Evaluating water-related health risks in East and Central Asian Islamic Nations using predictive models (2020–2030)

This paper presents a thorough evaluation of health outcomes linked to water-related challenges in Islamic nations across East Asia and Central Asia from 2020 to 2030. It has been examined carefully that the trajectory of deaths and disability-adjusted life years associated with unsafe water sources...

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Bibliographic Details
Published in:	Scientific reports Vol. 14; no. 1; pp. 16837 - 14
Main Authors:	Cheema, Mahwish Anwer, Hanif, Muhammad, Albalawi, Olayan, Mahmoud, Emad E., Nabi, Muhammad
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 22-07-2024 Nature Publishing Group Nature Portfolio
Subjects:	639/705 692/700 AHWF Asia - epidemiology DALYs Death Diarrhea Dysentery Gastrointestinal diseases Health promotion Health risks Humanities and Social Sciences Humans Islam LOS Malnutrition multidisciplinary Neural networks Prediction models Public health Quality-Adjusted Life Years Resource management Sanitation Science Science (multidisciplinary) Support Vector Machine Support vector machines USWS Water Pollution Water Quality Water resources management Water Supply Waterborne diseases Waterborne Diseases - epidemiology Asia AHWF LOS Death DALYs USWS
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Summary:	This paper presents a thorough evaluation of health outcomes linked to water-related challenges in Islamic nations across East Asia and Central Asia from 2020 to 2030. It has been examined carefully that the trajectory of deaths and disability-adjusted life years associated with unsafe water sources, lack of sanitation, and absence of handwashing facilities is showing a potential rise in negative health impacts due to water pollution. The direct health influences of water-related problems are thoughtful. The increase in deaths and DALYs due to poor water quality and sanitation leads to a higher occurrence of waterborne diseases such as cholera, diarrhea, and dysentery. These conditions not only cause instant health disasters but also subsidize to long-term health issues which include chronic gastrointestinal disorders and malnutrition that is particularly among susceptible populations like children and the elderly. Employing various predictive models including autoregressive integrated moving average, exponential smoothing, support vector machines, and neural networks. The study evaluates their predictive capabilities by using mean absolute percentage error. Support vector machines is found to be the most accurate in forecasting deaths and disability-adjusted life years which is outperforming autoregressive integrated moving average, exponential smoothing, and neural networks. This research aims to inform stakeholders by providing insights into effective strategies for improving water resource management and public health interventions in the targeted regions.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-67775-3