دورية أكاديمية

Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation

التفاصيل البيبلوغرافية
العنوان: Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation
المؤلفون: Xin-Chen Li, Hao-Ran Qian, Yan-Yan Zhang, Qi-Yu Zhang, Jing-Shu Liu, Hong-Yu Lai, Wei-Guo Zheng, Jian Sun, Bo Fu, Xiao-Nong Zhou, Xiao-Xi Zhang
المصدر: Infectious Disease Modelling, Vol 9, Iss 2, Pp 618-633 (2024)
بيانات النشر: KeAi Communications Co., Ltd., 2024.
سنة النشر: 2024
المجموعة: LCC:Infectious and parasitic diseases
مصطلحات موضوعية: High temperature-related diseases, Data-driven simulation, Optimal intervention, Disease burden, Graph neural network, Global warming, Infectious and parasitic diseases, RC109-216
الوصف: The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010–2019. The burdens of five categories of disease causes – cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases – were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2468-0427
Relation: http://www.sciencedirect.com/science/article/pii/S2468042724000344; https://doaj.org/toc/2468-0427
DOI: 10.1016/j.idm.2024.03.001
URL الوصول: https://doaj.org/article/1253a87ac16a4039996f952480d6ed2c
رقم الأكسشن: edsdoj.1253a87ac16a4039996f952480d6ed2c
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:24680427
DOI:10.1016/j.idm.2024.03.001