在COVID-19大流行期间应用的空间流行病学建模方法的系统审查
Kayode Oshinubi1, Ye Chen2, Eck Doerry1
1School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States.
medRxiv : the preprint server for health sciences
|October 3, 2025
概括
这次对SARS-CoV-2空间流行病学模型的系统审查发现基于代理的和隔间的方法是最常见的. 未来的研究应该评估模型的适当性,并改进网络基础设施的可重复性.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- SARS-CoV-2 流行病刺激了各种流行病学建模工作.
- 空间模型对于了解受人口异质性和移动影响的疾病传播至关重要.
研究的目的:
- 系统地审查SARS-CoV-2的空间流行病学建模方法.
- 为了分类研究问题,参数估计技术和这些模型中使用的网络基础设施.
- 为未来的流行病建模提供基础.
主要方法:
- 使用Web of Science和定义的关键词进行系统的文献审查.
- 开发一个地理,人口,运动 (GPM) 模型分析框架.
- 基于纳入标准,分析了109项选定的研究.
主要成果:
- 基于代理的 (47.7%) 和区间的 (29.4%) 模型是最常见的.
- 地理模型从微观世界到跨国规模各不相同.
- 马特拉布和Python是常见的编程语言,但只有30.6%的研究提供了开放访问代码.
结论:
- 多种空间建模策略和软件有效地解决了COVID-19的流行病学问题.
- 需要进行进一步的研究,以确定针对特定情景和病原体的最佳模型方法.
- 增强的网络基础设施可以提高透明度,可重复性和模型性能评估.
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