实时时空追踪在封闭环境中的传染病爆发,使用基于宿主-病原体的系统
Suhas Srinivasan1,2,3, Jeffrey King4, Jacob M Collins5
1Data Science Program, Worcester Polytechnic Institute, Worcester, MA 01609.
概括
一个AI-GIS模型准确地预测了感染在狭窄空间中的传播,确定了关键因素,并为未来的疫情提出了病原体特定的制协议.
科学领域:
- 计算流行病学计算流行病学
- 传染病建模传染病建模
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 传染病在像游轮,学校和医院这样的封闭环境中爆发,由于共享空间和高人口密度,传染病迅速传播.
- 有效的缓解需要先进的计算建模来理解时空动态,并制定制策略.
研究的目的:
- 开发和评估一种基于代理物的建模方法,用于研究受限环境中的感染动态.
- 通过使用现实世界的疫情数据,确定各种病原体的有效制协议.
主要方法:
- 开发了一个AI-GIS感染动力学 (AGID) 模型,集成3D GIS,宿主行为,病原体生物学和宿主-病原体/虫相互作用.
- 将AGID模型应用于诺罗病毒,H1N1流感A和SARS-CoV-2的历史爆发数据.
主要成果:
- AGID模型准确地预测了每天的感染数量,并确定了关键干预点,例如CDC卫生协议触发因素.
- 确定了影响感染传播的关键生物参数,并提出了对病原体特定修改的制协议.
- 使用各种传染病原体和疫情情情景验证了模型的有效性.
结论:
- 人工智能-GIS方法为在封闭环境中实时感染爆发建模提供了一个强大的框架.
- 提供适用于各种环境的见解,增强对新出现的传染病和病原体的准备.
- 进一步了解游轮和类似高密度环境中的感染动态.
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