通过数据驱动的模拟来减轻全球高温相关疾病负担的最佳决策
Xin-Chen Li1,2,3, Hao-Ran Qian4, Yan-Yan Zhang1,2,3
1School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Infectious Disease Modelling
|April 22, 2024
概括
全球变暖增加了与高温相关的疾病 (HTD). 本研究使用图形神经网络 (GNN) 模型来确定最佳干预措施,以减少全球七个地区的HTD负担.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 数据科学和人工智能 数据科学和人工智能
背景情况:
- 全球变暖正在加速,高温相关疾病 (HTD) 的发生率正在增加.
- 现有的HTD管理策略需要通过以证据为基础的,具体上下文的方法来加强.
- 一个健康概念为解决人类,动物和环境健康交集的复杂健康挑战提供了一个框架.
研究的目的:
- 开发一个概念框架和数据驱动模型,以减轻HTDs的全球负担.
- 识别和评估最佳的干预策略,以减轻在不同地理区域的HTDs.
- 为决策者提供一个系统的方法,为HTD管理提供基于证据的决策.
主要方法:
- 收集了2010-2019年五种疾病类别 (心血管,传染性呼吸道,伤害,代谢,非传染性呼吸道) 的国家级数据作为中间结果.
- 开发和评估了八个预测模型,选择了图形神经网络 (GNN) 模型,因为它的性能优越.
- 利用GNN模拟来确定适合七个主要全球地区的最佳干预策略.
主要成果:
- 图形神经网络 (GNN) 模型在评估HTD负担方面表现出卓越的预测性能.
- 确定了一套最佳干预策略,包括促进低碳能源,改善牲畜料和改善医疗保健准入.
- 基础设施和社区,生态系统弹性和卫生系统能力方面的部门措施在各地区产生了重大影响.
结论:
- 开发了一种新的决策工具,以系统地解决与高温相关的疾病日益增长的挑战.
- 该研究强调了量身定制的多部门干预措施在全球减轻HTD负担方面的有效性.
- 该框架支持政策制定者制定有针对性的战略,以应对全球变暖对健康的影响.
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