使用机器学习预测监狱中的COVID-19爆发情况
Giovanni S P Malloy1, Lisa B Puglisi2, Kristofer B Bucklen3
1RAND Corporation, Santa Monica, CA, USA.
MDM policy & practice
|January 31, 2024
概括
预测监狱中的传染病爆发至关重要. 县级的COVID-19数据,设施人口和测试阳性率最好预测疫情,而不是疫苗接种或人口统计等内部因素.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 传染病建模 传染病建模
背景情况:
- 惩戒设施面临着高的传染病传播风险,由于密切的隔离和有限的医疗保健服务.
- 现有的有关监狱传染病爆发的研究需要确定最佳的预测数据来源.
研究的目的:
- 为了确定哪些数据源最有效地预测监狱中的COVID-19疫情.
- 为了比较疫苗可用性之前和之后的预测模型.
主要方法:
- 利用了来自宾夕法尼亚州24个惩戒机构的设施,人口和健康数据 (2020年3月至2021年5月).
- 使用机器学习根据特征和后勤回归对监狱进行分类,以预测爆发事件 (没有病例,爆发,大爆发).
主要成果:
- 确定了8个设施集群;后勤回归预测了>55%的准确性爆发.
- 关键预测因素包括先前被监禁的人口病例 (2-32天前),进行的测试,设施人口,测试阳性率和县级COVID-19数据.
- 设施特定的累积病例,疫苗接种率和人口统计数据并不是显著的预测因素.
结论:
- 县级的COVID-19指标,设施人口和测试阳性是监狱爆发的有希望的预测指标.
- 惩戒设施应监测社区传播,并与内部数据一起进行有效的疫情应对.
- 这些预测策略适用于各种具有潜在社区传播的大型传染病.
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