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在COVID-19大流行期间的接触模式如何与大流行前的接触模式和移动趋势有关
Adrien Lajot1,2, James Wambua3, Pietro Coletti3
1Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium. adrien.lajot@sciensano.be.
BMC infectious diseases
|June 16, 2023
概括
疫情前的社交联系模式可以估计COVID-19的社交联系,但随着时间的推移,他们的关系会发生变化. 移动数据本身无法完全解释这些变化,这给实时流行病建模带来了挑战.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 公共卫生 公共卫生
背景情况:
- 在比利时实施了非药物干预措施 (NPIs),通过减少社交互动来制SARS-CoV-2的传播.
- 准确估计社会接触模式对于评估NPI在COVID-19大流行期间的影响至关重要,特别是当实时数据无法获得时.
研究的目的:
- 评估疫情前的社会接触模式和流动性数据是否可以预测COVID-19大流行期间的社会接触模式.
- 评估疫情前和疫情前社会接触模式之间的关系的时间变化性质.
主要方法:
- 采用基于模型的方法,允许时间变化的效果.
- 该研究分析了2020年11月11日至2022年7月4日之间的社交联系模式.
- 流动性是由过境站游客人数的变化所代表的.
主要成果:
- 特定位置的流行病前社会接触模式有效估计了流行病模式.
- 流行病前和流行病接触模式之间的关系表现出时间变化.
- 将移动数据 (运输站游客) 与大流行前的接触者相结合,无法充分解释观察到的时间变化关系.
结论:
- 在缺少实时流行病调查数据的情况下,疫情前社会联系模式的线性组合可能是有价值的.
- 一个关键的挑战在于准确地将NPI转换为随时间推移的适当系数.
- 将时间变化系数与汇总的移动数据联系起来被发现是评估研究期间接触者的不可接受假设.
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