一种预测工具,用于识别肠道疾病爆发的致病因子,使用爆发监测数据
Hannah Kisselburgh1, Alice White1, Beau B Bruce2
1University of Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado, USA.
Foodborne pathogens and disease
|November 9, 2023
概括
预测肠道疾病爆发的原因对于控制至关重要. 这项研究开发了使用临床和人口统计数据的模型,以准确识别疫情病因,即使没有实验室确认,并为调查人员创建了一个在线工具.
科学领域:
- 流行病学 流行病学
- 传染病控制和控制传染病
- 公共卫生信息学 公共卫生信息学
背景情况:
- 鉴定肠道疾病爆发的致病原体对于有效的控制策略至关重要,但报告的爆发中往往缺乏确定的病因.
- 目前的疫情调查方法在很大程度上依赖于实验室确认,这种确认并不总是可用或及时的.
研究的目的:
- 确定肠道疾病爆发病因的临床和人口预测因素.
- 开发一种预测工具,以帮助调查人员在没有实验室确认的情况下在疫情期间假设病原体.
主要方法:
- 利用了向美国疾病控制和预防中心 (CDC) 报告的肠道疾病爆发数据集.
- 开发了随机森林模型,以预测疫情的病因,无论是在类别 (细菌,寄生虫,毒素,病毒) 和特定水平 (例如,诺病毒,沙门氏菌).
- 模型是根据疫情病例的综合临床和人口统计特征进行训练的.
主要成果:
- 病因学类型模型实现了0.85的kappa和0.92.9的准确性.
- 病因特异型模型显示卡帕值为0.75,准确度为0.86.
- 在类型模型中观察到对细菌和病毒的高度敏感性,在特定模型中观察到对诺罗病毒和沙门氏菌的高度敏感性,所有病因都显示出高特异性.
结论:
- 临床和人口统计数据,包括症状和疾病严重程度,可以有效预测肠道疾病爆发的病因学或病因学类别.
- 一个在线,公开可访问的工具已经开发出来,以帮助公共卫生调查人员在疫情期间预测病原体.
- 这种工具增强了疫情调查能力,特别是当实验室确认正在等待或无法获得时.
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