使用基于代理物的建模方法研究美国州监狱中的COVID-19传播:一个模拟研究研究
Allison Lydia Owens1, Mike Fliss2, Lauren Brinkley-Rubinstein3
1Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA owens23a@unc.edu.
BMJ open
|December 12, 2025
概括
一个基于代理的模型合理地预测了COVID-19在监狱中的传播. 调查结果显示,疫苗接种和减产能力是制这些高风险环境中疫情爆发的关键.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 计算机建模 计算建模
背景情况:
- COVID-19对被监禁人口产生了重大影响,需要更好的预测模型.
- 现有的模型缺乏监狱特定因素,如容量和疫苗接种率,限制了通用性.
- 这项研究解决了针对惩戒设施中独特的系统因素而定制的模型的需求.
研究的目的:
- 开发和验证一种基于代理的模型,用于准确地预测美国州监狱中的COVID-19传播情况.
- 将监狱的特定特征,包括疫苗接种率和过度拥挤,纳入疾病传播模型.
- 提供可操作的见解,以减轻在惩戒机构的传染病爆发.
主要方法:
- 开发了一种基于半静态代理的模型,结合了地理空间接触网络和隔间动态.
- 模拟了北卡罗来纳州五所监狱 (2020年7月至2021年6月) 的COVID-19疫情,使用设施容量和疫苗接种率.
- 用近似贝叶斯计算来估计参数和模型与现实数据相匹配.
主要成果:
- 该模型的平均绝对百分比误差 (MAPE) 为23.0,表明感染和康复率的预测准确度合理.
- 估计平均疫苗接种率为54%,设施占用率为90%,表明疫苗接种不足和过度拥挤.
- 确定数据缺口是准确预测疫情的重要障碍,强调需要一致的数据报告.
结论:
- 空间接触网络和设施特征对于预测集体环境中传染病传播至关重要.
- 建议增加疫苗接种力度和减少潜在的容量,以减轻监狱中的COVID-19传播.
- 该研究强调了强有力的数据收集对于在惩戒设施有效的公共卫生干预的重要性.
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