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通过传染病预测满足全球卫生需求:开发可靠的数据驱动框架
Ravikiran Keshavamurthy1,2, Karl T Pazdernik1,3, Colby Ham1
1Artificial Intelligence and Data Analytics Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, 99354, United States.
JMIR public health and surveillance
|March 21, 2025
概括
准确的传染病预测对公共卫生至关重要. 这项研究开发了一个标准化的管道和仪表板,用于可靠的ID预测,改善全球的疫情预防和缓解策略.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 传染病 (IDs) 构成了严重的全球健康威胁.
- 准确及时预测IDs有助于实施有效的控制措施和预防政策.
研究的目的:
- 开发一个标准化,可靠和值得信赖的传染病 (ID) 预测管道和可视化仪表板.
- 确保在不同的建模技术,疾病和全球位置上实现对操作决策的概括性.
主要方法:
- 在4个大陆和8个国家预测了6种动物传染病.
- 利用 9 种不同的模型 (统计,机器学习,深度学习) 训练了 One Health 景观中的 ~ 2326 个特征.
- 专注于操作指标:预测准确性,计算效率,时空通用性,不确定性量化和可解释性.
主要成果:
- 采用集体技术,选择每个场景中最好的模型进行最佳预测.
- 该系统可以使用来自类似疾病流行地区的模型预测新区域的疾病行为.
- 仪表板可视化时间模式,预测,不确定性和特征的重要性.
结论:
- 标准化,自动化的平台推进了实时ID预测能力.
- 该系统支持基于证据的公共卫生决策,以预防和减轻未来的ID爆发.
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