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Jack C, Parker C, Kouakou YE, Joubert B, McAllister KA, Ilias M, Maimela G, Chersich M, Makhanya S, Luchters S, Makanga PT, Vos E, Ebi KL, Koné B, Waljee AK, Cissé G. Leveraging data science and machine learning for urban climate adaptation in two major African cities: a HE 2AT Center study protocol. BMJ Open 2024; 14:e077529. [PMID: 38890141 PMCID: PMC11191804 DOI: 10.1136/bmjopen-2023-077529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 05/03/2024] [Indexed: 06/20/2024] Open
Abstract
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.
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Affiliation(s)
- Christopher Jack
- Climate System Analysis Group, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Craig Parker
- Wits Planetary Health Research, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Yao Etienne Kouakou
- University Peleforo Gon Coulibaly, Korhogo, Côte d'Ivoire
- Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
| | - Bonnie Joubert
- National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | | | - Maliha Ilias
- National Heart Lung and Blood Institute, Bethesda, Maryland, USA
| | - Gloria Maimela
- Climate and Health Directorate, Wits Reproductive Health and HIV Institute, Hillbrow, Gauteng, South Africa
| | - Matthew Chersich
- Wits Planetary Health Research, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Public Health and Primary Care, School of Medicine, Trinity College Dublin, Dublin, UK
| | | | - Stanley Luchters
- Centre for Sexual Health and HIV & AIDS Research (CeSHHAR), Harare, Zimbabwe
- Liverpool School of Tropical Medicine, Liverpool, UK
| | - Prestige Tatenda Makanga
- Centre for Sexual Health and HIV & AIDS Research (CeSHHAR), Harare, Zimbabwe
- Surveying and Geomatics Department, Midlands State University, Gweru, Zimbabwe
| | - Etienne Vos
- IBM Research-Africa, Johannesburg, South Africa
| | | | - Brama Koné
- University Peleforo Gon Coulibaly, Korhogo, Côte d'Ivoire
- Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
| | - Akbar K Waljee
- Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA
- Ann Arbor VA Medical Center, VA Center for Clinical Management Research, Ann Arbor, Michigan, USA
| | - Guéladio Cissé
- University Peleforo Gon Coulibaly, Korhogo, Côte d'Ivoire
- Centre Suisse de Recherches Scientifiques, Abidjan, Côte d'Ivoire
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Boudreault J, Campagna C, Chebana F. Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:14059-14070. [PMID: 38270762 DOI: 10.1007/s11356-024-31969-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/07/2024] [Indexed: 01/26/2024]
Abstract
Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.
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Affiliation(s)
- Jérémie Boudreault
- Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de La Couronne, Quebec, QC, G1K 9A9, Canada.
- Direction de la santé environnementale, au travail et de la toxicologie, Institut national de santé publique du Québec (INSPQ), 945 Avenue Wolfe, Quebec, QC, G1V 5B3, Canada.
| | - Céline Campagna
- Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de La Couronne, Quebec, QC, G1K 9A9, Canada
- Direction de la santé environnementale, au travail et de la toxicologie, Institut national de santé publique du Québec (INSPQ), 945 Avenue Wolfe, Quebec, QC, G1V 5B3, Canada
| | - Fateh Chebana
- Centre Eau Terre Environnement, Institut national de la recherche scientifique (INRS), 490 de La Couronne, Quebec, QC, G1K 9A9, Canada
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Kondraganti A, Narayanamurthy G, Sharifi H. A systematic literature review on the use of big data analytics in humanitarian and disaster operations. ANNALS OF OPERATIONS RESEARCH 2022:1-38. [PMID: 36846245 PMCID: PMC9936938 DOI: 10.1007/s10479-022-04904-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 06/18/2023]
Abstract
At the start of this review, 168 million individuals required humanitarian assistance, at the conclusion of the research, the number had risen to 235 million. Humanitarian aid is critical not just for dealing with a pandemic that occurs once every century, but more for assisting amid civil conflicts, surging natural disasters, as well as other kinds of emergencies. Technology's dependability to support humanitarian and disaster operations has never been more pertinent and significant than it is right now. The ever-increasing volume of data, as well as innovations in the field of data analytics, present an incentive for the humanitarian sector. Given that the interaction between big data and humanitarian and disaster operations is crucial in the coming days, this systematic literature review offers a comprehensive overview of big data analytics in a humanitarian and disaster setting. In addition to presenting the descriptive aspects of the literature reviewed, the results explain review of existent reviews, the current state of research by disaster categories, disaster phases, disaster locations, and the big data sources used. A framework is also created to understand why researchers employ various big data sources in different crisis situations. The study, in particular, uncovered a considerable research disparity in the disaster group, disaster phase, and disaster regions, emphasising how the focus is on reactionary interventions rather than preventative approaches. These measures will merely compound the crisis, and so is the reality in many COVID-19-affected countries. Implications for practice and policy-making are also discussed.
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Affiliation(s)
- Abhilash Kondraganti
- University of Liverpool Management School, Chatham Street, Liverpool, L69 7ZH UK
| | | | - Hossein Sharifi
- University of Liverpool Management School, Chatham Street, Liverpool, L69 7ZH UK
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Special Issue on Applied Machine Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12042039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Machine learning (ML) is one of the most exciting fields of computing today [...]
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Cho Y, Jung YM, Park YT. Synthesis of 1,
1‐Diisopropyl
‐ or ‐Diphenyl ‐2,5‐dibromo‐ or ‐bis(trimethylsilyl)‐3,4‐diphenyl‐siloles and the Electrochemical Properties as Anode Materials for
Lithium‐Ion
Battery. B KOREAN CHEM SOC 2020. [DOI: 10.1002/bkcs.12193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Yoon‐ho Cho
- Department of Chemistry Keimyung University Daegu 42601 Korea
| | - Young Min Jung
- Division of Green Energy Research Daegu Gyeongbuk Institute of Science & Technology Daegu 42988 Korea
| | - Young Tae Park
- Department of Chemistry Keimyung University Daegu 42601 Korea
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