一种社会网络分析模型方法,以了解马达加斯加偏远农村的结核病传播
Christine Pando1, Ashley Hazel2, Lai Yu Tsang1
1Stony Brook University, 101 Nicolls Road, Stony Brook, NY, 11794-8343, USA.
BMC public health
|August 9, 2023
概括
社交网络分析 (SNA) 模型显示,虽然干预措施减少了结核病 (TB) 疾病,但社区互联性影响了结核病重新引入的性. SNA为针对性结核病传播建模提供了至关重要的超局部洞察力.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 公共卫生 公共卫生
背景情况:
- 由于无法获得监测数据,传统的结核病 (TB) 传播模式往往缺乏社区动态.
- 社会网络分析 (SNA) 提供了一种捕捉影响疾病传播的超局部社会结构的方法.
研究的目的:
- 在偏远社区开发和应用基于SNA的结核病传播模型.
- 评估干预措施的有效性和社区对结核病再引入的抵抗力.
主要方法:
- 采集了马达加斯加五个村庄的社交联系数据,使用名字生成器方法和雪球采样.
- 建立了特定村庄的随机结核病传输模型,其中包含来自SNA的网络结构.
- 利用指数随机图模型 (ERGM) 来定义联系形成和网络动态.
主要成果:
- 积极病例的发现和治疗等干预措施使结核病的患病率降低了67%-79%,死亡率降低了75%.
- 随着网络互连性 (过渡性) 的提高,社区对结核病再引入的抵抗力下降.
- 高度相互连接的网络显示出对重新引入的脆弱性,与连接较少的网络不同.
结论:
- 基于SNA的模型强调,世卫组织推的结核病干预措施可以减少活跃的疾病,但不能减少潜在的感染负担.
- 社区互联互通是结核病再引入性的关键因素,这是大规模模型错过的动态.
- SNA将人口层面和社区层面的结核病建模结合起来,提供重要的超局部洞察力.
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