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评估观察到的传染病在未来的时空系列中传播的空间变异性
Chih-Chieh Wu1,2, Chien-Hsiun Chen3, Shann-Rong Wang4
1Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 701, Taiwan. cc_wu@mail.ncku.edu.tw.
International journal of health geographics
|October 4, 2025
概括
这项研究引入了一个超几何概率模型,用于实时分析传染病传播异常. 它有助于在疫情爆发期间检测特定地区的快速发病率上升或下降,改善疾病监测.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 传统的疾病监测通常侧重于早期发现疫情,但并不总是考虑不统一的空间和时间传播.
- 在疫情爆发期间识别和评估疾病发病率的异常 (过量或下降) 对于有效的公共卫生反应至关重要.
研究的目的:
- 提出并制定一种新的超几何概率模型,用于调查传染病发病率传播中的实时异常.
- 评估在持续爆发期间,特定地理区域的疾病发病率是否比其他地区增长或下降得更快.
主要方法:
- 开发了一种超几何学概率模型,用于对地理上分散的群体进行日常监测.
- 整合了一个时间变化的基线风险模型,使用定期更新的疾病发病率数据.
- 对预期频率的评估偏差,考虑到采样波动和不同地区人口规模的不平等.
主要成果:
- 该模型量化了观察到的疾病发病率偏差是由于随机机会造成的概率.
- 使用台湾的登革热和COVID-19的时空监测数据演示了该模型的应用.
- 有效的R包可用于对大型数据集实施该模型的公式.
结论:
- 提出的超几何学概率模型推进了对传染病发病率传播异常的调查.
- 该方法为实时评估疫情期间疾病动态提供了强大的工具.
- 该模型适用于各种传染病和地理范围,增强公共卫生监测能力.
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