. There remain, however, major evidence gaps on the extent to which heat increases the risks for adverse health outcomes, and how this varies between settings. Evidence gaps are especially large in Africa. We will conduct an individual participant data (IPD) meta-analysis to quantify the impacts of heat on maternal and child health in sub-Saharan Africa. A detailed understanding and quantification of linkages between heat, and maternal and child health is essential for developing solutions to this critical research and policy area. Methods and analysis: We will use IPD from existing, large, longitudinal trial and cohort studies, on pregnant women and children from sub-Saharan Africa. We will systematically identify eligible studies through a mapping review, searching data repositories, and suggestions from experts. IPD will be acquired from data repositories, or through collaboration with data providers. Existing satellite imagery, climate reanalysis data, and station-based weather observations will be used to quantify weather and environmental exposures. IPD will be recoded and harmonised before being linked with climate, environmental, and socioeconomic data by location and time. Adopting a one-stage and two-stage meta-analysis method, analytical models such as time-to-event analysis, generalised additive models, and machine learning approaches will be employed to quantify associations between exposure to heat and adverse maternal and child health outcomes. Ethics and dissemination: The study has been approved by ethics committees. There is minimal risk to study participants. Participant privacy is protected through the anonymisation of data for analysis, secure data transfer and restricted access. Findings will be disseminated through conferences, journal publications, related policy and research fora, and data may be shared in accordance with data sharing policies of the National Institutes of Health. PROSPERO registration number: CRD42022346068

Climate change projections from a multi-model ensemble of CORDEX and CMIPs over Angola

Angola has been characterized as one of the most vulnerable regions to climate change. Climate change compounded by existing poverty, a legacy of conflict and other risk factors, currently impede development and are expected to become worse as climate change impacts increase. In this study we analyze the signal of climate change on temperature and rainfall variables for two time periods, 2020–2040 and 2040–2060. The analysis is based on multi-model ensemble of the Coupled Model Intercomparison Projects (CMIP5 and CMIP6) and the Coordinated Regional Downscaling Experiments (CORDEX). Our findings from the observed dataset indicate that the mean annual temperature over Angola has risen by an average of 1.4 °C since 1951, with a warming rate of approximately 0.2 [0.14–0.25] °C per decade. However, the rainfall pattern appears to be primarily influenced by natural variability. Projections of extreme temperature show an increase with the coldest nights projected to become warmer and the hottest days hotter. Rainfall projections suggest a change in the nature of the rainy season with increases in heavy precipitation events in the future. We investigated how droughts might change in all river basins of Angola, and we found an increased uncertainty about drought in the future. The changes in climate and increased variability demonstrate the need for adaptation measures that focuses on reducing risks in key sectors with a particular focus on adaptation of cities in Angola given a potential increase in mobility towards urban areas