美国COVID-19流行病动态的空间预测
Çiğdem Ak1, Alex D Chitsazan1, Mehmet Gönen2
1Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, 2720 S Moody Ave, Portland, OR 97201, USA.
概括
根据地点,COVID-19的风险有所不同. 总统投票率和城市化是疾病传播和死亡率的关键预测指标,以及人口指标. 主题建模确定了具有相似特征和不同COVID-19动态的县群.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 地理空间分析是什么
背景情况:
- 美国的COVID-19影响在地理上不均,传播和死亡率存在显著差异.
- 了解特定位置的风险因素对于有效的公共卫生干预至关重要.
研究的目的:
- 测试COVID-19风险取决于位置的假设.
- 确定与COVID-19传播和死亡率相关的人口和社会经济特征.
- 为县级COVID-19结果开发一个预测模型.
主要方法:
- 利用美国各县的地理联系的社会,经济,政治和人口统计数据.
- 开发了一个使用结构化高斯过程进行预测的计算框架.
- 应用无监督的集群和主题建模来识别预测性特征组.
主要成果:
- 该模型准确地预测了县级COVID-19病例数 (皮尔森的r=0.96,R平方=0.84).
- 总统投票率和城市化是高度预测的特征,仅次于人口指标.
- 主题建模揭示了具有相似特征和多样化的COVID-19动态的不同县集群.
结论:
- 位置和相关的人口特征显著影响COVID-19风险.
- 主题建模是分组县和理解流行病学模式的宝贵工具.
- 地理空间分析和特征聚类可以增强流行病学研究和公共卫生战略.
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